New upstream version 1.0.2+dfsg
Andreas Tille
1 year, 4 months ago
| 0 | 0 | Type: Package |
| 1 | 1 | Package: broom |
| 2 | 2 | Title: Convert Statistical Objects into Tidy Tibbles |
| 3 | Version: 1.0.1 | |
| 3 | Version: 1.0.2 | |
| 4 | 4 | Authors@R: |
| 5 | 5 | c(person(given = "David", |
| 6 | 6 | family = "Robinson", |
| 337 | 337 | family = "Hester", |
| 338 | 338 | role = "ctb", |
| 339 | 339 | email = "jim.hester@rstudio.com"), |
| 340 | person(given = "Cory", | |
| 341 | family = "Brunson", | |
| 342 | role = "ctb", | |
| 343 | email = "cornelioid@gmail.com"), | |
| 344 | 340 | person(given = "Ben", |
| 345 | 341 | family = "Schneider", |
| 346 | 342 | role = "ctb", |
| 544 | 540 | Depends: R (>= 3.1) |
| 545 | 541 | Imports: backports, dplyr (>= 1.0.0), ellipsis, generics (>= 0.0.2), |
| 546 | 542 | glue, purrr, rlang, stringr, tibble (>= 3.0.0), tidyr (>= |
| 547 | 1.0.0), ggplot2 | |
| 543 | 1.0.0) | |
| 548 | 544 | Suggests: AER, AUC, bbmle, betareg, biglm, binGroup, boot, btergm (>= |
| 549 | 545 | 1.10.6), car, carData, caret, cluster, cmprsk, coda, covr, drc, |
| 550 | 546 | e1071, emmeans, epiR, ergm (>= 3.10.4), fixest (>= 0.9.0), gam |
| 551 | (>= 1.15), gee, geepack, glmnet, glmnetUtils, gmm, Hmisc, | |
| 552 | irlba, interp, joineRML, Kendall, knitr, ks, Lahman, lavaan, | |
| 553 | leaps, lfe, lm.beta, lme4, lmodel2, lmtest (>= 0.9.38), | |
| 547 | (>= 1.15), gee, geepack, ggplot2, glmnet, glmnetUtils, gmm, | |
| 548 | Hmisc, irlba, interp, joineRML, Kendall, knitr, ks, Lahman, | |
| 549 | lavaan, leaps, lfe, lm.beta, lme4, lmodel2, lmtest (>= 0.9.38), | |
| 554 | 550 | lsmeans, maps, maptools, margins, MASS, mclust, mediation, |
| 555 | 551 | metafor, mfx, mgcv, mlogit, modeldata, modeltests, muhaz, |
| 556 | 552 | multcomp, network, nnet, orcutt (>= 2.2), ordinal, plm, poLCA, |
| 607 | 603 | 'systemfit-tidiers.R' 'tseries-tidiers.R' 'utilities.R' |
| 608 | 604 | 'vars-tidiers.R' 'zoo-tidiers.R' 'zzz.R' |
| 609 | 605 | NeedsCompilation: no |
| 610 | Packaged: 2022-08-29 19:41:53 UTC; simoncouch | |
| 606 | Packaged: 2022-12-14 15:57:36 UTC; simoncouch | |
| 611 | 607 | Author: David Robinson [aut], |
| 612 | 608 | Alex Hayes [aut] (<https://orcid.org/0000-0002-4985-5160>), |
| 613 | 609 | Simon Couch [aut, cre] (<https://orcid.org/0000-0001-5676-5107>), |
| 690 | 686 | Sergio Oller [ctb], |
| 691 | 687 | Luke Sonnet [ctb], |
| 692 | 688 | Jim Hester [ctb], |
| 693 | Cory Brunson [ctb], | |
| 694 | 689 | Ben Schneider [ctb], |
| 695 | 690 | Bernie Gray [ctb] (<https://orcid.org/0000-0001-9190-6032>), |
| 696 | 691 | Mara Averick [ctb], |
| 739 | 734 | Alex Reinhart [ctb] (<https://orcid.org/0000-0002-6658-514X>) |
| 740 | 735 | Maintainer: Simon Couch <simonpatrickcouch@gmail.com> |
| 741 | 736 | Repository: CRAN |
| 742 | Date/Publication: 2022-08-29 21:00:08 UTC | |
| 737 | Date/Publication: 2022-12-15 13:10:20 UTC | |
| 0 | 9190afa3ae7efa6fc12a98afb28dd301 *DESCRIPTION | |
| 0 | 0237b8802fae5c64386d2019e804599e *DESCRIPTION | |
| 1 | 1 | 10390b90ea27c186e1fe37138085bd25 *LICENSE |
| 2 | 2 | cf43a69ce1557ab94bce3afdf18a4738 *NAMESPACE |
| 3 | 27d7c89b6e661378805bdbdb6a6a7b14 *NEWS.md | |
| 3 | e22780bd4d34d39a733b29e084226076 *NEWS.md | |
| 4 | 4 | 303a2e66bc5febc2a128d8600a94ad75 *R/aaa-documentation-helper.R |
| 5 | 5 | fb52af46a21b958f646559b3f6831219 *R/aer-tidiers.R |
| 6 | 830a3da844ebb64683b3b3c6627b7dc8 *R/auc-tidiers.R | |
| 6 | 9cb2e33b3554fed23bff78b9ebec8f6e *R/auc-tidiers.R | |
| 7 | 7 | f64bdbd4790ac88f9580d655629b97e2 *R/base-tidiers.R |
| 8 | 8 | 54137e8597cac1d77d394a66dfe8921c *R/bbmle-tidiers.R |
| 9 | 9 | db5094671c331548e3ff4591ea50c730 *R/betareg-tidiers.R |
| 10 | 10 | 667a019087e982a1f111f7b82e0dba4f *R/biglm-tidiers.R |
| 11 | c516e7a790bb15f010d42756a6dcd7f5 *R/bingroup-tidiers.R | |
| 11 | 11f8090ba3e09293147d24b67c0c0c55 *R/bingroup-tidiers.R | |
| 12 | 12 | 43366f850d226756e0c2463c1002f38c *R/boot-tidiers.R |
| 13 | 32de1e01fc126b3911b1430aa507ce10 *R/broom-package.R | |
| 13 | 50aada59e0d2193beee7080ec88c95bd *R/broom-package.R | |
| 14 | 14 | 2fd0e39201b54a9a5feb244209a1ba68 *R/broom.R |
| 15 | 15 | bd2d2f35f9c5d0ae7051c4aeb1ccbf1c *R/btergm-tidiers.R |
| 16 | 16 | 3db3452c708326b444785cfbbbb00363 *R/car-tidiers.R |
| 17 | 17 | 910ef33ff34552050efe859c49f35088 *R/caret-tidiers.R |
| 18 | 54594ed3d087aab7651ed48143496ae0 *R/cluster-tidiers.R | |
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| 22 | 22 | 705d2ced3341675bdc6f6c5fe39c9e65 *R/drc-tidiers.R |
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| 25 | 25 | 1573224ecbb4471b3097a6aebf1dff00 *R/ergm-tidiers.R |
| 26 | 7cf6c4aa9b063f0817fa935300de3486 *R/fixest-tidiers.R | |
| 26 | 471000837e4cd0604114d4abf33d5042 *R/fixest-tidiers.R | |
| 27 | 27 | f3814a0bce84e5e3624d973fcd6b0381 *R/gam-tidiers.R |
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| 29 | f848d5c3c6b202f7ebb41eae4322c331 *R/glmnet-cv-glmnet-tidiers.R | |
| 30 | 3d652ad1a1aa60af091707ea277bb6bc *R/glmnet-glmnet-tidiers.R | |
| 31 | f6c8ba6a2c7f0762f2e195fc96f7a325 *R/gmm-tidiers.R | |
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| 35 | 1d7723329dedd2f0ed80b2d1160ed864 *R/ks-tidiers.R | |
| 35 | f44c97f20f55c502851cc933d75ca615 *R/ks-tidiers.R | |
| 36 | 36 | ad7d2eb41b3cfcdd30e504cab620329c *R/lavaan-tidiers.R |
| 37 | 37 | c477b7ed6d9cff0864481e0533858333 *R/leaps-tidiers.R |
| 38 | 38 | 0d15d4f1ab3f1e64f189df34251f39ac *R/lfe-tidiers.R |
| 39 | 39 | 7cf58ac01016d4ead87b6e8e69929101 *R/list-irlba.R |
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| 41 | dca4c38dfb227ddad678ffffba2345ce *R/list-svd-tidiers.R | |
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| 44 | 44 | 4d7667b9525337c6b55cf67216e5ef3f *R/lm-beta-tidiers.R |
| 45 | 9ecad7423b12fcb0af05b3128d100e8c *R/lmodel2-tidiers.R | |
| 45 | fed78dc462419f83b27c4232e25389ab *R/lmodel2-tidiers.R | |
| 46 | 46 | 8772b5b2252b10be07ee35afb725b9bb *R/lmtest-tidiers.R |
| 47 | 91669ad5121496878640995d6cce06d8 *R/maps-tidiers.R | |
| 48 | 26ee81f61b69bcbbed6c617d1a49726c *R/margins-tidiers.R | |
| 47 | e6b5c4777cdc58adce44813139dad2a7 *R/maps-tidiers.R | |
| 48 | 91988e49eea1de0df83a58aa2ba8f545 *R/margins-tidiers.R | |
| 49 | 49 | a2a64a3c5ab335663c15cf9af9977082 *R/mass-fitdistr-tidiers.R |
| 50 | 50 | 78ee805f323377c55d86ddfca3bb0546 *R/mass-negbin-tidiers.R |
| 51 | 51 | 56bb672eee6db52b7e880d839df39f3a *R/mass-polr-tidiers.R |
| 52 | d0beba4085b3c668edbfb69351ce58a9 *R/mass-ridgelm-tidiers.R | |
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| 53 | 53 | 1d39c424bc5705bce1bb8ab144713951 *R/mass-rlm-tidiers.R |
| 54 | 54 | 02ed2982c81646362ff2eddc2ea02365 *R/mclust-tidiers.R |
| 55 | 55 | b63d90b6589dfd6d94efa492da89e30b *R/mediation-tidiers.R |
| 58 | 58 | 477741ae94592f3be7d2948b72c21c11 *R/mgcv-tidiers.R |
| 59 | 59 | 69f69e163acc58506bc60c6e7b697204 *R/mlogit-tidiers.R |
| 60 | 60 | c09adb63eabdcd356f07c8f8d1b1b068 *R/muhaz-tidiers.R |
| 61 | 968668988a0d3fcb887e2bae00d50f31 *R/multcomp-tidiers.R | |
| 61 | a67198372c2fb969bade5bd2aab12c1c *R/multcomp-tidiers.R | |
| 62 | 62 | 6444f84ecfa5db755946726adbdd5edf *R/nnet-tidiers.R |
| 63 | 63 | 35fab3dea6564c591c12aa5e186dd325 *R/nobs.R |
| 64 | 64 | f2c28c80dcd7de65ff45d7088b10d6f2 *R/null-and-default-tidiers.R |
| 66 | 66 | c883cfcc49c026c6b10a7c6bcae38af1 *R/ordinal-clm-tidiers.R |
| 67 | 67 | 88674857b3858a357e71656777b39bd2 *R/ordinal-clmm-tidiers.R |
| 68 | 68 | 3cdd3c056e8d2bdd2bf1c8f4439091d8 *R/plm-tidiers.R |
| 69 | 16f9d5297942af46124c5a9038f5905c *R/polca-tidiers.R | |
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| 71 | 71 | b845055da0fa580ec49897b5988e486b *R/quantreg-nlrq-tidiers.R |
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| 82 | 60bb082a0d1039cefc6545922f84781e *R/stats-anova-tidiers.R | |
| 82 | b0124fe7d56de3376ff96ca0584534aa *R/stats-anova-tidiers.R | |
| 83 | 83 | 00cd246052fd5e04eaf4a095fbb7c29a *R/stats-arima-tidiers.R |
| 84 | 4c1f76fbd818154c23d6a2312ac25edb *R/stats-decompose-tidiers.R | |
| 84 | 01d4f8b5c04d489d346438ed12fca6e4 *R/stats-decompose-tidiers.R | |
| 85 | 85 | 9af3af0d331cf932c5492a579382a458 *R/stats-factanal-tidiers.R |
| 86 | 86 | 7e47bef6c903014949d459281f9adbb7 *R/stats-glm-tidiers.R |
| 87 | 8eede361ce831f4c7b63fde8dbb2a485 *R/stats-htest-tidiers.R | |
| 87 | 441c8b6b94407e2c462ab95d49b4f38a *R/stats-htest-tidiers.R | |
| 88 | 88 | c5107a5b84d52685005dc45939145b6c *R/stats-kmeans-tidiers.R |
| 89 | 539e0433407db9d30fefb09d04dbff31 *R/stats-lm-tidiers.R | |
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| 91 | 91 | e9bb4cd088d2886eb415ac26d7bcfa08 *R/stats-mlm-tidiers.R |
| 92 | 8f39406fb3060794c37f44339180c4e6 *R/stats-nls-tidiers.R | |
| 93 | 91a4d3e4099fec4b74405841ff7caab1 *R/stats-prcomp-tidiers.R | |
| 94 | caba965c8c4c20e47cbeec45e919986e *R/stats-smooth.spline-tidiers.R | |
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| 100 | 714b9a6d9da4122ca351fcdc55948f3a *R/survival-coxph-tidiers.R | |
| 99 | 6b5402b57ad5e43f011f83a500c80318 *R/survival-cch-tidiers.R | |
| 100 | a8ebc8b2d274715eaffc40c6898873ba *R/survival-coxph-tidiers.R | |
| 101 | 101 | 51b820fd950f7ebecb8c3655b6fd6fab *R/survival-pyears-tidiers.R |
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| 112 | 112 | a1c1829b987daed614b734ae41c62bfe *R/zzz.R |
| 113 | 113 | ba757dec676977ef193caf8a4153ee5f *README.md |
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| 325 | 325 | ea253b475ca79245ce6c444fb0423af9 *man/tidy.nlrq.Rd |
| 326 | 07ed21f32de50c8325856fb8f891ba2c *man/tidy.nls.Rd | |
| 326 | 1c1a80bc7e4fb792582ef0b8992e94cf *man/tidy.nls.Rd | |
| 327 | 327 | c02f8406c0027af93ea541f6577b8553 *man/tidy.orcutt.Rd |
| 328 | 328 | ab5e6fcd7ec35ed7ec73d8974588f90f *man/tidy.pairwise.htest.Rd |
| 329 | aef0512933782a248b1ef59e59d6b42c *man/tidy.pam.Rd | |
| 329 | d2880fa11805b129a4b03c726faffe3e *man/tidy.pam.Rd | |
| 330 | 330 | 1bd4ed7bd1143d720585f9a65b2644f5 *man/tidy.plm.Rd |
| 331 | 8d69c6f200f6725e4d8be5502f9917cf *man/tidy.poLCA.Rd | |
| 331 | 2b1f157651e390d8237a7962278eb7a6 *man/tidy.poLCA.Rd | |
| 332 | 332 | f44bcae3478222965798da4029d79829 *man/tidy.polr.Rd |
| 333 | 561d1be208176c32092f451aed5df2e9 *man/tidy.power.htest.Rd | |
| 334 | 1ff21807f27af5471fd2867cca405001 *man/tidy.prcomp.Rd | |
| 333 | c86c1b8051537157d4fbfe91d1e89ac7 *man/tidy.power.htest.Rd | |
| 334 | 27eadcd5c7ba4602502d7c4124947096 *man/tidy.prcomp.Rd | |
| 335 | 335 | a8f9eead94a00d866e27b8d83a65c9b6 *man/tidy.pyears.Rd |
| 336 | 4cff4e53584c4415c333a9cfbebd8c89 *man/tidy.rcorr.Rd | |
| 337 | 59da900ec86013dda8b0aa2b7e69612b *man/tidy.ref.grid.Rd | |
| 336 | 25c712fc42f09fa23277e891028faba0 *man/tidy.rcorr.Rd | |
| 337 | ede81656463f16df4742de7b54262f3c *man/tidy.ref.grid.Rd | |
| 338 | 338 | 2664ac6dcb9ff86b34ca71bc9b24fea3 *man/tidy.regsubsets.Rd |
| 339 | 9d18662f19096c41c108a0bae6eb63b9 *man/tidy.ridgelm.Rd | |
| 339 | f0f4f3958d2820e02e0ef1aa881ca27e *man/tidy.ridgelm.Rd | |
| 340 | 340 | 3a6fb1b49cea4153869ce3e472a3ecdb *man/tidy.rlm.Rd |
| 341 | 341 | 8a1ff9f53fa7800fa080622ba1637923 *man/tidy.robustbase.glmrob.Rd |
| 342 | 342 | f1a6ead4045e3df9fd9fd4e7bef5fd29 *man/tidy.robustbase.lmrob.Rd |
| 343 | 1e758a620a2b6addc4115fa6318d30b1 *man/tidy.roc.Rd | |
| 343 | c22462bbed0567eefe95dc78fee8d598 *man/tidy.roc.Rd | |
| 344 | 344 | c54716edaaf5afedf86e3b42ca108d4e *man/tidy.rq.Rd |
| 345 | 345 | 405d00fc8adb766775c6935c3c829086 *man/tidy.rqs.Rd |
| 346 | 346 | a4d325d42d086e29f2b1ede566d9b545 *man/tidy.sarlm.Rd |
| 347 | ed694180f65533b98220652af962def6 *man/tidy.spec.Rd | |
| 347 | 15633df8aeee68f1f828c029306a07c4 *man/tidy.spec.Rd | |
| 348 | 348 | 5205ad2fb503fa299aa90ae0f9c06727 *man/tidy.speedglm.Rd |
| 349 | 349 | 12d30a2408370ce13213058d539e7128 *man/tidy.speedlm.Rd |
| 350 | e74db25118a3c82485cc9b1653889a31 *man/tidy.summary.glht.Rd | |
| 350 | 4f710b7516bc685f091c63ea66b170a9 *man/tidy.summary.glht.Rd | |
| 351 | 351 | 8cb4a595f32240799942f7bd96afc871 *man/tidy.summary.lm.Rd |
| 352 | 3658c82bded9c5ff7a928d051fe93d43 *man/tidy.summary_emm.Rd | |
| 352 | cf3287cd82213e09127998304dadd1a1 *man/tidy.summary_emm.Rd | |
| 353 | 353 | db6f8a7fd7429a096a0a7e1e9fe240b3 *man/tidy.survdiff.Rd |
| 354 | 354 | bbd2d39b971582db22b7757aca60a5bd *man/tidy.survexp.Rd |
| 355 | dc114636eedeed4012e2052f350b1fe9 *man/tidy.survfit.Rd | |
| 356 | 9d72e1eb5adffd951cb28d40755b3b41 *man/tidy.survreg.Rd | |
| 355 | d847ac400e1a77e34283df50ce5cc142 *man/tidy.survfit.Rd | |
| 356 | 03bd6bde61f2f933f49d12cfd688aeb7 *man/tidy.survreg.Rd | |
| 357 | 357 | 9f321efdf528399cff7e35668ba5a25d *man/tidy.svyglm.Rd |
| 358 | 358 | 9b4126d011338b32e47c745a2ac44cf1 *man/tidy.svyolr.Rd |
| 359 | 359 | 3f2f99e6e80ebcdd6a7358c1d9e5a848 *man/tidy.systemfit.Rd |
| 360 | 360 | f8e15a0389aadc2325f8a13cf661cb00 *man/tidy.table.Rd |
| 361 | 361 | b8c043a86cce7062242324d71dde88d4 *man/tidy.ts.Rd |
| 362 | 362 | 2453f34bf129c78004f63def7500d5f6 *man/tidy.varest.Rd |
| 363 | 782052a7497fb91613448ad5aa05818a *man/tidy.zoo.Rd | |
| 363 | 874ef72fe550a6a621809fb94365aabb *man/tidy.zoo.Rd | |
| 364 | 364 | 322fa33097f070f4e8a94025845558f3 *man/tidy_gam_hastie.Rd |
| 365 | 9d0b81b8c65c768a813280b5535ead53 *man/tidy_irlba.Rd | |
| 365 | ae54b2905faa741c81e9c0bbbe0f1f1e *man/tidy_irlba.Rd | |
| 366 | 366 | 06a84012b03e22c1642dfd517a6204d0 *man/tidy_optim.Rd |
| 367 | 4a6baffc1ef654aada036e4e96eb87e2 *man/tidy_svd.Rd | |
| 367 | 9c957ca5803bc4dcb85f0c8b52e72425 *man/tidy_svd.Rd | |
| 368 | 368 | 4e26b8870e5b302601513b3de2549ea4 *man/tidy_xyz.Rd |
| 369 | 369 | dd4a9243950fa76015dd2e904b834992 *man/vector_tidiers.Rd |
| 370 | 370 | 50f330eeca8db092d6807e04457bd06d *tests/spelling.R |
| 371 | 371 | ee8c1b68c4f50d216c1e59dad52e8070 *tests/test-all.R |
| 372 | 6052bbfc5df984a81629588d8c369373 *tests/testthat/test-aaa-documentation-helper.R | |
| 373 | 372 | 6cddcd34001460a5521725562540c11d *tests/testthat/test-aer.R |
| 374 | 373 | e2a28d185876b8711a5fff8b66349938 *tests/testthat/test-aov.R |
| 375 | 374 | 8079176da7944da1509ed030b65d52a6 *tests/testthat/test-auc.R |
| 381 | 380 | cc8ebf48ba5116ad15e4876fb79b2715 *tests/testthat/test-btergm.R |
| 382 | 381 | 7beee6417ceea77a8cb08a3851e31c19 *tests/testthat/test-car.R |
| 383 | 382 | df3b2a5d56252101fbf6fd98f10b8386 *tests/testthat/test-caret.R |
| 384 | 4eb9886c6945547ead747a6ae8449336 *tests/testthat/test-cluster.R | |
| 383 | 1f39bdadd8a91473d455c508ab40be80 *tests/testthat/test-cluster.R | |
| 385 | 384 | dd81840f362c7a15fbce5de9a58c6e26 *tests/testthat/test-cmprsk.R |
| 386 | 385 | 3d298bac994dcfbe8ba9efa8cdee6ab1 *tests/testthat/test-drc.R |
| 387 | 386 | 41000fc6116247dae6234742ecdb53e6 *tests/testthat/test-emmeans.R |
| 399 | 398 | 9350fbc996a20bdbfb1e8a736ba7d463 *tests/testthat/test-ks.R |
| 400 | 399 | c12589d5d0f961d6ba6e56e50e99cff9 *tests/testthat/test-lavaan.R |
| 401 | 400 | 2eca4ca161af7bb45c4355b7629e1a9a *tests/testthat/test-leaps.R |
| 402 | 68426b789cafcb11dd5a100ebc7216a6 *tests/testthat/test-lfe.R | |
| 401 | 4b7cc7e3536e3e85498101e519f0d668 *tests/testthat/test-lfe.R | |
| 403 | 402 | b480bcdef3b2caa56a03b114a691b98b *tests/testthat/test-list-irlba.R |
| 404 | 403 | 53e59bef082f622042cf77cafc8edc96 *tests/testthat/test-list-optim.R |
| 405 | 404 | 0e228297dd031755687c70a92a66964d *tests/testthat/test-list-svd.R |
| 415 | 414 | 63bef46f66a55a31ad0fd3c778de0d34 *tests/testthat/test-mass-polr.R |
| 416 | 415 | 1d5a4eff5796abbfb198ce6bf1289517 *tests/testthat/test-mass-ridgelm.R |
| 417 | 416 | 287fb52c8898c191cc68ca4ba94190f1 *tests/testthat/test-mass-rlm.R |
| 418 | f4fb5551722d60b2dde5d64764dd2841 *tests/testthat/test-mclust.R | |
| 417 | bc64d1740c57f3fa0835e1bc8e83f2ba *tests/testthat/test-mclust.R | |
| 419 | 418 | aa4b5c55c684e93e9b00e8f95b6e4ff4 *tests/testthat/test-mediation.R |
| 420 | 419 | 607c59df208bfaf465133bd524a8a896 *tests/testthat/test-metafor.R |
| 421 | 06e6ff820e4023e1092a51fbb396a982 *tests/testthat/test-mfx.R | |
| 422 | 3dcd010cce4a386c59cbe546ac9311b6 *tests/testthat/test-mgcv.R | |
| 423 | 4157af0039f6c26f142dc9bfb74f8cc5 *tests/testthat/test-muhaz.R | |
| 420 | a499ce1e0678d4a384c1279a09acd0b8 *tests/testthat/test-mfx.R | |
| 421 | fa2adb1fe65d1cc58d5bc74cb132db7c *tests/testthat/test-mgcv.R | |
| 422 | ad241cbc465608381dcb6f644aba772e *tests/testthat/test-muhaz.R | |
| 424 | 423 | cbef2c791f181283735663fe060c87db *tests/testthat/test-multcomp.R |
| 425 | 424 | 3fb071fb263841ddd09e2a59255b7c64 *tests/testthat/test-nnet.R |
| 426 | 425 | 39c854757f25e3b0327944473a9bdb85 *tests/testthat/test-null-and-default.R |
| 433 | 432 | d12379331b0b98602c658b1de5809d05 *tests/testthat/test-quantreg-rq.R |
| 434 | 433 | 4ae33991c38416bce07d7b6f1550dbbc *tests/testthat/test-quantreg-rqs.R |
| 435 | 434 | 098ac7958932093a21ef2631a04258ac *tests/testthat/test-robust.R |
| 436 | 3554b5e232ae064827e9d8f888851d23 *tests/testthat/test-robustbase.R | |
| 435 | 7c3a7e5eafd3ea72bbc2e2f26deb2171 *tests/testthat/test-robustbase.R | |
| 437 | 436 | 4d92b111afeb56a7c953f6bbae78d6c8 *tests/testthat/test-sp.R |
| 438 | 437 | 7e2b132bd16f7cb05785789d5f370141 *tests/testthat/test-spdep.R |
| 439 | 438 | 9e505e23ba065f466d96f5b7e2150667 *tests/testthat/test-speedglm-speedglm.R |
| 442 | 441 | 6b380611f6a1b804a44d62a89a29dffd *tests/testthat/test-stats-arima.R |
| 443 | 442 | e87f283681d702c9b7c9e9e6175ea26f *tests/testthat/test-stats-decompose.R |
| 444 | 443 | b465102ed72d0f61c27b304a059173e2 *tests/testthat/test-stats-factanal.R |
| 445 | 97df7704f71226b33c19c4d44f5c5b74 *tests/testthat/test-stats-glm.R | |
| 444 | 92a8522c58d3cab7cfe87442834549ae *tests/testthat/test-stats-glm.R | |
| 446 | 445 | 7ac21cd4b71613cbdd49ca118375e4f0 *tests/testthat/test-stats-htest.R |
| 447 | 446 | f637a56842c8e7b35faacd4db5749a15 *tests/testthat/test-stats-kmeans.R |
| 448 | 447 | aca1721884b3b473fc494915e543d618 *tests/testthat/test-stats-lm.R |
| 456 | 455 | 626bc9aa0d826dd67c9469576bc84a01 *tests/testthat/test-survey.R |
| 457 | 456 | b88a2a5e3185994bab7e5d5a44d82dab *tests/testthat/test-survival-aareg.R |
| 458 | 457 | 6af265db3b7dacca2aeda94c73fb4b51 *tests/testthat/test-survival-cch.R |
| 459 | ef6f2baf720fff04eaeb197ce8c14963 *tests/testthat/test-survival-coxph.R | |
| 458 | c6de5c42c53cdd386bd95be97619b8d2 *tests/testthat/test-survival-coxph.R | |
| 460 | 459 | b54ebca8f1ce212de324061879a2eb92 *tests/testthat/test-survival-pyears.R |
| 461 | 460 | e31daf532003cf1fd9989356742ac1ac *tests/testthat/test-survival-survdiff.R |
| 462 | 461 | 78cfe7d1a286d8b49eef5a2652d2b0ed *tests/testthat/test-survival-survexp.R |
| 463 | 462 | 06c5351728b8d5c52f7f05a8c037cc7b *tests/testthat/test-survival-survfit.R |
| 464 | 07bde389cabde75beb0320c5a649e632 *tests/testthat/test-survival-survreg.R | |
| 463 | 751026716654c107a4fa7b39fb0845bb *tests/testthat/test-survival-survreg.R | |
| 465 | 464 | d9d0904b1018f905c5d3bb9ada5c5884 *tests/testthat/test-systemfit.R |
| 466 | 465 | 81394af7a4e283671f56ae6d44114821 *tests/testthat/test-tseries.R |
| 467 | 02fc6e88f2a0c60f2e098f6f33fe9a76 *tests/testthat/test-utilities.R | |
| 466 | 28d75afb23bec63802dcdf82edd73049 *tests/testthat/test-utilities.R | |
| 468 | 467 | 935809ca0977cb4ffb09deca6158b62f *tests/testthat/test-vars.R |
| 469 | 468 | a828c17aaa8b851c1e22f9d9d1a9bbde *tests/testthat/test-zoo.R |
| 470 | 469 | 906ef10bdc07a57e4b0bf997068bf859 *vignettes/adding-tidiers.Rmd |
| 472 | 471 | ff8f69aaff51a387afb999c83d6fdcb6 *vignettes/available-methods.Rmd |
| 473 | 472 | e078805eb9f9c16e9c24b376f71ec778 *vignettes/bootstrapping.Rmd |
| 474 | 473 | 5092072954108fd18db2b727844a034e *vignettes/broom.Rmd |
| 475 | f6bd7233294809d0bfcd8eb4b6085cd6 *vignettes/broom_and_dplyr.Rmd | |
| 474 | 127a6fe5a6336908d469904804c703dc *vignettes/broom_and_dplyr.Rmd | |
| 476 | 475 | 6fc83a2700fedd6988f437fc7fd6d6de *vignettes/kmeans.Rmd |
| 0 | # broom 1.0.2 | |
| 1 | ||
| 2 | * The default `data` argument for `augment.coxph()` and `augment.survreg()` has been transitioned from `NULL` to `model.frame(x)` (#1126 by `@capnrefsmmat`). | |
| 3 | * Migrated 'ggplot2' from strong to weak dependency, i.e. moved from `Imports` to `Suggests`. | |
| 4 | * Fixed a bug where `augment()` results would not include residuals when the response term included a function call (#1121, #946, #937, #124). | |
| 5 | ||
| 0 | 6 | # broom 1.0.1 |
| 1 | 7 | |
| 2 | 8 | * Improves performance of `tidy.lm()` and `tidy.glm()` for full-rank fits (#1112 by `@capnrefsmmat`). |
| 5 | 5 | #' |
| 6 | 6 | #' @evalRd return_tidy("cutoff", "tpr", "fpr") |
| 7 | 7 | #' |
| 8 | #' @examplesIf rlang::is_installed("AUC") | |
| 8 | #' @examplesIf rlang::is_installed(c("AUC", "ggplot2")) | |
| 9 | 9 | #' |
| 10 | 10 | #' # load libraries for models and data |
| 11 | 11 | #' library(AUC) |
| 41 | 41 | #' power = "Power achieved for given value of n." |
| 42 | 42 | #' ) |
| 43 | 43 | #' |
| 44 | #' @examples | |
| 45 | #' | |
| 46 | #' if (requireNamespace("binGroup", quietly = TRUE)) { | |
| 44 | #' @examplesIf rlang::is_installed(c("binGroup", "ggplot2")) | |
| 47 | 45 | #' |
| 48 | 46 | #' library(binGroup) |
| 49 | 47 | #' des <- binDesign( |
| 59 | 57 | #' ggplot(tidy(des), aes(n, power)) + |
| 60 | 58 | #' geom_line() |
| 61 | 59 | #' |
| 62 | #' } | |
| 63 | 60 | #' |
| 64 | 61 | #' @export |
| 65 | 62 | #' @family bingroup tidiers |
| 84 | 81 | #' maxit = "Number of iterations performed." |
| 85 | 82 | #' ) |
| 86 | 83 | #' |
| 87 | #' @examplesIf rlang::is_installed("binGroup") | |
| 84 | #' @examplesIf rlang::is_installed(c("binGroup", "ggplot2")) | |
| 88 | 85 | #' |
| 89 | 86 | #' # load libraries for models and data |
| 90 | 87 | #' library(binGroup) |
| 25 | 25 | #' @keywords internal |
| 26 | 26 | "_PACKAGE" |
| 27 | 27 | |
| 28 | # address unused Imports warning from R CMD check | |
| 29 | import_ggplot <- function() { | |
| 30 | ggplot2::aes() | |
| 31 | } |
| 24 | 24 | #' @seealso [tidy()], [cluster::pam()] |
| 25 | 25 | #' @family pam tidiers |
| 26 | 26 | # skip running examples - occasionally over CRAN check time limit |
| 27 | #' @examplesIf (rlang::is_installed("cluster") & rlang::is_installed("modeldata") && identical(Sys.getenv("NOT_CRAN"), "true")) | |
| 27 | #' @examplesIf (rlang::is_installed(c("cluster", "modeldata", "ggplot2")) && identical(Sys.getenv("NOT_CRAN"), "true")) | |
| 28 | 28 | #' |
| 29 | 29 | #' # load libraries for models and data |
| 30 | 30 | #' library(dplyr) |
| 31 | 31 | #' kurtosis and related tests. R package version 0.14. \cr |
| 32 | 32 | #' https://CRAN.R-project.org/package=moments |
| 33 | 33 | #' |
| 34 | #' @examples | |
| 34 | #' @examplesIf rlang::is_installed("ggplot2") | |
| 35 | 35 | #' |
| 36 | 36 | #' td <- tidy(mtcars) |
| 37 | 37 | #' td |
| 25 | 25 | #' There are a large number of arguments that can be |
| 26 | 26 | #' passed on to [emmeans::summary.emmGrid()] or [lsmeans::summary.ref.grid()]. |
| 27 | 27 | #' |
| 28 | #' @examplesIf rlang::is_installed("emmeans") | |
| 28 | #' @examplesIf rlang::is_installed(c("emmeans", "ggplot2")) | |
| 29 | 29 | #' |
| 30 | 30 | #' # load libraries for models and data |
| 31 | 31 | #' library(emmeans) |
| 201 | 201 | col_order <- c("r.squared", "adj.r.squared", "within.r.squared", |
| 202 | 202 | "pseudo.r.squared", "sigma", "nobs", "AIC", "BIC", "logLik") |
| 203 | 203 | res <- bind_cols(res_common, res_r2, res_specific) %>% |
| 204 | select(col_order) | |
| 204 | select(dplyr::any_of(col_order)) | |
| 205 | 205 | res |
| 206 | 206 | } |
| 15 | 15 | #' lamdba" |
| 16 | 16 | #' ) |
| 17 | 17 | #' |
| 18 | #' @examplesIf rlang::is_installed("glmnet") | |
| 18 | #' @examplesIf rlang::is_installed(c("glmnet", "ggplot2")) | |
| 19 | 19 | #' |
| 20 | 20 | #' # load libraries for models and data |
| 21 | 21 | #' library(glmnet) |
| 23 | 23 | #' logical. Furthermore, predictions make sense only with a specific |
| 24 | 24 | #' choice of lambda. |
| 25 | 25 | #' |
| 26 | #' @examplesIf rlang::is_installed("glmnet") | |
| 26 | #' @examplesIf rlang::is_installed(c("glmnet", "ggplot2")) | |
| 27 | 27 | #' |
| 28 | 28 | #' # load libraries for models and data |
| 29 | 29 | #' library(glmnet) |
| 7 | 7 | #' |
| 8 | 8 | #' @evalRd return_tidy(regression = TRUE) |
| 9 | 9 | #' |
| 10 | #' @examplesIf rlang::is_installed("gmm") | |
| 10 | #' @examplesIf rlang::is_installed(c("gmm", "ggplot2")) | |
| 11 | 11 | #' |
| 12 | 12 | #' # load libraries for models and data |
| 13 | 13 | #' library(gmm) |
| 20 | 20 | #' `cor(B, A)`. Only one of these pairs will ever be present in the tidy |
| 21 | 21 | #' output. |
| 22 | 22 | #' |
| 23 | #' @examplesIf rlang::is_installed("Hmisc") | |
| 23 | #' @examplesIf rlang::is_installed(c("Hmisc", "ggplot2")) | |
| 24 | 24 | #' |
| 25 | 25 | #' # load libraries for models and data |
| 26 | 26 | #' library(Hmisc) |
| 9 | 9 | #' \code{tidyr::pivot_wider(..., names_from = variable, values_from = value)} |
| 10 | 10 | #' on the output to return to a wide format. |
| 11 | 11 | #' |
| 12 | #' @examplesIf rlang::is_installed("ks") | |
| 12 | #' @examplesIf rlang::is_installed(c("ks", "ggplot2")) | |
| 13 | 13 | #' |
| 14 | 14 | #' # load libraries for models and data |
| 15 | 15 | #' library(ks) |
| 3 | 3 | #' @inherit tidy.prcomp return details params |
| 4 | 4 | #' @param x A list with components `u`, `d`, `v` returned by [base::svd()]. |
| 5 | 5 | #' |
| 6 | #' @examplesIf rlang::is_installed("modeldata") | |
| 6 | #' @examplesIf rlang::is_installed(c("modeldata", "ggplot2")) | |
| 7 | 7 | #' |
| 8 | 8 | #' library(modeldata) |
| 9 | 9 | #' data(hpc_data) |
| 22 | 22 | #' be valid. More information can be found in |
| 23 | 23 | #' `vignette("mod2user", package = "lmodel2")`. |
| 24 | 24 | #' |
| 25 | #' @examplesIf rlang::is_installed("lmodel2") | |
| 25 | #' @examplesIf rlang::is_installed(c("lmodel2", "ggplot2")) | |
| 26 | 26 | #' |
| 27 | 27 | #' # load libraries for models and data |
| 28 | 28 | #' library(lmodel2) |
| 11 | 11 | #' and depend on the inputted map object. See ?maps::map for more information." |
| 12 | 12 | #' ) |
| 13 | 13 | #' |
| 14 | #' @examplesIf rlang::is_installed("maps") | |
| 14 | #' @examplesIf rlang::is_installed(c("maps", "ggplot2")) | |
| 15 | 15 | #' |
| 16 | 16 | #' # load libraries for models and data |
| 17 | 17 | #' library(maps) |
| 101 | 101 | ret <- |
| 102 | 102 | ret %>% |
| 103 | 103 | dplyr::select( |
| 104 | term = .data$factor, | |
| 104 | term = factor, | |
| 105 | 105 | dplyr::contains("at."), |
| 106 | estimate = .data$AME, | |
| 107 | std.error = .data$SE, | |
| 108 | statistic = .data$z, | |
| 109 | p.value = .data$p, | |
| 110 | conf.low = .data$lower, | |
| 111 | conf.high = .data$upper | |
| 106 | estimate = AME, | |
| 107 | std.error = SE, | |
| 108 | statistic = z, | |
| 109 | p.value = p, | |
| 110 | conf.low = lower, | |
| 111 | conf.high = upper | |
| 112 | 112 | ) |
| 113 | 113 | |
| 114 | 114 | # Remove confidence interval if not specified |
| 8 | 8 | #' scale = "Scaling factor of estimated coefficient" |
| 9 | 9 | #' ) |
| 10 | 10 | #' |
| 11 | #' @examplesIf rlang::is_installed("MASS") | |
| 11 | #' @examplesIf rlang::is_installed(c("MASS", "ggplot2")) | |
| 12 | 12 | #' |
| 13 | 13 | #' # load libraries for models and data |
| 14 | 14 | #' library(MASS) |
| 6 | 6 | #' |
| 7 | 7 | #' @evalRd return_tidy("contrast", "null.value", "estimate") |
| 8 | 8 | #' |
| 9 | #' @examplesIf rlang::is_installed("multcomp") | |
| 9 | #' @examplesIf rlang::is_installed(c("multcomp", "ggplot2")) | |
| 10 | 10 | #' |
| 11 | 11 | #' # load libraries for models and data |
| 12 | 12 | #' library(multcomp) |
| 11 | 11 | #' "std.error" |
| 12 | 12 | #' ) |
| 13 | 13 | #' |
| 14 | #' @examplesIf rlang::is_installed("poLCA") | |
| 14 | #' @examplesIf rlang::is_installed(c("poLCA", "ggplot2")) | |
| 15 | 15 | #' |
| 16 | 16 | #' # load libraries for models and data |
| 17 | 17 | #' library(poLCA) |
| 92 | 92 | } |
| 93 | 93 | |
| 94 | 94 | probs <- probs %>% |
| 95 | mutate(class = utils::type.convert(class)) | |
| 95 | mutate(class = utils::type.convert(class, as.is = TRUE)) | |
| 96 | 96 | |
| 97 | 97 | probs_se <- purrr::map2_df(x$probs.se, names(x$probs.se), reshape_probs) %>% |
| 98 | 98 | mutate(variable = as.character(variable)) %>% |
| 14 | 14 | #' cannot be set in `tidy`. Instead you must set the `alpha` argument |
| 15 | 15 | #' to [psych::cohen.kappa()] when creating the `kappa` object. |
| 16 | 16 | #' |
| 17 | #' @examplesIf rlang::is_installed("psych") | |
| 17 | #' @examplesIf rlang::is_installed(c("psych", "ggplot2")) | |
| 18 | 18 | #' |
| 19 | 19 | #' # load libraries for models and data |
| 20 | 20 | #' library(psych) |
| 132 | 132 | ret < cbind(cbind(term, ret), response) |
| 133 | 133 | row.names(ret) <- NULL |
| 134 | 134 | } |
| 135 | } else if (is.null(ret$term) & length(mod_lines) != 0) { | |
| 135 | } else if ((!"term" %in% colnames(ret)) & length(mod_lines) != 0) { | |
| 136 | 136 | mods <- sub(".*: ", "", strsplit(mod_lines, "\n")[[1]]) |
| 137 | 137 | ret <- cbind(term = mods, ret) |
| 138 | } else if (is.null(ret$term) & !is.null(row.names(ret))) { | |
| 138 | } else if ((!"term" %in% colnames(ret)) & !is.null(row.names(ret))) { | |
| 139 | 139 | ret <- cbind(term = row.names(ret), ret) |
| 140 | 140 | row.names(ret) <- NULL |
| 141 | 141 | } |
| 14 | 14 | #' \item{`.seasadj`}{The seasonally adjusted (or "deseasonalised") |
| 15 | 15 | #' series.} |
| 16 | 16 | #' |
| 17 | #' @examples | |
| 17 | #' @examplesIf rlang::is_installed("ggplot2") | |
| 18 | 18 | #' |
| 19 | 19 | #' # time series of temperatures in Nottingham, 1920-1939: |
| 20 | 20 | #' nottem |
| 225 | 225 | #' |
| 226 | 226 | #' @evalRd return_tidy("n", "delta", "sd", "sig.level", "power") |
| 227 | 227 | #' |
| 228 | #' @examples | |
| 228 | #' @examplesIf rlang::is_installed("ggplot2") | |
| 229 | 229 | #' |
| 230 | 230 | #' ptt <- power.t.test(n = 2:30, delta = 1) |
| 231 | 231 | #' tidy(ptt) |
| 10 | 10 | #' @details If the linear model is an `mlm` object (multiple linear model), |
| 11 | 11 | #' there is an additional column `response`. See [tidy.mlm()]. |
| 12 | 12 | #' |
| 13 | #' @examples | |
| 13 | #' @examplesIf rlang::is_installed("ggplot2") | |
| 14 | 14 | #' |
| 15 | 15 | #' library(ggplot2) |
| 16 | 16 | #' library(dplyr) |
| 6 | 6 | #' |
| 7 | 7 | #' @evalRd return_tidy(regression = TRUE) |
| 8 | 8 | #' |
| 9 | #' @examples | |
| 9 | #' @examplesIf rlang::is_installed("ggplot2") | |
| 10 | 10 | #' |
| 11 | 11 | #' # fit model |
| 12 | 12 | #' n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) |
| 53 | 53 | #' for information on how to interpret the various tidied matrices. Note |
| 54 | 54 | #' that SVD is only equivalent to PCA on centered data. |
| 55 | 55 | #' |
| 56 | #' @examplesIf rlang::is_installed("maps") | |
| 56 | #' @examplesIf rlang::is_installed(c("maps", "ggplot2")) | |
| 57 | 57 | #' |
| 58 | 58 | #' pc <- prcomp(USArrests, scale = TRUE) |
| 59 | 59 | #' |
| 4 | 4 | #' @template param_data |
| 5 | 5 | #' @template param_unused_dots |
| 6 | 6 | #' |
| 7 | #' @examples | |
| 7 | #' @examplesIf rlang::is_installed("ggplot2") | |
| 8 | 8 | #' |
| 9 | 9 | #' # fit model |
| 10 | 10 | #' spl <- smooth.spline(mtcars$wt, mtcars$mpg, df = 4) |
| 71 | 71 | #' |
| 72 | 72 | #' @evalRd return_tidy("freq", "spec") |
| 73 | 73 | #' |
| 74 | #' @examples | |
| 74 | #' @examplesIf rlang::is_installed("ggplot2") | |
| 75 | 75 | #' |
| 76 | 76 | #' spc <- spectrum(lh) |
| 77 | 77 | #' tidy(spc) |
| 6 | 6 | #' |
| 7 | 7 | #' @evalRd return_tidy(regression = TRUE) |
| 8 | 8 | #' |
| 9 | #' @examplesIf rlang::is_installed("survival") | |
| 9 | #' @examplesIf rlang::is_installed(c("survival", "ggplot2")) | |
| 10 | 10 | #' |
| 11 | 11 | #' # load libraries for models and data |
| 12 | 12 | #' library(survival) |
| 12 | 12 | #' "p.value" |
| 13 | 13 | #' ) |
| 14 | 14 | #' |
| 15 | #' @examplesIf rlang::is_installed("survival") | |
| 15 | #' @examplesIf rlang::is_installed(c("survival", "ggplot2")) | |
| 16 | 16 | #' |
| 17 | 17 | #' # load libraries for models and data |
| 18 | 18 | #' library(survival) |
| 114 | 114 | #' @seealso [augment()], [survival::coxph()] |
| 115 | 115 | #' @family coxph tidiers |
| 116 | 116 | #' @family survival tidiers |
| 117 | augment.coxph <- function(x, data = NULL, newdata = NULL, | |
| 117 | augment.coxph <- function(x, data = model.frame(x), newdata = NULL, | |
| 118 | 118 | type.predict = "lp", type.residuals = "martingale", |
| 119 | 119 | ...) { |
| 120 | if (is.null(data) && is.null(newdata)) { | |
| 121 | stop("Must specify either `data` or `newdata` argument.", call. = FALSE) | |
| 122 | } | |
| 123 | ||
| 124 | 120 | augment_columns(x, data, newdata, |
| 125 | 121 | type.predict = type.predict, |
| 126 | 122 | type.residuals = type.residuals |
| 17 | 17 | #' strata = "strata if stratified survfit object input" |
| 18 | 18 | #' ) |
| 19 | 19 | #' |
| 20 | #' @examplesIf rlang::is_installed("survival") | |
| 20 | #' @examplesIf rlang::is_installed(c("survival", "ggplot2")) | |
| 21 | 21 | #' |
| 22 | 22 | #' # load libraries for models and data |
| 23 | 23 | #' library(survival) |
| 6 | 6 | #' |
| 7 | 7 | #' @evalRd return_tidy(regression = TRUE) |
| 8 | 8 | #' |
| 9 | #' @examplesIf rlang::is_installed("survival") | |
| 9 | #' @examplesIf rlang::is_installed(c("survival", "ggplot2")) | |
| 10 | 10 | #' |
| 11 | 11 | #' # load libraries for models and data |
| 12 | 12 | #' library(survival) |
| 74 | 74 | #' @seealso [augment()], [survival::survreg()] |
| 75 | 75 | #' @family survreg tidiers |
| 76 | 76 | #' @family survival tidiers |
| 77 | augment.survreg <- function(x, data = NULL, newdata = NULL, | |
| 77 | augment.survreg <- function(x, data = model.frame(x), newdata = NULL, | |
| 78 | 78 | type.predict = "response", |
| 79 | 79 | type.residuals = "response", ...) { |
| 80 | if (is.null(data) && is.null(newdata)) { | |
| 81 | stop("Must specify either `data` or `newdata` argument.", call. = FALSE) | |
| 82 | } | |
| 83 | ||
| 84 | 80 | augment_columns(x, data, newdata, |
| 85 | 81 | type.predict = type.predict, |
| 86 | 82 | type.residuals = type.residuals |
| 12 | 12 | } |
| 13 | 13 | |
| 14 | 14 | exponentiate <- function(data, col = "estimate") { |
| 15 | data <- mutate_at(data, vars(col), exp) | |
| 15 | data <- data %>% mutate(across(all_of(col), exp)) | |
| 16 | 16 | |
| 17 | 17 | if ("conf.low" %in% colnames(data)) { |
| 18 | data <- mutate_at(data, vars(conf.low, conf.high), exp) | |
| 18 | data <- data %>% mutate(across(c(conf.low, conf.high), exp)) | |
| 19 | 19 | } |
| 20 | 20 | |
| 21 | 21 | data |
| 323 | 323 | as_tibble(ret) |
| 324 | 324 | } |
| 325 | 325 | |
| 326 | response <- function(object, newdata = NULL) { | |
| 327 | model.response(model.frame(terms(object), data = newdata, na.action = na.pass)) | |
| 326 | response <- function(object, newdata = NULL, has_response) { | |
| 327 | if (!has_response) { | |
| 328 | return(NULL) | |
| 329 | } | |
| 330 | ||
| 331 | res <- | |
| 332 | tryCatch( | |
| 333 | model.response(model.frame(terms(object), data = newdata, na.action = na.pass)), | |
| 334 | error = function(e) NULL | |
| 335 | ) | |
| 336 | ||
| 337 | if (is.null(res)) { | |
| 338 | res <- model.response(model.frame(object)) | |
| 339 | } | |
| 340 | ||
| 341 | res | |
| 328 | 342 | } |
| 329 | 343 | |
| 330 | 344 | data_error <- function(cnd) { |
| 370 | 384 | df <- if (passed_newdata) newdata else data |
| 371 | 385 | df <- as_augment_tibble(df) |
| 372 | 386 | # interval <- match.arg(interval) |
| 373 | # Check if response variable is in newdata: | |
| 374 | response_var_in_newdata <- x$call %>% | |
| 375 | all.vars() %>% | |
| 376 | .[[1]] %>% | |
| 377 | is.element(names(df)) | |
| 387 | # check if response variable is in newdata, if relevant: | |
| 388 | if (!is.null(x$terms) & inherits(x$terms, "formula")) { | |
| 389 | has_response <- | |
| 390 | # TRUE if response includes a function call and is in column names, | |
| 391 | # usually with no `data` or `newdata` supplied, | |
| 392 | # and `data` defaults to `model_frame(x)` | |
| 393 | rlang::as_label(rlang::f_lhs(x$terms)) %in% names(df) || | |
| 394 | # TRUE if the response variable itself is in column names | |
| 395 | all.vars(x$terms)[1] %in% names(df) | |
| 396 | } else { | |
| 397 | has_response <- FALSE | |
| 398 | } | |
| 378 | 399 | |
| 379 | 400 | # NOTE: It is important use predict(x, newdata = newdata) rather than |
| 380 | 401 | # predict(x, newdata = df). This is to avoid an edge case breakage |
| 434 | 455 | } |
| 435 | 456 | } |
| 436 | 457 | |
| 437 | resp <- safe_response(x, df) | |
| 458 | resp <- safe_response(x, df, has_response) | |
| 438 | 459 | |
| 439 | 460 | if (!is.null(resp) && is.numeric(resp)) { |
| 440 | 461 | df$.resid <- (resp - df$.fitted) %>% unname() |
| 517 | 538 | ".tau", |
| 518 | 539 | "aic", |
| 519 | 540 | "alternative", |
| 541 | "AME", | |
| 520 | 542 | "bic", |
| 521 | 543 | "chosen", |
| 522 | 544 | "ci.lower", |
| 562 | 584 | "lhs", |
| 563 | 585 | "lm", |
| 564 | 586 | "loading", |
| 587 | "lower", | |
| 565 | 588 | "method", |
| 566 | 589 | "Method", |
| 567 | 590 | "N", |
| 571 | 594 | "objs", |
| 572 | 595 | "obs", |
| 573 | 596 | "op", |
| 597 | "p", | |
| 574 | 598 | "p.value", |
| 575 | 599 | "packageVersion", |
| 576 | 600 | "PC", |
| 586 | 610 | "rowname", |
| 587 | 611 | "rstudent", |
| 588 | 612 | "se", |
| 613 | "SE", | |
| 589 | 614 | "series", |
| 590 | 615 | "Slope", |
| 591 | 616 | "stat", |
| 598 | 623 | "tau2.del", |
| 599 | 624 | "term", |
| 600 | 625 | "type", |
| 626 | "upper", | |
| 601 | 627 | "value", |
| 602 | 628 | "Var1", |
| 603 | 629 | "Var2", |
| 5 | 5 | #' |
| 6 | 6 | #' @evalRd return_tidy("index", "series", "value") |
| 7 | 7 | #' |
| 8 | #' @examplesIf rlang::is_installed("zoo") | |
| 8 | #' @examplesIf rlang::is_installed(c("zoo", "ggplot2")) | |
| 9 | 9 | #' |
| 10 | 10 | #' # load libraries for models and data |
| 11 | 11 | #' library(zoo) |
Binary diff not shown
| 0 | 0 | ## ----setup, include = FALSE--------------------------------------------------- |
| 1 | 1 | knitr::opts_chunk$set(message = FALSE, warning = FALSE) |
| 2 | ||
| 3 | if (rlang::is_installed("ggplot2")) { | |
| 4 | run <- TRUE | |
| 5 | } else { | |
| 6 | run <- FALSE | |
| 7 | } | |
| 8 | ||
| 9 | knitr::opts_chunk$set( | |
| 10 | eval = run | |
| 11 | ) | |
| 2 | 12 | |
| 3 | 13 | ## ----------------------------------------------------------------------------- |
| 4 | 14 | library(broom) |
| 9 | 9 | |
| 10 | 10 | ```{r setup, include = FALSE} |
| 11 | 11 | knitr::opts_chunk$set(message = FALSE, warning = FALSE) |
| 12 | ||
| 13 | if (rlang::is_installed("ggplot2")) { | |
| 14 | run <- TRUE | |
| 15 | } else { | |
| 16 | run <- FALSE | |
| 17 | } | |
| 18 | ||
| 19 | knitr::opts_chunk$set( | |
| 20 | eval = run | |
| 21 | ) | |
| 12 | 22 | ``` |
| 13 | 23 | |
| 14 | 24 | # broom and dplyr |
| 57 | 57 | object with varying degrees of success. |
| 58 | 58 | |
| 59 | 59 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 60 | \strong{same number of rows} as the passed dataset. This means that the | |
| 61 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 62 | support matrix-columns. This means you should not specify a matrix | |
| 63 | of covariates in a model formula during the original model fitting | |
| 64 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 65 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 66 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 67 | model on data in a tibble. | |
| 60 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 61 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 62 | of a matrix of covariates, such as when the model formula uses | |
| 63 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 64 | as a matrix column. | |
| 68 | 65 | |
| 69 | 66 | We are in the process of defining behaviors for models fit with various |
| 70 | 67 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 78 | 78 | object with varying degrees of success. |
| 79 | 79 | |
| 80 | 80 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 81 | \strong{same number of rows} as the passed dataset. This means that the | |
| 82 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 83 | support matrix-columns. This means you should not specify a matrix | |
| 84 | of covariates in a model formula during the original model fitting | |
| 85 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 86 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 87 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 88 | model on data in a tibble. | |
| 81 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 82 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 83 | of a matrix of covariates, such as when the model formula uses | |
| 84 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 85 | as a matrix column. | |
| 89 | 86 | |
| 90 | 87 | We are in the process of defining behaviors for models fit with various |
| 91 | 88 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 79 | 79 | object with varying degrees of success. |
| 80 | 80 | |
| 81 | 81 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 82 | \strong{same number of rows} as the passed dataset. This means that the | |
| 83 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 84 | support matrix-columns. This means you should not specify a matrix | |
| 85 | of covariates in a model formula during the original model fitting | |
| 86 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 87 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 88 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 89 | model on data in a tibble. | |
| 82 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 83 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 84 | of a matrix of covariates, such as when the model formula uses | |
| 85 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 86 | as a matrix column. | |
| 90 | 87 | |
| 91 | 88 | We are in the process of defining behaviors for models fit with various |
| 92 | 89 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 71 | 71 | object with varying degrees of success. |
| 72 | 72 | |
| 73 | 73 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 74 | \strong{same number of rows} as the passed dataset. This means that the | |
| 75 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 76 | support matrix-columns. This means you should not specify a matrix | |
| 77 | of covariates in a model formula during the original model fitting | |
| 78 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 79 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 80 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 81 | model on data in a tibble. | |
| 74 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 75 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 76 | of a matrix of covariates, such as when the model formula uses | |
| 77 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 78 | as a matrix column. | |
| 82 | 79 | |
| 83 | 80 | We are in the process of defining behaviors for models fit with various |
| 84 | 81 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 5 | 5 | \usage{ |
| 6 | 6 | \method{augment}{coxph}( |
| 7 | 7 | x, |
| 8 | data = NULL, | |
| 8 | data = model.frame(x), | |
| 9 | 9 | newdata = NULL, |
| 10 | 10 | type.predict = "lp", |
| 11 | 11 | type.residuals = "martingale", |
| 79 | 79 | object with varying degrees of success. |
| 80 | 80 | |
| 81 | 81 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 82 | \strong{same number of rows} as the passed dataset. This means that the | |
| 83 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 84 | support matrix-columns. This means you should not specify a matrix | |
| 85 | of covariates in a model formula during the original model fitting | |
| 86 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 87 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 88 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 89 | model on data in a tibble. | |
| 82 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 83 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 84 | of a matrix of covariates, such as when the model formula uses | |
| 85 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 86 | as a matrix column. | |
| 90 | 87 | |
| 91 | 88 | We are in the process of defining behaviors for models fit with various |
| 92 | 89 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 103 | 100 | warning is raised and the incomplete rows are dropped. |
| 104 | 101 | } |
| 105 | 102 | \examples{ |
| 106 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 103 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 107 | 104 | |
| 108 | 105 | # load libraries for models and data |
| 109 | 106 | library(survival) |
| 62 | 62 | object with varying degrees of success. |
| 63 | 63 | |
| 64 | 64 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 65 | \strong{same number of rows} as the passed dataset. This means that the | |
| 66 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 67 | support matrix-columns. This means you should not specify a matrix | |
| 68 | of covariates in a model formula during the original model fitting | |
| 69 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 70 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 71 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 72 | model on data in a tibble. | |
| 65 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 66 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 67 | of a matrix of covariates, such as when the model formula uses | |
| 68 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 69 | as a matrix column. | |
| 73 | 70 | |
| 74 | 71 | We are in the process of defining behaviors for models fit with various |
| 75 | 72 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 76 | 73 | missing at this time. |
| 77 | 74 | } |
| 78 | 75 | \examples{ |
| 76 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 79 | 77 | |
| 80 | 78 | # time series of temperatures in Nottingham, 1920-1939: |
| 81 | 79 | nottem |
| 126 | 124 | group = decomp |
| 127 | 125 | )) |
| 128 | 126 | |
| 127 | \dontshow{\}) # examplesIf} | |
| 129 | 128 | } |
| 130 | 129 | \seealso{ |
| 131 | 130 | \code{\link[=augment]{augment()}}, \code{\link[stats:decompose]{stats::decompose()}} |
| 81 | 81 | object with varying degrees of success. |
| 82 | 82 | |
| 83 | 83 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 84 | \strong{same number of rows} as the passed dataset. This means that the | |
| 85 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 86 | support matrix-columns. This means you should not specify a matrix | |
| 87 | of covariates in a model formula during the original model fitting | |
| 88 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 89 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 90 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 91 | model on data in a tibble. | |
| 84 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 85 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 86 | of a matrix of covariates, such as when the model formula uses | |
| 87 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 88 | as a matrix column. | |
| 92 | 89 | |
| 93 | 90 | We are in the process of defining behaviors for models fit with various |
| 94 | 91 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 67 | 67 | object with varying degrees of success. |
| 68 | 68 | |
| 69 | 69 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 70 | \strong{same number of rows} as the passed dataset. This means that the | |
| 71 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 72 | support matrix-columns. This means you should not specify a matrix | |
| 73 | of covariates in a model formula during the original model fitting | |
| 74 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 75 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 76 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 77 | model on data in a tibble. | |
| 70 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 71 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 72 | of a matrix of covariates, such as when the model formula uses | |
| 73 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 74 | as a matrix column. | |
| 78 | 75 | |
| 79 | 76 | We are in the process of defining behaviors for models fit with various |
| 80 | 77 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 57 | 57 | object with varying degrees of success. |
| 58 | 58 | |
| 59 | 59 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 60 | \strong{same number of rows} as the passed dataset. This means that the | |
| 61 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 62 | support matrix-columns. This means you should not specify a matrix | |
| 63 | of covariates in a model formula during the original model fitting | |
| 64 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 65 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 66 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 67 | model on data in a tibble. | |
| 60 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 61 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 62 | of a matrix of covariates, such as when the model formula uses | |
| 63 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 64 | as a matrix column. | |
| 68 | 65 | |
| 69 | 66 | We are in the process of defining behaviors for models fit with various |
| 70 | 67 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 68 | 68 | object with varying degrees of success. |
| 69 | 69 | |
| 70 | 70 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 71 | \strong{same number of rows} as the passed dataset. This means that the | |
| 72 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 73 | support matrix-columns. This means you should not specify a matrix | |
| 74 | of covariates in a model formula during the original model fitting | |
| 75 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 76 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 77 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 78 | model on data in a tibble. | |
| 71 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 72 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 73 | of a matrix of covariates, such as when the model formula uses | |
| 74 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 75 | as a matrix column. | |
| 79 | 76 | |
| 80 | 77 | We are in the process of defining behaviors for models fit with various |
| 81 | 78 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 79 | 79 | object with varying degrees of success. |
| 80 | 80 | |
| 81 | 81 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 82 | \strong{same number of rows} as the passed dataset. This means that the | |
| 83 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 84 | support matrix-columns. This means you should not specify a matrix | |
| 85 | of covariates in a model formula during the original model fitting | |
| 86 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 87 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 88 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 89 | model on data in a tibble. | |
| 82 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 83 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 84 | of a matrix of covariates, such as when the model formula uses | |
| 85 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 86 | as a matrix column. | |
| 90 | 87 | |
| 91 | 88 | We are in the process of defining behaviors for models fit with various |
| 92 | 89 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 80 | 80 | object with varying degrees of success. |
| 81 | 81 | |
| 82 | 82 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 83 | \strong{same number of rows} as the passed dataset. This means that the | |
| 84 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 85 | support matrix-columns. This means you should not specify a matrix | |
| 86 | of covariates in a model formula during the original model fitting | |
| 87 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 88 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 89 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 90 | model on data in a tibble. | |
| 83 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 84 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 85 | of a matrix of covariates, such as when the model formula uses | |
| 86 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 87 | as a matrix column. | |
| 91 | 88 | |
| 92 | 89 | We are in the process of defining behaviors for models fit with various |
| 93 | 90 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 38 | 38 | object with varying degrees of success. |
| 39 | 39 | |
| 40 | 40 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 41 | \strong{same number of rows} as the passed dataset. This means that the | |
| 42 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 43 | support matrix-columns. This means you should not specify a matrix | |
| 44 | of covariates in a model formula during the original model fitting | |
| 45 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 46 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 47 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 48 | model on data in a tibble. | |
| 41 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 42 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 43 | of a matrix of covariates, such as when the model formula uses | |
| 44 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 45 | as a matrix column. | |
| 49 | 46 | |
| 50 | 47 | We are in the process of defining behaviors for models fit with various |
| 51 | 48 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 50 | 50 | object with varying degrees of success. |
| 51 | 51 | |
| 52 | 52 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 53 | \strong{same number of rows} as the passed dataset. This means that the | |
| 54 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 55 | support matrix-columns. This means you should not specify a matrix | |
| 56 | of covariates in a model formula during the original model fitting | |
| 57 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 58 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 59 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 60 | model on data in a tibble. | |
| 53 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 54 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 55 | of a matrix of covariates, such as when the model formula uses | |
| 56 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 57 | as a matrix column. | |
| 61 | 58 | |
| 62 | 59 | We are in the process of defining behaviors for models fit with various |
| 63 | 60 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 62 | 62 | object with varying degrees of success. |
| 63 | 63 | |
| 64 | 64 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 65 | \strong{same number of rows} as the passed dataset. This means that the | |
| 66 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 67 | support matrix-columns. This means you should not specify a matrix | |
| 68 | of covariates in a model formula during the original model fitting | |
| 69 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 70 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 71 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 72 | model on data in a tibble. | |
| 65 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 66 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 67 | of a matrix of covariates, such as when the model formula uses | |
| 68 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 69 | as a matrix column. | |
| 73 | 70 | |
| 74 | 71 | We are in the process of defining behaviors for models fit with various |
| 75 | 72 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 57 | 57 | object with varying degrees of success. |
| 58 | 58 | |
| 59 | 59 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 60 | \strong{same number of rows} as the passed dataset. This means that the | |
| 61 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 62 | support matrix-columns. This means you should not specify a matrix | |
| 63 | of covariates in a model formula during the original model fitting | |
| 64 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 65 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 66 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 67 | model on data in a tibble. | |
| 60 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 61 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 62 | of a matrix of covariates, such as when the model formula uses | |
| 63 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 64 | as a matrix column. | |
| 68 | 65 | |
| 69 | 66 | We are in the process of defining behaviors for models fit with various |
| 70 | 67 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 77 | 77 | object with varying degrees of success. |
| 78 | 78 | |
| 79 | 79 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 80 | \strong{same number of rows} as the passed dataset. This means that the | |
| 81 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 82 | support matrix-columns. This means you should not specify a matrix | |
| 83 | of covariates in a model formula during the original model fitting | |
| 84 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 85 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 86 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 87 | model on data in a tibble. | |
| 80 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 81 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 82 | of a matrix of covariates, such as when the model formula uses | |
| 83 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 84 | as a matrix column. | |
| 88 | 85 | |
| 89 | 86 | We are in the process of defining behaviors for models fit with various |
| 90 | 87 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 107 | 104 | \code{.se.fit} columns. |
| 108 | 105 | } |
| 109 | 106 | \examples{ |
| 107 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 110 | 108 | |
| 111 | 109 | library(ggplot2) |
| 112 | 110 | library(dplyr) |
| 180 | 178 | result <- lm(b ~ a) |
| 181 | 179 | |
| 182 | 180 | tidy(result) |
| 183 | ||
| 181 | \dontshow{\}) # examplesIf} | |
| 184 | 182 | } |
| 185 | 183 | \seealso{ |
| 186 | 184 | \link[stats:na.action]{stats::na.action} |
| 62 | 62 | object with varying degrees of success. |
| 63 | 63 | |
| 64 | 64 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 65 | \strong{same number of rows} as the passed dataset. This means that the | |
| 66 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 67 | support matrix-columns. This means you should not specify a matrix | |
| 68 | of covariates in a model formula during the original model fitting | |
| 69 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 70 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 71 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 72 | model on data in a tibble. | |
| 65 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 66 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 67 | of a matrix of covariates, such as when the model formula uses | |
| 68 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 69 | as a matrix column. | |
| 73 | 70 | |
| 74 | 71 | We are in the process of defining behaviors for models fit with various |
| 75 | 72 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 125 | 125 | object with varying degrees of success. |
| 126 | 126 | |
| 127 | 127 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 128 | \strong{same number of rows} as the passed dataset. This means that the | |
| 129 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 130 | support matrix-columns. This means you should not specify a matrix | |
| 131 | of covariates in a model formula during the original model fitting | |
| 132 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 133 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 134 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 135 | model on data in a tibble. | |
| 128 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 129 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 130 | of a matrix of covariates, such as when the model formula uses | |
| 131 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 132 | as a matrix column. | |
| 136 | 133 | |
| 137 | 134 | We are in the process of defining behaviors for models fit with various |
| 138 | 135 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 69 | 69 | object with varying degrees of success. |
| 70 | 70 | |
| 71 | 71 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 72 | \strong{same number of rows} as the passed dataset. This means that the | |
| 73 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 74 | support matrix-columns. This means you should not specify a matrix | |
| 75 | of covariates in a model formula during the original model fitting | |
| 76 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 77 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 78 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 79 | model on data in a tibble. | |
| 72 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 73 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 74 | of a matrix of covariates, such as when the model formula uses | |
| 75 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 76 | as a matrix column. | |
| 80 | 77 | |
| 81 | 78 | We are in the process of defining behaviors for models fit with various |
| 82 | 79 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 51 | 51 | object with varying degrees of success. |
| 52 | 52 | |
| 53 | 53 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 54 | \strong{same number of rows} as the passed dataset. This means that the | |
| 55 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 56 | support matrix-columns. This means you should not specify a matrix | |
| 57 | of covariates in a model formula during the original model fitting | |
| 58 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 59 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 60 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 61 | model on data in a tibble. | |
| 54 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 55 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 56 | of a matrix of covariates, such as when the model formula uses | |
| 57 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 58 | as a matrix column. | |
| 62 | 59 | |
| 63 | 60 | We are in the process of defining behaviors for models fit with various |
| 64 | 61 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 43 | 43 | specify which components to return. |
| 44 | 44 | } |
| 45 | 45 | \examples{ |
| 46 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 47 | |
| 47 | 48 | # fit model |
| 48 | 49 | n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) |
| 62 | 63 | newdata$wt <- newdata$wt + 1 |
| 63 | 64 | |
| 64 | 65 | augment(n, newdata = newdata) |
| 65 | ||
| 66 | \dontshow{\}) # examplesIf} | |
| 66 | 67 | } |
| 67 | 68 | \seealso{ |
| 68 | 69 | \code{\link[=augment]{augment()}}, \code{\link[quantreg:nlrq]{quantreg::nlrq()}} |
| 62 | 62 | object with varying degrees of success. |
| 63 | 63 | |
| 64 | 64 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 65 | \strong{same number of rows} as the passed dataset. This means that the | |
| 66 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 67 | support matrix-columns. This means you should not specify a matrix | |
| 68 | of covariates in a model formula during the original model fitting | |
| 69 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 70 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 71 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 72 | model on data in a tibble. | |
| 65 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 66 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 67 | of a matrix of covariates, such as when the model formula uses | |
| 68 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 69 | as a matrix column. | |
| 73 | 70 | |
| 74 | 71 | We are in the process of defining behaviors for models fit with various |
| 75 | 72 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 80 | 77 | a lack of support in stats::predict.nls(). |
| 81 | 78 | } |
| 82 | 79 | \examples{ |
| 80 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 83 | 81 | |
| 84 | 82 | # fit model |
| 85 | 83 | n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) |
| 99 | 97 | newdata$wt <- newdata$wt + 1 |
| 100 | 98 | |
| 101 | 99 | augment(n, newdata = newdata) |
| 102 | ||
| 100 | \dontshow{\}) # examplesIf} | |
| 103 | 101 | } |
| 104 | 102 | \seealso{ |
| 105 | 103 | \link{tidy}, \code{\link[stats:nls]{stats::nls()}}, \code{\link[stats:predict.nls]{stats::predict.nls()}} |
| 57 | 57 | object with varying degrees of success. |
| 58 | 58 | |
| 59 | 59 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 60 | \strong{same number of rows} as the passed dataset. This means that the | |
| 61 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 62 | support matrix-columns. This means you should not specify a matrix | |
| 63 | of covariates in a model formula during the original model fitting | |
| 64 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 65 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 66 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 67 | model on data in a tibble. | |
| 60 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 61 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 62 | of a matrix of covariates, such as when the model formula uses | |
| 63 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 64 | as a matrix column. | |
| 68 | 65 | |
| 69 | 66 | We are in the process of defining behaviors for models fit with various |
| 70 | 67 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 71 | 68 | missing at this time. |
| 72 | 69 | } |
| 73 | 70 | \examples{ |
| 74 | \dontshow{if ((rlang::is_installed("cluster") & rlang::is_installed("modeldata") && identical(Sys.getenv("NOT_CRAN"), "true"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 71 | \dontshow{if ((rlang::is_installed(c("cluster", "modeldata", "ggplot2")) && identical(Sys.getenv("NOT_CRAN"), "true"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 75 | 72 | |
| 76 | 73 | # load libraries for models and data |
| 77 | 74 | library(dplyr) |
| 57 | 57 | object with varying degrees of success. |
| 58 | 58 | |
| 59 | 59 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 60 | \strong{same number of rows} as the passed dataset. This means that the | |
| 61 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 62 | support matrix-columns. This means you should not specify a matrix | |
| 63 | of covariates in a model formula during the original model fitting | |
| 64 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 65 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 66 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 67 | model on data in a tibble. | |
| 60 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 61 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 62 | of a matrix of covariates, such as when the model formula uses | |
| 63 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 64 | as a matrix column. | |
| 68 | 65 | |
| 69 | 66 | We are in the process of defining behaviors for models fit with various |
| 70 | 67 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 57 | 57 | object with varying degrees of success. |
| 58 | 58 | |
| 59 | 59 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 60 | \strong{same number of rows} as the passed dataset. This means that the | |
| 61 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 62 | support matrix-columns. This means you should not specify a matrix | |
| 63 | of covariates in a model formula during the original model fitting | |
| 64 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 65 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 66 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 67 | model on data in a tibble. | |
| 60 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 61 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 62 | of a matrix of covariates, such as when the model formula uses | |
| 63 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 64 | as a matrix column. | |
| 68 | 65 | |
| 69 | 66 | We are in the process of defining behaviors for models fit with various |
| 70 | 67 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 82 | 79 | included in the augmented output. |
| 83 | 80 | } |
| 84 | 81 | \examples{ |
| 85 | \dontshow{if (rlang::is_installed("poLCA")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 82 | \dontshow{if (rlang::is_installed(c("poLCA", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 86 | 83 | |
| 87 | 84 | # load libraries for models and data |
| 88 | 85 | library(poLCA) |
| 72 | 72 | object with varying degrees of success. |
| 73 | 73 | |
| 74 | 74 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 75 | \strong{same number of rows} as the passed dataset. This means that the | |
| 76 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 77 | support matrix-columns. This means you should not specify a matrix | |
| 78 | of covariates in a model formula during the original model fitting | |
| 79 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 80 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 81 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 82 | model on data in a tibble. | |
| 75 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 76 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 77 | of a matrix of covariates, such as when the model formula uses | |
| 78 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 79 | as a matrix column. | |
| 83 | 80 | |
| 84 | 81 | We are in the process of defining behaviors for models fit with various |
| 85 | 82 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 67 | 67 | object with varying degrees of success. |
| 68 | 68 | |
| 69 | 69 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 70 | \strong{same number of rows} as the passed dataset. This means that the | |
| 71 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 72 | support matrix-columns. This means you should not specify a matrix | |
| 73 | of covariates in a model formula during the original model fitting | |
| 74 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 75 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 76 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 77 | model on data in a tibble. | |
| 70 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 71 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 72 | of a matrix of covariates, such as when the model formula uses | |
| 73 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 74 | as a matrix column. | |
| 78 | 75 | |
| 79 | 76 | We are in the process of defining behaviors for models fit with various |
| 80 | 77 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 66 | 66 | object with varying degrees of success. |
| 67 | 67 | |
| 68 | 68 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 69 | \strong{same number of rows} as the passed dataset. This means that the | |
| 70 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 71 | support matrix-columns. This means you should not specify a matrix | |
| 72 | of covariates in a model formula during the original model fitting | |
| 73 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 74 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 75 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 76 | model on data in a tibble. | |
| 69 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 70 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 71 | of a matrix of covariates, such as when the model formula uses | |
| 72 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 73 | as a matrix column. | |
| 77 | 74 | |
| 78 | 75 | We are in the process of defining behaviors for models fit with various |
| 79 | 76 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 57 | 57 | object with varying degrees of success. |
| 58 | 58 | |
| 59 | 59 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 60 | \strong{same number of rows} as the passed dataset. This means that the | |
| 61 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 62 | support matrix-columns. This means you should not specify a matrix | |
| 63 | of covariates in a model formula during the original model fitting | |
| 64 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 65 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 66 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 67 | model on data in a tibble. | |
| 60 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 61 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 62 | of a matrix of covariates, such as when the model formula uses | |
| 63 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 64 | as a matrix column. | |
| 68 | 65 | |
| 69 | 66 | We are in the process of defining behaviors for models fit with various |
| 70 | 67 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 84 | 84 | object with varying degrees of success. |
| 85 | 85 | |
| 86 | 86 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 87 | \strong{same number of rows} as the passed dataset. This means that the | |
| 88 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 89 | support matrix-columns. This means you should not specify a matrix | |
| 90 | of covariates in a model formula during the original model fitting | |
| 91 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 92 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 93 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 94 | model on data in a tibble. | |
| 87 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 88 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 89 | of a matrix of covariates, such as when the model formula uses | |
| 90 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 91 | as a matrix column. | |
| 95 | 92 | |
| 96 | 93 | We are in the process of defining behaviors for models fit with various |
| 97 | 94 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 66 | 66 | object with varying degrees of success. |
| 67 | 67 | |
| 68 | 68 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 69 | \strong{same number of rows} as the passed dataset. This means that the | |
| 70 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 71 | support matrix-columns. This means you should not specify a matrix | |
| 72 | of covariates in a model formula during the original model fitting | |
| 73 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 74 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 75 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 76 | model on data in a tibble. | |
| 69 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 70 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 71 | of a matrix of covariates, such as when the model formula uses | |
| 72 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 73 | as a matrix column. | |
| 77 | 74 | |
| 78 | 75 | We are in the process of defining behaviors for models fit with various |
| 79 | 76 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 74 | 74 | object with varying degrees of success. |
| 75 | 75 | |
| 76 | 76 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 77 | \strong{same number of rows} as the passed dataset. This means that the | |
| 78 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 79 | support matrix-columns. This means you should not specify a matrix | |
| 80 | of covariates in a model formula during the original model fitting | |
| 81 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 82 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 83 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 84 | model on data in a tibble. | |
| 77 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 78 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 79 | of a matrix of covariates, such as when the model formula uses | |
| 80 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 81 | as a matrix column. | |
| 85 | 82 | |
| 86 | 83 | We are in the process of defining behaviors for models fit with various |
| 87 | 84 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 74 | 74 | object with varying degrees of success. |
| 75 | 75 | |
| 76 | 76 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 77 | \strong{same number of rows} as the passed dataset. This means that the | |
| 78 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 79 | support matrix-columns. This means you should not specify a matrix | |
| 80 | of covariates in a model formula during the original model fitting | |
| 81 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 82 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 83 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 84 | model on data in a tibble. | |
| 77 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 78 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 79 | of a matrix of covariates, such as when the model formula uses | |
| 80 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 81 | as a matrix column. | |
| 85 | 82 | |
| 86 | 83 | We are in the process of defining behaviors for models fit with various |
| 87 | 84 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 53 | 53 | object with varying degrees of success. |
| 54 | 54 | |
| 55 | 55 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 56 | \strong{same number of rows} as the passed dataset. This means that the | |
| 57 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 58 | support matrix-columns. This means you should not specify a matrix | |
| 59 | of covariates in a model formula during the original model fitting | |
| 60 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 61 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 62 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 63 | model on data in a tibble. | |
| 56 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 57 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 58 | of a matrix of covariates, such as when the model formula uses | |
| 59 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 60 | as a matrix column. | |
| 64 | 61 | |
| 65 | 62 | We are in the process of defining behaviors for models fit with various |
| 66 | 63 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 39 | 39 | specify which components to return. |
| 40 | 40 | } |
| 41 | 41 | \examples{ |
| 42 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 42 | 43 | |
| 43 | 44 | # fit model |
| 44 | 45 | spl <- smooth.spline(mtcars$wt, mtcars$mpg, df = 4) |
| 54 | 55 | geom_point() + |
| 55 | 56 | geom_line(aes(y = .fitted)) |
| 56 | 57 | |
| 58 | \dontshow{\}) # examplesIf} | |
| 57 | 59 | } |
| 58 | 60 | \seealso{ |
| 59 | 61 | \code{\link[=augment]{augment()}}, \code{\link[stats:smooth.spline]{stats::smooth.spline()}}, |
| 62 | 62 | object with varying degrees of success. |
| 63 | 63 | |
| 64 | 64 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 65 | \strong{same number of rows} as the passed dataset. This means that the | |
| 66 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 67 | support matrix-columns. This means you should not specify a matrix | |
| 68 | of covariates in a model formula during the original model fitting | |
| 69 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 70 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 71 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 72 | model on data in a tibble. | |
| 65 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 66 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 67 | of a matrix of covariates, such as when the model formula uses | |
| 68 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 69 | as a matrix column. | |
| 73 | 70 | |
| 74 | 71 | We are in the process of defining behaviors for models fit with various |
| 75 | 72 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 66 | 66 | object with varying degrees of success. |
| 67 | 67 | |
| 68 | 68 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 69 | \strong{same number of rows} as the passed dataset. This means that the | |
| 70 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 71 | support matrix-columns. This means you should not specify a matrix | |
| 72 | of covariates in a model formula during the original model fitting | |
| 73 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 74 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 75 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 76 | model on data in a tibble. | |
| 69 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 70 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 71 | of a matrix of covariates, such as when the model formula uses | |
| 72 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 73 | as a matrix column. | |
| 77 | 74 | |
| 78 | 75 | We are in the process of defining behaviors for models fit with various |
| 79 | 76 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 5 | 5 | \usage{ |
| 6 | 6 | \method{augment}{survreg}( |
| 7 | 7 | x, |
| 8 | data = NULL, | |
| 8 | data = model.frame(x), | |
| 9 | 9 | newdata = NULL, |
| 10 | 10 | type.predict = "response", |
| 11 | 11 | type.residuals = "response", |
| 79 | 79 | object with varying degrees of success. |
| 80 | 80 | |
| 81 | 81 | The augmented dataset is always returned as a \link[tibble:tibble]{tibble::tibble} with the |
| 82 | \strong{same number of rows} as the passed dataset. This means that the | |
| 83 | passed data must be coercible to a tibble. At this time, tibbles do not | |
| 84 | support matrix-columns. This means you should not specify a matrix | |
| 85 | of covariates in a model formula during the original model fitting | |
| 86 | process, and that \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}} and | |
| 87 | \code{\link[survival:Surv]{survival::Surv()}} objects are not supported in input data. If you | |
| 88 | encounter errors, try explicitly passing a tibble, or fitting the original | |
| 89 | model on data in a tibble. | |
| 82 | \strong{same number of rows} as the passed dataset. This means that the passed | |
| 83 | data must be coercible to a tibble. If a predictor enters the model as part | |
| 84 | of a matrix of covariates, such as when the model formula uses | |
| 85 | \code{\link[splines:ns]{splines::ns()}}, \code{\link[stats:poly]{stats::poly()}}, or \code{\link[survival:Surv]{survival::Surv()}}, it is represented | |
| 86 | as a matrix column. | |
| 90 | 87 | |
| 91 | 88 | We are in the process of defining behaviors for models fit with various |
| 92 | 89 | \code{na.action} arguments, but make no guarantees about behavior when data is |
| 93 | 90 | missing at this time. |
| 94 | 91 | } |
| 95 | 92 | \examples{ |
| 96 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 93 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 97 | 94 | |
| 98 | 95 | # load libraries for models and data |
| 99 | 96 | library(survival) |
| 121 | 121 | \item Sergio Oller \email{sergioller@gmail.com} [contributor] |
| 122 | 122 | \item Luke Sonnet \email{luke.sonnet@gmail.com} [contributor] |
| 123 | 123 | \item Jim Hester \email{jim.hester@rstudio.com} [contributor] |
| 124 | \item Cory Brunson \email{cornelioid@gmail.com} [contributor] | |
| 125 | 124 | \item Ben Schneider \email{benjamin.julius.schneider@gmail.com} [contributor] |
| 126 | 125 | \item Bernie Gray \email{bfgray3@gmail.com} (\href{https://orcid.org/0000-0001-9190-6032}{ORCID}) [contributor] |
| 127 | 126 | \item Mara Averick \email{mara@rstudio.com} [contributor] |
| 67 | 67 | throw an error. |
| 68 | 68 | } |
| 69 | 69 | \examples{ |
| 70 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 70 | 71 | |
| 71 | 72 | td <- tidy(mtcars) |
| 72 | 73 | td |
| 78 | 79 | ggplot(td, aes(mean, sd)) + geom_point() + |
| 79 | 80 | geom_text(aes(label = column), hjust = 1, vjust = 1) + |
| 80 | 81 | scale_x_log10() + scale_y_log10() + geom_abline() |
| 81 | ||
| 82 | \dontshow{\}) # examplesIf} | |
| 82 | 83 | } |
| 83 | 84 | \seealso{ |
| 84 | 85 | Other deprecated: |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("binGroup")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("binGroup", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(binGroup) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(survival) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(survival) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("glmnet")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("glmnet", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(glmnet) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("glmnet")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("glmnet", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(glmnet) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("gmm")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("gmm", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(gmm) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | 46 | |
| 46 | 47 | library(ggplot2) |
| 47 | 48 | library(dplyr) |
| 115 | 116 | result <- lm(b ~ a) |
| 116 | 117 | |
| 117 | 118 | tidy(result) |
| 118 | ||
| 119 | \dontshow{\}) # examplesIf} | |
| 119 | 120 | } |
| 120 | 121 | \seealso{ |
| 121 | 122 | \code{\link[=glance]{glance()}}, \code{\link[=glance.summary.lm]{glance.summary.lm()}} |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("lmodel2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("lmodel2", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(lmodel2) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | 46 | |
| 46 | 47 | # fit model |
| 47 | 48 | n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) |
| 61 | 62 | newdata$wt <- newdata$wt + 1 |
| 62 | 63 | |
| 63 | 64 | augment(n, newdata = newdata) |
| 64 | ||
| 65 | \dontshow{\}) # examplesIf} | |
| 65 | 66 | } |
| 66 | 67 | \seealso{ |
| 67 | 68 | \link{tidy}, \code{\link[stats:nls]{stats::nls()}} |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if ((rlang::is_installed("cluster") & rlang::is_installed("modeldata") && identical(Sys.getenv("NOT_CRAN"), "true"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if ((rlang::is_installed(c("cluster", "modeldata", "ggplot2")) && identical(Sys.getenv("NOT_CRAN"), "true"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(dplyr) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("poLCA")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("poLCA", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(poLCA) |
| 46 | 46 | returned rather than printed. |
| 47 | 47 | } |
| 48 | 48 | \examples{ |
| 49 | \dontshow{if (rlang::is_installed("MASS")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 49 | \dontshow{if (rlang::is_installed(c("MASS", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 50 | 50 | |
| 51 | 51 | # load libraries for models and data |
| 52 | 52 | library(MASS) |
| 30 | 30 | specify which components to return. |
| 31 | 31 | } |
| 32 | 32 | \examples{ |
| 33 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 33 | 34 | |
| 34 | 35 | # fit model |
| 35 | 36 | spl <- smooth.spline(mtcars$wt, mtcars$mpg, df = 4) |
| 45 | 46 | geom_point() + |
| 46 | 47 | geom_line(aes(y = .fitted)) |
| 47 | 48 | |
| 49 | \dontshow{\}) # examplesIf} | |
| 48 | 50 | } |
| 49 | 51 | \seealso{ |
| 50 | 52 | \code{\link[=augment]{augment()}}, \code{\link[stats:smooth.spline]{stats::smooth.spline()}} |
| 49 | 49 | non-summary method (e.g. AIC and BIC will be missing.) |
| 50 | 50 | } |
| 51 | 51 | \examples{ |
| 52 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 52 | 53 | |
| 53 | 54 | library(ggplot2) |
| 54 | 55 | library(dplyr) |
| 122 | 123 | result <- lm(b ~ a) |
| 123 | 124 | |
| 124 | 125 | tidy(result) |
| 125 | ||
| 126 | \dontshow{\}) # examplesIf} | |
| 126 | 127 | } |
| 127 | 128 | \seealso{ |
| 128 | 129 | \code{\link[=glance]{glance()}}, \code{\link[=glance.summary.lm]{glance.summary.lm()}} |
| 32 | 32 | of the appropriate type. |
| 33 | 33 | } |
| 34 | 34 | \examples{ |
| 35 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 36 | 36 | |
| 37 | 37 | # load libraries for models and data |
| 38 | 38 | library(survival) |
| 42 | 42 | of the appropriate type. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(survival) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | ||
| 35 | if (requireNamespace("binGroup", quietly = TRUE)) { | |
| 34 | \dontshow{if (rlang::is_installed(c("binGroup", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 36 | 35 | |
| 37 | 36 | library(binGroup) |
| 38 | 37 | des <- binDesign( |
| 48 | 47 | ggplot(tidy(des), aes(n, power)) + |
| 49 | 48 | geom_line() |
| 50 | 49 | |
| 51 | } | |
| 52 | ||
| 50 | \dontshow{\}) # examplesIf} | |
| 53 | 51 | } |
| 54 | 52 | \seealso{ |
| 55 | 53 | \code{\link[=tidy]{tidy()}}, \code{\link[binGroup:binDesign]{binGroup::binDesign()}} |
| 33 | 33 | specify which components to return. |
| 34 | 34 | } |
| 35 | 35 | \examples{ |
| 36 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 36 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 37 | 37 | |
| 38 | 38 | # load libraries for models and data |
| 39 | 39 | library(survival) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("multcomp")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | \dontshow{if (rlang::is_installed(c("multcomp", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | 35 | |
| 36 | 36 | # load libraries for models and data |
| 37 | 37 | library(multcomp) |
| 32 | 32 | specify which components to return. |
| 33 | 33 | } |
| 34 | 34 | \examples{ |
| 35 | \dontshow{if (rlang::is_installed("multcomp")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | \dontshow{if (rlang::is_installed(c("multcomp", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 36 | 36 | |
| 37 | 37 | # load libraries for models and data |
| 38 | 38 | library(multcomp) |
| 43 | 43 | specify which components to return. |
| 44 | 44 | } |
| 45 | 45 | \examples{ |
| 46 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 47 | 47 | |
| 48 | 48 | # load libraries for models and data |
| 49 | 49 | library(survival) |
| 30 | 30 | specify which components to return. |
| 31 | 31 | } |
| 32 | 32 | \examples{ |
| 33 | \dontshow{if (rlang::is_installed("glmnet")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 33 | \dontshow{if (rlang::is_installed(c("glmnet", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | 34 | |
| 35 | 35 | # load libraries for models and data |
| 36 | 36 | library(glmnet) |
| 36 | 36 | passed on to \code{\link[emmeans:summary.emmGrid]{emmeans::summary.emmGrid()}} or \code{\link[lsmeans:ref.grid]{lsmeans::summary.ref.grid()}}. |
| 37 | 37 | } |
| 38 | 38 | \examples{ |
| 39 | \dontshow{if (rlang::is_installed("emmeans")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 39 | \dontshow{if (rlang::is_installed(c("emmeans", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 40 | 40 | |
| 41 | 41 | # load libraries for models and data |
| 42 | 42 | library(emmeans) |
| 38 | 38 | specify which components to return. |
| 39 | 39 | } |
| 40 | 40 | \examples{ |
| 41 | \dontshow{if (rlang::is_installed("multcomp")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 41 | \dontshow{if (rlang::is_installed(c("multcomp", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 42 | 42 | |
| 43 | 43 | # load libraries for models and data |
| 44 | 44 | library(multcomp) |
| 45 | 45 | choice of lambda. |
| 46 | 46 | } |
| 47 | 47 | \examples{ |
| 48 | \dontshow{if (rlang::is_installed("glmnet")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 48 | \dontshow{if (rlang::is_installed(c("glmnet", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 49 | 49 | |
| 50 | 50 | # load libraries for models and data |
| 51 | 51 | library(glmnet) |
| 43 | 43 | specify which components to return. |
| 44 | 44 | } |
| 45 | 45 | \examples{ |
| 46 | \dontshow{if (rlang::is_installed("gmm")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | \dontshow{if (rlang::is_installed(c("gmm", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 47 | 47 | |
| 48 | 48 | # load libraries for models and data |
| 49 | 49 | library(gmm) |
| 37 | 37 | to \code{\link[psych:kappa]{psych::cohen.kappa()}} when creating the \code{kappa} object. |
| 38 | 38 | } |
| 39 | 39 | \examples{ |
| 40 | \dontshow{if (rlang::is_installed("psych")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 40 | \dontshow{if (rlang::is_installed(c("psych", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 41 | 41 | |
| 42 | 42 | # load libraries for models and data |
| 43 | 43 | library(psych) |
| 37 | 37 | on the output to return to a wide format. |
| 38 | 38 | } |
| 39 | 39 | \examples{ |
| 40 | \dontshow{if (rlang::is_installed("ks")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 40 | \dontshow{if (rlang::is_installed(c("ks", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 41 | 41 | |
| 42 | 42 | # load libraries for models and data |
| 43 | 43 | library(ks) |
| 47 | 47 | there is an additional column \code{response}. See \code{\link[=tidy.mlm]{tidy.mlm()}}. |
| 48 | 48 | } |
| 49 | 49 | \examples{ |
| 50 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 50 | 51 | |
| 51 | 52 | library(ggplot2) |
| 52 | 53 | library(dplyr) |
| 120 | 121 | result <- lm(b ~ a) |
| 121 | 122 | |
| 122 | 123 | tidy(result) |
| 123 | ||
| 124 | \dontshow{\}) # examplesIf} | |
| 124 | 125 | } |
| 125 | 126 | \seealso{ |
| 126 | 127 | \code{\link[=tidy]{tidy()}}, \code{\link[stats:summary.lm]{stats::summary.lm()}} |
| 42 | 42 | \code{vignette("mod2user", package = "lmodel2")}. |
| 43 | 43 | } |
| 44 | 44 | \examples{ |
| 45 | \dontshow{if (rlang::is_installed("lmodel2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 45 | \dontshow{if (rlang::is_installed(c("lmodel2", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | 46 | |
| 47 | 47 | # load libraries for models and data |
| 48 | 48 | library(lmodel2) |
| 37 | 37 | passed on to \code{\link[emmeans:summary.emmGrid]{emmeans::summary.emmGrid()}} or \code{\link[lsmeans:ref.grid]{lsmeans::summary.ref.grid()}}. |
| 38 | 38 | } |
| 39 | 39 | \examples{ |
| 40 | \dontshow{if (rlang::is_installed("emmeans")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 40 | \dontshow{if (rlang::is_installed(c("emmeans", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 41 | 41 | |
| 42 | 42 | # load libraries for models and data |
| 43 | 43 | library(emmeans) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("maps")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | \dontshow{if (rlang::is_installed(c("maps", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | 35 | |
| 36 | 36 | # load libraries for models and data |
| 37 | 37 | library(maps) |
| 38 | 38 | specify which components to return. |
| 39 | 39 | } |
| 40 | 40 | \examples{ |
| 41 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 41 | 42 | |
| 42 | 43 | # fit model |
| 43 | 44 | n <- nls(mpg ~ k * e^wt, data = mtcars, start = list(k = 1, e = 2)) |
| 57 | 58 | newdata$wt <- newdata$wt + 1 |
| 58 | 59 | |
| 59 | 60 | augment(n, newdata = newdata) |
| 60 | ||
| 61 | \dontshow{\}) # examplesIf} | |
| 61 | 62 | } |
| 62 | 63 | \seealso{ |
| 63 | 64 | \link{tidy}, \code{\link[stats:nls]{stats::nls()}}, \code{\link[stats:summary.nls]{stats::summary.nls()}} |
| 37 | 37 | For examples, see the pam vignette. |
| 38 | 38 | } |
| 39 | 39 | \examples{ |
| 40 | \dontshow{if ((rlang::is_installed("cluster") & rlang::is_installed("modeldata") && identical(Sys.getenv("NOT_CRAN"), "true"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 40 | \dontshow{if ((rlang::is_installed(c("cluster", "modeldata", "ggplot2")) && identical(Sys.getenv("NOT_CRAN"), "true"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 41 | 41 | |
| 42 | 42 | # load libraries for models and data |
| 43 | 43 | library(dplyr) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("poLCA")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | \dontshow{if (rlang::is_installed(c("poLCA", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | 35 | |
| 36 | 36 | # load libraries for models and data |
| 37 | 37 | library(poLCA) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | 35 | |
| 35 | 36 | ptt <- power.t.test(n = 2:30, delta = 1) |
| 36 | 37 | tidy(ptt) |
| 39 | 40 | |
| 40 | 41 | ggplot(tidy(ptt), aes(n, power)) + |
| 41 | 42 | geom_line() |
| 43 | \dontshow{\}) # examplesIf} | |
| 42 | 44 | } |
| 43 | 45 | \seealso{ |
| 44 | 46 | \code{\link[stats:power.t.test]{stats::power.t.test()}} |
| 82 | 82 | that SVD is only equivalent to PCA on centered data. |
| 83 | 83 | } |
| 84 | 84 | \examples{ |
| 85 | \dontshow{if (rlang::is_installed("maps")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 85 | \dontshow{if (rlang::is_installed(c("maps", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 86 | 86 | |
| 87 | 87 | pc <- prcomp(USArrests, scale = TRUE) |
| 88 | 88 |
| 43 | 43 | output. |
| 44 | 44 | } |
| 45 | 45 | \examples{ |
| 46 | \dontshow{if (rlang::is_installed("Hmisc")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 46 | \dontshow{if (rlang::is_installed(c("Hmisc", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 47 | 47 | |
| 48 | 48 | # load libraries for models and data |
| 49 | 49 | library(Hmisc) |
| 36 | 36 | passed on to \code{\link[emmeans:summary.emmGrid]{emmeans::summary.emmGrid()}} or \code{\link[lsmeans:ref.grid]{lsmeans::summary.ref.grid()}}. |
| 37 | 37 | } |
| 38 | 38 | \examples{ |
| 39 | \dontshow{if (rlang::is_installed("emmeans")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 39 | \dontshow{if (rlang::is_installed(c("emmeans", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 40 | 40 | |
| 41 | 41 | # load libraries for models and data |
| 42 | 42 | library(emmeans) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("MASS")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | \dontshow{if (rlang::is_installed(c("MASS", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | 35 | |
| 36 | 36 | # load libraries for models and data |
| 37 | 37 | library(MASS) |
| 32 | 32 | specify which components to return. |
| 33 | 33 | } |
| 34 | 34 | \examples{ |
| 35 | \dontshow{if (rlang::is_installed("AUC")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | \dontshow{if (rlang::is_installed(c("AUC", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 36 | 36 | |
| 37 | 37 | # load libraries for models and data |
| 38 | 38 | library(AUC) |
| 30 | 30 | specify which components to return. |
| 31 | 31 | } |
| 32 | 32 | \examples{ |
| 33 | \dontshow{if (rlang::is_installed("ggplot2")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 33 | 34 | |
| 34 | 35 | spc <- spectrum(lh) |
| 35 | 36 | tidy(spc) |
| 37 | 38 | library(ggplot2) |
| 38 | 39 | ggplot(tidy(spc), aes(freq, spec)) + |
| 39 | 40 | geom_line() |
| 41 | \dontshow{\}) # examplesIf} | |
| 40 | 42 | } |
| 41 | 43 | \seealso{ |
| 42 | 44 | \code{\link[=tidy]{tidy()}}, \code{\link[stats:spectrum]{stats::spectrum()}} |
| 32 | 32 | specify which components to return. |
| 33 | 33 | } |
| 34 | 34 | \examples{ |
| 35 | \dontshow{if (rlang::is_installed("multcomp")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | \dontshow{if (rlang::is_installed(c("multcomp", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 36 | 36 | |
| 37 | 37 | # load libraries for models and data |
| 38 | 38 | library(multcomp) |
| 31 | 31 | passed on to \code{\link[emmeans:summary.emmGrid]{emmeans::summary.emmGrid()}} or \code{\link[lsmeans:ref.grid]{lsmeans::summary.ref.grid()}}. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("emmeans")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | \dontshow{if (rlang::is_installed(c("emmeans", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | 35 | |
| 36 | 36 | # load libraries for models and data |
| 37 | 37 | library(emmeans) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | 35 | |
| 36 | 36 | # load libraries for models and data |
| 37 | 37 | library(survival) |
| 38 | 38 | specify which components to return. |
| 39 | 39 | } |
| 40 | 40 | \examples{ |
| 41 | \dontshow{if (rlang::is_installed("survival")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 41 | \dontshow{if (rlang::is_installed(c("survival", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 42 | 42 | |
| 43 | 43 | # load libraries for models and data |
| 44 | 44 | library(survival) |
| 31 | 31 | specify which components to return. |
| 32 | 32 | } |
| 33 | 33 | \examples{ |
| 34 | \dontshow{if (rlang::is_installed("zoo")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 34 | \dontshow{if (rlang::is_installed(c("zoo", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 35 | 35 | |
| 36 | 36 | # load libraries for models and data |
| 37 | 37 | library(zoo) |
| 75 | 75 | A very thin wrapper around \code{\link[=tidy_svd]{tidy_svd()}}. |
| 76 | 76 | } |
| 77 | 77 | \examples{ |
| 78 | \dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 78 | \dontshow{if (rlang::is_installed(c("modeldata", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 79 | 79 | |
| 80 | 80 | library(modeldata) |
| 81 | 81 | data(hpc_data) |
| 88 | 88 | that SVD is only equivalent to PCA on centered data. |
| 89 | 89 | } |
| 90 | 90 | \examples{ |
| 91 | \dontshow{if (rlang::is_installed("modeldata")) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 91 | \dontshow{if (rlang::is_installed(c("modeldata", "ggplot2"))) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} | |
| 92 | 92 | |
| 93 | 93 | library(modeldata) |
| 94 | 94 | data(hpc_data) |
| 0 | context("test-aaa-documentation-helper") | |
| 1 | ||
| 2 | skip("documentation helper tests not yet written") | |
| 3 | ||
| 4 | test_that("warns user when column doesn't exist", { | |
| 5 | ||
| 6 | }) | |
| 7 | ||
| 8 | test_that("no named arguments", { | |
| 9 | ||
| 10 | }) | |
| 11 | ||
| 12 | test_that("only named arguments", { | |
| 13 | ||
| 14 | }) | |
| 15 | ||
| 16 | test_that("mix of named arguments and logical switches", { | |
| 17 | ||
| 18 | }) |
| 35 | 35 | }) |
| 36 | 36 | |
| 37 | 37 | test_that("augment.pam", { |
| 38 | check_augment_function( | |
| 39 | aug = augment.pam, | |
| 40 | model = fit, | |
| 41 | data = x, | |
| 42 | newdata = x | |
| 38 | suppressWarnings( | |
| 39 | check_augment_function( | |
| 40 | aug = augment.pam, | |
| 41 | model = fit, | |
| 42 | data = x, | |
| 43 | newdata = x | |
| 44 | ) | |
| 43 | 45 | ) |
| 44 | 46 | }) |
| 101 | 101 | }) |
| 102 | 102 | |
| 103 | 103 | test_that("augment.felm", { |
| 104 | check_augment_function( | |
| 105 | aug = augment.felm, | |
| 106 | model = fit, | |
| 107 | data = df | |
| 104 | suppressWarnings( | |
| 105 | check_augment_function( | |
| 106 | aug = augment.felm, | |
| 107 | model = fit, | |
| 108 | data = df | |
| 109 | ) | |
| 108 | 110 | ) |
| 109 | 111 | |
| 110 | check_augment_function( | |
| 111 | aug = augment.felm, | |
| 112 | model = fit2, | |
| 113 | data = df | |
| 112 | suppressWarnings( | |
| 113 | check_augment_function( | |
| 114 | aug = augment.felm, | |
| 115 | model = fit2, | |
| 116 | data = df | |
| 117 | ) | |
| 114 | 118 | ) |
| 115 | 119 | |
| 116 | check_augment_function( | |
| 117 | aug = augment.felm, | |
| 118 | model = fit_form, | |
| 119 | data = df | |
| 120 | suppressWarnings( | |
| 121 | check_augment_function( | |
| 122 | aug = augment.felm, | |
| 123 | model = fit_form, | |
| 124 | data = df | |
| 125 | ) | |
| 120 | 126 | ) |
| 127 | ||
| 121 | 128 | expect_error(augment(fit_multi), |
| 122 | 129 | "Augment does not support linear models with multiple responses.") |
| 123 | 130 |
| 56 | 56 | }) |
| 57 | 57 | |
| 58 | 58 | test_that("augment.Mclust", { |
| 59 | check_augment_function( | |
| 60 | aug = augment.Mclust, | |
| 61 | model = fit, | |
| 62 | data = dat, | |
| 63 | newdata = dat | |
| 59 | suppressWarnings( | |
| 60 | check_augment_function( | |
| 61 | aug = augment.Mclust, | |
| 62 | model = fit, | |
| 63 | data = dat, | |
| 64 | newdata = dat | |
| 65 | ) | |
| 64 | 66 | ) |
| 65 | 67 | |
| 66 | check_augment_function( | |
| 67 | aug = augment.Mclust, | |
| 68 | model = fit2, | |
| 69 | data = dat, | |
| 70 | newdata = dat | |
| 68 | suppressWarnings( | |
| 69 | check_augment_function( | |
| 70 | aug = augment.Mclust, | |
| 71 | model = fit2, | |
| 72 | data = dat, | |
| 73 | newdata = dat | |
| 74 | ) | |
| 71 | 75 | ) |
| 72 | 76 | |
| 73 | 77 | check_augment_function( |
| 68 | 68 | gl_logitmfx <- glance(fit_logitmfx) |
| 69 | 69 | check_glance_outputs(gl_logitmfx) |
| 70 | 70 | # negbin |
| 71 | gl_negbinmfx <- glance(fit_negbinmfx) | |
| 71 | suppressWarnings( | |
| 72 | gl_negbinmfx <- glance(fit_negbinmfx) | |
| 73 | ) | |
| 72 | 74 | check_glance_outputs(gl_negbinmfx) |
| 73 | 75 | # poisson |
| 74 | 76 | gl_poissonmfx <- glance(fit_poissonmfx) |
| 35 | 35 | }) |
| 36 | 36 | |
| 37 | 37 | test_that("augment.gam", { |
| 38 | check_augment_function( | |
| 39 | augment.gam, | |
| 40 | fit, | |
| 41 | data = ChickWeight, | |
| 42 | newdata = ChickWeight | |
| 38 | suppressWarnings( | |
| 39 | check_augment_function( | |
| 40 | augment.gam, | |
| 41 | fit, | |
| 42 | data = ChickWeight, | |
| 43 | newdata = ChickWeight | |
| 44 | ) | |
| 43 | 45 | ) |
| 44 | 46 | }) |
| 8 | 8 | skip_if_not_installed("survival") # does this skip with base R? |
| 9 | 9 | |
| 10 | 10 | library(muhaz) |
| 11 | data(ovarian, package = "survival") | |
| 11 | ||
| 12 | # load the ovarian data | |
| 13 | data(cancer, package = "survival") | |
| 12 | 14 | |
| 13 | 15 | fit <- muhaz(ovarian$futime, ovarian$fustat) |
| 14 | 16 |
| 45 | 45 | gl <- glance(fit) |
| 46 | 46 | check_glance_outputs(gl) |
| 47 | 47 | |
| 48 | gl_rd <- glance(fit_rd) | |
| 48 | suppressWarnings( | |
| 49 | gl_rd <- glance(fit_rd) | |
| 50 | ) | |
| 51 | ||
| 49 | 52 | check_glance_outputs(gl_rd) |
| 50 | 53 | }) |
| 51 | 54 |
| 49 | 49 | |
| 50 | 50 | |
| 51 | 51 | test_that("augment.glm", { |
| 52 | skip("come back to glm augment checks") | |
| 53 | ||
| 54 | 52 | check_augment_function( |
| 55 | 53 | aug = augment.glm, |
| 56 | 54 | model = gfit, |
| 64 | 62 | data = mtcars, |
| 65 | 63 | newdata = mtcars |
| 66 | 64 | ) |
| 67 | ||
| 68 | check_augment_function( | |
| 69 | aug = augment.glm, | |
| 70 | model = gfit3, | |
| 71 | data = mtcars, | |
| 72 | newdata = mtcars | |
| 73 | ) | |
| 74 | 65 | }) |
| 58 | 58 | }) |
| 59 | 59 | |
| 60 | 60 | test_that("augment.coxph", { |
| 61 | expect_error( | |
| 62 | augment(fit), | |
| 63 | regexp = "Must specify either `data` or `newdata` argument." | |
| 64 | ) | |
| 65 | ||
| 66 | 61 | check_augment_function( |
| 67 | 62 | aug = augment.coxph, |
| 68 | 63 | model = fit, |
| 37 | 37 | }) |
| 38 | 38 | |
| 39 | 39 | test_that("augment.survreg", { |
| 40 | expect_error( | |
| 41 | augment(sr), | |
| 42 | regexp = "Must specify either `data` or `newdata` argument." | |
| 43 | ) | |
| 44 | ||
| 45 | 40 | check_augment_function( |
| 46 | 41 | aug = augment.survreg, |
| 47 | 42 | model = sr, |
| 34 | 34 | ) |
| 35 | 35 | }) |
| 36 | 36 | |
| 37 | skip("specification not yet complete") | |
| 37 | test_that("augment_newdata can handle function calls in response term (lm)", { | |
| 38 | mt_lm <- lm(data = mtcars, mpg ~ hp) | |
| 39 | mt_lm_log <- lm(data = mtcars, log(mpg) ~ hp) | |
| 38 | 40 | |
| 39 | skip_if_not_installed("betareg") | |
| 40 | library(betareg) | |
| 41 | aug_mt_lm_none <- augment(mt_lm) | |
| 42 | aug_mt_lm_data <- augment(mt_lm, data = mtcars) | |
| 43 | aug_mt_lm_newdata <- augment(mt_lm, newdata = mtcars[1:20,]) | |
| 44 | aug_mt_lm_no_resp <- augment(mt_lm, newdata = mtcars[1:20, 2:ncol(mtcars)]) | |
| 45 | ||
| 46 | aug_mt_lm_log_none <- augment(mt_lm_log) | |
| 47 | aug_mt_lm_log_data <- augment(mt_lm_log, data = mtcars) | |
| 48 | aug_mt_lm_log_newdata <- augment(mt_lm_log, newdata = mtcars[1:20,]) | |
| 49 | aug_mt_lm_log_no_resp <- augment(mt_lm_log, newdata = mtcars[1:20, 2:ncol(mtcars)]) | |
| 50 | ||
| 51 | expect_true(inherits(aug_mt_lm_log_none, "tbl_df")) | |
| 52 | expect_true(inherits(aug_mt_lm_log_data, "tbl_df")) | |
| 53 | expect_true(inherits(aug_mt_lm_log_newdata, "tbl_df")) | |
| 54 | expect_true(inherits(aug_mt_lm_log_no_resp, "tbl_df")) | |
| 55 | ||
| 56 | expect_equal(".resid" %in% colnames(aug_mt_lm_log_none), ".resid" %in% colnames(aug_mt_lm_none)) | |
| 57 | expect_equal(".resid" %in% colnames(aug_mt_lm_log_data), ".resid" %in% colnames(aug_mt_lm_data)) | |
| 58 | expect_equal(".resid" %in% colnames(aug_mt_lm_log_newdata), ".resid" %in% colnames(aug_mt_lm_newdata)) | |
| 59 | expect_equal(".resid" %in% colnames(aug_mt_lm_log_no_resp), ".resid" %in% colnames(aug_mt_lm_no_resp)) | |
| 41 | 60 | |
| 42 | test_that("validate_augment_input", { | |
| 43 | data(GasolineYield) | |
| 44 | ||
| 45 | model <- lm(hp ~ ., mtcars) | |
| 46 | poly_m <- lm(hp ~ poly(mpg, 2), mtcars) # augment(poly_m) works | |
| 47 | ||
| 48 | # augment(m3) breaks. it's possible that it wouldn't with a better | |
| 49 | # implementation, in which case we'd want something here | |
| 50 | # where it's imperative to specify either data or newdata to | |
| 51 | # prevent an explosion | |
| 52 | ||
| 53 | m3 <- betareg(yield ~ poly(temp, 2), GasolineYield) | |
| 54 | ||
| 55 | expect_warning( | |
| 56 | validate_augment_input(model, data = mtcars, newdata = mtcars), | |
| 57 | regexp = "Both `data` and `newdata` have been specified. Ignoring `data`." | |
| 58 | ) | |
| 59 | ||
| 60 | expect_warning( | |
| 61 | validate_augment_input(poly_m, mtcars[, 1:3]), | |
| 62 | "`data` might not contain columns present in original data." | |
| 63 | ) | |
| 64 | ||
| 65 | extra_rows <- bind_rows(mtcars, head(mtcars)) | |
| 66 | ||
| 67 | expect_warning( | |
| 68 | validate_augment_input(model, extra_rows), | |
| 69 | regexp = paste( | |
| 70 | "`data` must contain all rows passed to the original modelling", | |
| 71 | "function with no extras rows." | |
| 72 | ) | |
| 73 | ) | |
| 74 | ||
| 75 | expect_message( | |
| 76 | validate_augment_input(m3), | |
| 77 | regexp = paste0( | |
| 78 | "Neither `data` nor `newdata` has been specified.\n", | |
| 79 | "Attempting to reconstruct original data." | |
| 80 | ) | |
| 81 | ) | |
| 82 | ||
| 83 | expect_error( | |
| 84 | validate_augment_input(m3, model.frame(m3)), | |
| 85 | regexp = paste0( | |
| 86 | "`data` is malformed: must be coercable to a tibble.\n", | |
| 87 | "Did you pass `data` the data originally used to fit your model?" | |
| 88 | ) | |
| 89 | ) | |
| 90 | ||
| 91 | expect_error( | |
| 92 | validate_augment_input(model, 1L), | |
| 93 | regexp = "`data` argument must be a tibble or dataframe." | |
| 94 | ) | |
| 95 | ||
| 96 | expect_error( | |
| 97 | validate_augment_input(model, newdata = 1L), | |
| 98 | regexp = "`newdata` argument must be a tibble or dataframe." | |
| 99 | ) | |
| 61 | expect_equal(aug_mt_lm_log_none$.resid, log(mtcars$mpg) - unname(fitted(mt_lm_log, mtcars))) | |
| 62 | expect_equal(aug_mt_lm_log_data$.resid, log(mtcars$mpg) - unname(fitted(mt_lm_log, mtcars))) | |
| 63 | expect_equal(aug_mt_lm_log_newdata$.resid, log(mtcars$mpg[1:20]) - unname(predict(mt_lm_log, mtcars[1:20,]))) | |
| 100 | 64 | }) |
| 101 | 65 | |
| 66 | test_that("augment_newdata can handle function calls in response term (glm)", { | |
| 67 | mt_glm <- glm(data = mtcars, mpg ~ .) | |
| 68 | mt_glm_log <- glm(data = mtcars, log(mpg) ~ .) | |
| 69 | ||
| 70 | aug_mt_glm_none <- augment(mt_glm) | |
| 71 | aug_mt_glm_data <- augment(mt_glm, data = mtcars) | |
| 72 | aug_mt_glm_newdata <- augment(mt_glm, newdata = mtcars[1:20,]) | |
| 73 | aug_mt_glm_no_resp <- augment(mt_glm, newdata = mtcars[1:20, 2:ncol(mtcars)]) | |
| 74 | ||
| 75 | aug_mt_glm_log_none <- augment(mt_glm_log) | |
| 76 | aug_mt_glm_log_data <- augment(mt_glm_log, data = mtcars) | |
| 77 | aug_mt_glm_log_newdata <- augment(mt_glm_log, newdata = mtcars[1:20,]) | |
| 78 | aug_mt_glm_log_no_resp <- augment(mt_glm_log, newdata = mtcars[1:20, 2:ncol(mtcars)]) | |
| 79 | ||
| 80 | expect_true(inherits(aug_mt_glm_log_none, "tbl_df")) | |
| 81 | expect_true(inherits(aug_mt_glm_log_data, "tbl_df")) | |
| 82 | expect_true(inherits(aug_mt_glm_log_newdata, "tbl_df")) | |
| 83 | expect_true(inherits(aug_mt_glm_log_no_resp, "tbl_df")) | |
| 84 | ||
| 85 | expect_equal(".resid" %in% colnames(aug_mt_glm_log_none), ".resid" %in% colnames(aug_mt_glm_none)) | |
| 86 | expect_equal(".resid" %in% colnames(aug_mt_glm_log_data), ".resid" %in% colnames(aug_mt_glm_data)) | |
| 87 | expect_equal(".resid" %in% colnames(aug_mt_glm_log_newdata), ".resid" %in% colnames(aug_mt_glm_newdata)) | |
| 88 | expect_equal(".resid" %in% colnames(aug_mt_glm_log_no_resp), ".resid" %in% colnames(aug_mt_glm_no_resp)) | |
| 89 | ||
| 90 | expect_equal(aug_mt_glm_log_none$.resid, log(mtcars$mpg) - unname(fitted(mt_glm_log, mtcars))) | |
| 91 | expect_equal(aug_mt_glm_log_data$.resid, log(mtcars$mpg) - unname(fitted(mt_glm_log, mtcars))) | |
| 92 | }) | |
| 93 | ||
| 94 | test_that("augment_newdata can handle function calls in response term (loess)", { | |
| 95 | mt_loess <- loess(data = mtcars, mpg ~ hp + disp) | |
| 96 | mt_loess_log <- loess(data = mtcars, log(mpg) ~ hp + disp) | |
| 97 | ||
| 98 | aug_mt_loess_none <- augment(mt_loess) | |
| 99 | aug_mt_loess_data <- augment(mt_loess, data = mtcars) | |
| 100 | aug_mt_loess_newdata <- augment(mt_loess, newdata = mtcars[1:20,]) | |
| 101 | aug_mt_loess_no_resp <- augment(mt_loess, newdata = mtcars[1:20, 2:ncol(mtcars)]) | |
| 102 | ||
| 103 | aug_mt_loess_log_none <- augment(mt_loess_log) | |
| 104 | aug_mt_loess_log_data <- augment(mt_loess_log, data = mtcars) | |
| 105 | aug_mt_loess_log_newdata <- augment(mt_loess_log, newdata = mtcars[1:20,]) | |
| 106 | aug_mt_loess_log_no_resp <- augment(mt_loess_log, newdata = mtcars[1:20, 2:ncol(mtcars)]) | |
| 107 | ||
| 108 | expect_true(inherits(aug_mt_loess_log_none, "tbl_df")) | |
| 109 | expect_true(inherits(aug_mt_loess_log_data, "tbl_df")) | |
| 110 | expect_true(inherits(aug_mt_loess_log_newdata, "tbl_df")) | |
| 111 | expect_true(inherits(aug_mt_loess_log_no_resp, "tbl_df")) | |
| 112 | ||
| 113 | expect_equal(".resid" %in% colnames(aug_mt_loess_log_none), ".resid" %in% colnames(aug_mt_loess_none)) | |
| 114 | expect_equal(".resid" %in% colnames(aug_mt_loess_log_data), ".resid" %in% colnames(aug_mt_loess_data)) | |
| 115 | expect_equal(".resid" %in% colnames(aug_mt_loess_log_newdata), ".resid" %in% colnames(aug_mt_loess_newdata)) | |
| 116 | expect_equal(".resid" %in% colnames(aug_mt_loess_log_no_resp), ".resid" %in% colnames(aug_mt_loess_no_resp)) | |
| 117 | ||
| 118 | expect_equal(aug_mt_loess_log_none$.resid, log(mtcars$mpg) - unname(fitted(mt_loess_log, mtcars))) | |
| 119 | expect_equal(aug_mt_loess_log_data$.resid, log(mtcars$mpg) - unname(fitted(mt_loess_log, mtcars))) | |
| 120 | expect_equal(aug_mt_loess_log_newdata$.resid, log(mtcars$mpg[1:20]) - unname(predict(mt_loess_log, mtcars[1:20,]))) | |
| 121 | }) | |
| 102 | 122 | |
| 103 | 123 | test_that("as_glance_tibble", { |
| 104 | ||
| 105 | 124 | df1 <- as_glance_tibble(x = 1, y = 1, na_types = "rr") |
| 106 | 125 | df2 <- as_glance_tibble(x = 1, y = NULL, na_types = "rc") |
| 107 | 126 | df3 <- as_glance_tibble(x = 1, y = NULL, na_types = "rr") |
| 108 | 127 | |
| 109 | 128 | expect_equal(purrr::map(df1, class), |
| 110 | purrr::map(df2, class)) | |
| 129 | purrr::map(df3, class)) | |
| 111 | 130 | |
| 112 | expect_true(class(df1$y) == class(df2$y)) | |
| 131 | expect_true(class(df1$y) == class(df3$y)) | |
| 113 | 132 | |
| 114 | expect_false(class(df2$y) == class(df3$y)) | |
| 133 | expect_false(class(df1$y) == class(df2$y)) | |
| 115 | 134 | |
| 116 | 135 | expect_error( |
| 117 | 136 | as_glance_tibble(x = 1, y = 1, na_types = "rrr") |
| 118 | 137 | ) |
| 119 | ||
| 120 | 138 | }) |
| 121 | 139 | |
| 122 | 140 | test_that("appropriate warning on (g)lm-subclassed models", { |