Package check result: NOTE Check: examples, Result: NOTE Examples with CPU (user + system) or elapsed time > 5s user system elapsed profile-methods 6.806 0.1 6.907 Changes to worse in reverse depends: Package: ALDEx3 Check: tests New result: ERROR Running ‘testthat.R’ [62s/70s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(ALDEx3) > > test_check("ALDEx3") Saving _problems/test-aldex-mem-46.R Saving _problems/test-aldex-mem-97.R Loading required package: rBeta2009 Attaching package: 'rBeta2009' The following object is masked from 'package:stats': rbeta Loading required package: lmtest Loading required package: zoo Attaching package: 'zoo' The following objects are masked from 'package:base': as.Date, as.Date.numeric Loading required package: sandwich [ FAIL 2 | WARN 1 | SKIP 0 | PASS 63 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-aldex-mem.R:41:3'): aldex mem lme4/nlme correct naming ───────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. └─ALDEx3::aldex(...) at test-aldex-mem.R:41:3 2. └─ALDEx3:::sr.mem(...) 3. └─base (local) lapply_func(...) 4. └─ALDEx3 (local) FUN(X[[i]], ...) 5. ├─base::suppressMessages(...) 6. │ └─base::withCallingHandlers(...) 7. └─lmerTest::lmer(update(formula, y ~ .), data = data_temp) 8. └─lmerTest:::as_lmerModLT(model, devfun) ── Error ('test-aldex-mem.R:91:3'): aldex mem lme4 correct results ───────────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. └─ALDEx3::aldex(...) at test-aldex-mem.R:91:3 2. └─ALDEx3:::sr.mem(...) 3. └─base (local) lapply_func(...) 4. └─ALDEx3 (local) FUN(X[[i]], ...) 5. ├─base::suppressMessages(...) 6. │ └─base::withCallingHandlers(...) 7. └─lmerTest::lmer(update(formula, y ~ .), data = data_temp) 8. └─lmerTest:::as_lmerModLT(model, devfun) [ FAIL 2 | WARN 1 | SKIP 0 | PASS 63 ] Error: ! Test failures. Execution halted Package: broom.mixed Check: tests New result: ERROR Running ‘test-all.R’ [117s/123s] Running the tests in ‘tests/test-all.R’ failed. Complete output: > Sys.setenv("R_TESTS" = "") > library(testthat) > test_check("broom.mixed") Loading required package: broom.mixed GAMLSS-RS iteration 1: Global Deviance = 4771.925 GAMLSS-CG iteration 1: Global Deviance = 4771.013 GAMLSS-CG iteration 2: Global Deviance = 4770.994 GAMLSS-CG iteration 3: Global Deviance = 4770.994 Saving _problems/test-lme4-213.R [ FAIL 1 | WARN 6 | SKIP 2 | PASS 287 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-rstanarm.R:36:5' • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-lme4.R:213:5'): conf intervals for ranef in correct order ──── Expected `cor_vals$conf.low > (-1) && cor_vals$conf.high < 1` to be TRUE. Differences: `actual`: FALSE `expected`: TRUE [ FAIL 1 | WARN 6 | SKIP 2 | PASS 287 ] Error: ! Test failures. Execution halted Package: buildmer Check: examples New result: ERROR Running examples in ‘buildmer-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: buildmer > ### Title: Use 'buildmer' to fit mixed-effects models using 'lmer'/'glmer' > ### from 'lme4' > ### Aliases: buildmer > > ### ** Examples > > library(buildmer) > model <- buildmer(Reaction ~ Days + (Days|Subject),lme4::sleepstudy) Determining predictor order Fitting via lm: Reaction ~ 1 Currently evaluating LRT for: Days Fitting via lm: Reaction ~ 1 + Days Updating formula: Reaction ~ 1 + Days Fitting via gam, with REML: Reaction ~ 1 + Days Currently evaluating LRT for: 1 | Subject Fitting via lmer, with REML: Reaction ~ 1 + Days + (1 | Subject) Updating formula: Reaction ~ 1 + Days + (1 | Subject) Currently evaluating LRT for: Days | Subject Fitting via lmer, with REML: Reaction ~ 1 + Days + (1 + Days | Subject) Updating formula: Reaction ~ 1 + Days + (1 + Days | Subject) Fitting ML and REML reference models Fitting via lmer, with REML: Reaction ~ 1 + Days + (1 + Days | Subject) Fitting via lmer, with ML: Reaction ~ 1 + Days + (1 + Days | Subject) Testing terms Fitting via lmer, with REML: Reaction ~ 1 + Days + (1 | Subject) grouping term block score Iteration LRT 1 1 NA NA 1 NA 1 NA 2 Days NA NA Days -32.67300 1 NA 3 Subject 1 NA Subject 1 -57.53561 1 NA 4 Subject Days NA Subject Days -21.99890 1 2.79253e-10 All terms are significant Finalizing by converting the model to lmerTest Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Error in `*tmp*`@call : no applicable method for `@` applied to an object of class "try-error" Calls: buildmer Execution halted Package: cocoon Check: examples New result: ERROR Running examples in ‘cocoon-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: format_stats > ### Title: Format statistical results > ### Aliases: format_stats > > ### ** Examples > > # Format cor.test() object > format_stats(cor.test(mtcars$mpg, mtcars$cyl)) [1] "_r_ = -.85, 95% CI [-0.93, -0.72], _p_ < .001" > > # Format correlation::correlation() object > format_stats(correlation::correlation(data = mtcars, select = "mpg", select2 = "cyl")) [1] "_r_ = -.85, 95% CI [-0.93, -0.72], _p_ < .001" > > # Format t.test() object > format_stats(t.test(mtcars$vs, mtcars$am)) [1] "_M_ = 0.0, 95% CI [-0.2, 0.3], _t_(62) = 0.2, _p_ = .804" > > # Format aov() object > format_stats(aov(mpg ~ cyl * hp, data = mtcars), term = "cyl") [1] "_F_(1, 28) = 92.5, _p_ < .001" > > # Format lm() or glm() object > format_stats(lm(mpg ~ cyl * hp, data = mtcars), term = "cyl") [1] "_β_ = -4.119, SE = 0.988, _t_ = -4.168, _p_ < .001" > format_stats(glm(am ~ cyl * hp, data = mtcars, family = binomial), term = "cyl") [1] "_β_ = -1.749, SE = 0.839, _z_ = -2.084, _p_ = .037" > > # Format lme4::lmer() or lme4::glmer() object > format_stats(lme4::lmer(mpg ~ hp + (1 | cyl), data = mtcars), term = "hp") [1] "_β_ = -0.030, SE = 0.015, _t_ = -2.088" > format_stats(lme4::glmer(am ~ hp + (1 | cyl), data = mtcars, family = binomial), term = "hp") [1] "_β_ = 0.022, SE = 0.017, _z_ = 1.300, _p_ = .194" > > # Format lmerTest::lmer() object > format_stats(lmerTest::lmer(mpg ~ hp + (1 | cyl), data = mtcars), term = "hp") Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Calls: format_stats -> -> as_lmerModLT Execution halted Package: cocoon Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘cocoon.Rmd’ using rmarkdown Quitting from cocoon.Rmd:18-20 [setup-real] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `source_dir()`: ! Failed to evaluate '/home/hornik/tmp/CRAN_recheck/cocoon.Rcheck/vign_test/cocoon/tests/testthat/helper.R'. Caused by error in `forceNewMerMod()`: ! could not find function "forceNewMerMod" --- Backtrace: ▆ 1. └─devtools::load_all() 2. └─pkgload::load_all(...) 3. └─pkgload:::populate_pkg_env(...) 4. └─testthat (local) testthat_source_test_helpers(find_test_dir(path), env = pkg_env) 5. └─testthat::source_dir(path, "^helper.*\\.[rR]$", env = env, wrap = FALSE) 6. └─base::lapply(...) 7. └─testthat (local) FUN(X[[i]], ...) 8. └─testthat::source_file(...) 9. ├─base::withCallingHandlers(...) 10. └─base::eval(exprs, env) 11. └─base::eval(exprs, env) 12. ├─base::suppressMessages(lmerTest::lmer(c ~ a + (1 | e), data = df)) at tests/testthat/helper.R:41:1 13. │ └─base::withCallingHandlers(...) 14. └─lmerTest::lmer(c ~ a + (1 | e), data = df) 15. └─lmerTest:::as_lmerModLT(model, devfun) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'cocoon.Rmd' failed with diagnostics: Failed to evaluate '/home/hornik/tmp/CRAN_recheck/cocoon.Rcheck/vign_test/cocoon/tests/testthat/helper.R'. Caused by error in `forceNewMerMod()`: ! could not find function "forceNewMerMod" --- failed re-building ‘cocoon.Rmd’ SUMMARY: processing the following file failed: ‘cocoon.Rmd’ Error: Vignette re-building failed. Execution halted Package: cocoon Check: tests New result: ERROR Running ‘testthat.R’ [3s/3s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(cocoon) > > test_check("cocoon") Error in `source_dir()`: ! Failed to evaluate './helper.R'. Caused by error in `forceNewMerMod()`: ! could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─testthat::test_check("cocoon") 2. │ └─testthat::test_dir(...) 3. │ └─testthat:::test_files(...) 4. │ └─testthat:::test_files_serial(...) 5. │ └─testthat:::test_files_setup_state(...) 6. │ └─testthat::source_test_helpers(".", env) 7. │ └─testthat::source_dir(path, "^helper.*\\.[rR]$", env = env, wrap = FALSE) 8. │ └─base::lapply(...) 9. │ └─testthat (local) FUN(X[[i]], ...) 10. │ └─testthat::source_file(...) 11. │ ├─base::withCallingHandlers(...) 12. │ └─base::eval(exprs, env) 13. │ └─base::eval(exprs, env) 14. │ ├─base::suppressMessages(lmerTest::lmer(c ~ a + (1 | e), data = df)) at ./helper.R:41:1 15. │ │ └─base::withCallingHandlers(...) 16. │ └─lmerTest::lmer(c ~ a + (1 | e), data = df) 17. │ └─lmerTest:::as_lmerModLT(model, devfun) 18. └─base::.handleSimpleError(...) 19. └─testthat (local) h(simpleError(msg, call)) 20. └─cli::cli_abort(...) 21. └─rlang::abort(...) Execution halted Package: doremi Check: examples New result: ERROR Running examples in ‘doremi-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: analyze.1order > ### Title: DOREMI first order analysis function > ### Aliases: analyze.1order > ### Keywords: analysis exponential first-order > > ### ** Examples > > myresult <- analyze.1order(data = cardio, + id = "id", + input = "load", + time = "time", + signal = "hr", + dermethod ="gold", + derparam = 5) WARN [2026-02-09 22:16:24] Linear mixed-effect regression produced an error. Verify the regression object of the result. Error in analyze.1order(data = cardio, id = "id", input = "load", time = "time", : object 'tau' not found Execution halted Package: doremi Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘Introduction-to-doremi.Rmd’ using rmarkdown [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘Introduction-to-doremi.Rmd’ --- re-building ‘derivatives.Rmd’ using rmarkdown [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘derivatives.Rmd’ --- re-building ‘first-order.Rmd’ using rmarkdown Quitting from first-order.Rmd:193-202 [analysis res3] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `analyze.1order()`: ! object 'tau' not found --- Backtrace: ▆ 1. └─doremi::analyze.1order(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'first-order.Rmd' failed with diagnostics: object 'tau' not found --- failed re-building ‘first-order.Rmd’ --- re-building ‘second-order.Rmd’ using rmarkdown Quitting from second-order.Rmd:174-182 [analysis, example1] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `analyze.2order()`: ! object 'xi' not found --- Backtrace: ▆ 1. └─doremi::analyze.2order(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'second-order.Rmd' failed with diagnostics: object 'xi' not found --- failed re-building ‘second-order.Rmd’ SUMMARY: processing the following files failed: ‘first-order.Rmd’ ‘second-order.Rmd’ Error: Vignette re-building failed. Execution halted Package: effectsize Check: tests New result: ERROR Running ‘testthat.R’ [44s/23s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(effectsize) > > test_check("effectsize") Starting 2 test processes. > test-htest_data.R: For paired samples, 'repeated_measures_d()' provides more options. > test-htest_data.R: For paired samples, 'repeated_measures_d()' provides more options. > test-htest_data.R: For paired samples, 'repeated_measures_d()' provides more options. > test-htest_data.R: For paired samples, 'repeated_measures_d()' provides more options. > test-htest_data.R: For paired samples, 'repeated_measures_d()' provides more options. Saving _problems/test-eta_squared-510.R Saving _problems/test-eta_squared-657.R > test-print.R: For paired samples, 'repeated_measures_d()' provides more options. > test-print.R: For paired samples, 'repeated_measures_d()' provides more options. > test-utils_validate_input_data.R: For paired samples, 'repeated_measures_d()' provides more options. [ FAIL 2 | WARN 0 | SKIP 12 | PASS 831 ] ══ Skipped tests (12) ══════════════════════════════════════════════════════════ • On CRAN (12): 'test-convert_between.R:68:3', 'test-common_language.R:81:3', 'test-effectsize.R:334:3', 'test-eta_squared_posterior.R:2:3', 'test-eta_squared.R:111:3', 'test-eta_squared.R:373:3', 'test-eta_squared.R:668:3', 'test-eta_squared.R:686:3', 'test-eta_squared.R:700:3', 'test-interpret.R:139:3', 'test-interpret.R:233:3', 'test-print.R:110:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-eta_squared.R:507:3'): afex | mixed() ────────────────────────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─base::suppressMessages(...) at test-eta_squared.R:507:3 2. │ └─base::withCallingHandlers(...) 3. └─afex::mixed(iq ~ timecat + (1 + time | id), data = md_15.1, method = "S") 4. ├─base::eval(mf) 5. │ └─base::eval(mf) 6. └─lmerTest::lmer(formula = iq ~ timecat + (1 + time | id), data = data) 7. └─lmerTest:::as_lmerModLT(model, devfun) ── Error ('test-eta_squared.R:657:3'): merMod and lmerModLmerTest ────────────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. └─lmerTest::lmer(Reaction ~ Days + (Days | Subject), data = sleepstudy) at test-eta_squared.R:657:3 2. └─lmerTest:::as_lmerModLT(model, devfun) [ FAIL 2 | WARN 0 | SKIP 12 | PASS 831 ] Error: ! Test failures. Execution halted Package: embed Check: tests New result: ERROR Running ‘testthat.R’ [98s/98s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(embed) Loading required package: recipes Loading required package: dplyr Attaching package: 'dplyr' The following objects are masked from 'package:stats': filter, lag The following objects are masked from 'package:base': intersect, setdiff, setequal, union Attaching package: 'recipes' The following object is masked from 'package:stats': step > > test_check("embed") Hint: To use tensorflow with `py_require()`, call `py_require("tensorflow")` at the start of the R session Hint: To use tensorflow with `py_require()`, call `py_require("tensorflow")` at the start of the R session Hint: To use tensorflow with `py_require()`, call `py_require("tensorflow")` at the start of the R session Hint: To use tensorflow with `py_require()`, call `py_require("tensorflow")` at the start of the R session Hint: To use tensorflow with `py_require()`, call `py_require("tensorflow")` at the start of the R session Saving _problems/test-lencode_bayes-507.R boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') [ FAIL 2 | WARN 0 | SKIP 102 | PASS 366 ] ══ Skipped tests (102) ═════════════════════════════════════════════════════════ • On CRAN (101): 'test-collapse_cart.R:110:1', 'test-collapse_cart.R:125:1', 'test-collapse_cart.R:142:1', 'test-collapse_cart.R:179:1', 'test-collapse_stringdist.R:198:1', 'test-collapse_stringdist.R:215:1', 'test-collapse_stringdist.R:234:1', 'test-collapse_stringdist.R:271:1', 'test-discretize_cart.R:17:1', 'test-discretize_cart.R:46:1', 'test-discretize_cart.R:75:1', 'test-discretize_cart.R:95:1', 'test-discretize_cart.R:115:1', 'test-discretize_cart.R:127:1', 'test-discretize_cart.R:157:1', 'test-discretize_cart.R:207:1', 'test-discretize_cart.R:231:1', 'test-discretize_cart.R:247:1', 'test-discretize_cart.R:284:1', 'test-discretize_xgb.R:4:1', 'test-embed.R:5:3', 'test-embed.R:83:3', 'test-embed.R:154:3', 'test-embed.R:226:3', 'test-embed.R:302:3', 'test-embed.R:333:1', 'test-embed.R:351:3', 'test-embed.R:373:3', 'test-embed.R:419:3', 'test-embed.R:459:3', 'test-embed.R:478:3', 'test-extension_check.R:1:1', 'test-feature_hash.R:1:1', 'test-lencode.R:1:1', 'test-lencode.R:60:1', 'test-lencode.R:129:1', 'test-lencode.R:306:1', 'test-lencode.R:326:1', 'test-lencode.R:377:1', 'test-lencode.R:391:1', 'test-lencode.R:433:1', 'test-lencode_bayes.R:13:3', 'test-lencode_bayes.R:86:3', 'test-lencode_bayes.R:160:3', 'test-lencode_bayes.R:236:3', 'test-lencode_bayes.R:309:3', 'test-lencode_bayes.R:383:3', 'test-lencode_bayes.R:427:1', 'test-lencode_bayes.R:454:1', 'test-lencode_glm.R:1:1', 'test-lencode_glm.R:58:1', 'test-lencode_glm.R:115:1', 'test-lencode_glm.R:228:1', 'test-lencode_glm.R:241:1', 'test-lencode_glm.R:269:1', 'test-lencode_glm.R:283:1', 'test-lencode_glm.R:325:1', 'test-lencode_mixed.R:1:1', 'test-lencode_mixed.R:119:1', 'test-lencode_mixed.R:238:1', 'test-lencode_mixed.R:254:1', 'test-lencode_mixed.R:286:1', 'test-lencode_mixed.R:303:1', 'test-lencode_mixed.R:345:1', 'test-pca_sparse.R:1:1', 'test-pca_sparse.R:50:1', 'test-pca_sparse.R:110:1', 'test-pca_sparse.R:132:1', 'test-pca_sparse.R:164:1', 'test-pca_sparse.R:249:1', 'test-pca_sparse.R:271:1', 'test-pca_sparse_bayes.R:1:1', 'test-pca_sparse_bayes.R:55:1', 'test-pca_sparse_bayes.R:119:1', 'test-pca_sparse_bayes.R:147:1', 'test-pca_sparse_bayes.R:179:1', 'test-pca_sparse_bayes.R:262:1', 'test-pca_sparse_bayes.R:284:1', 'test-pca_truncated.R:47:1', 'test-pca_truncated.R:88:1', 'test-pca_truncated.R:104:1', 'test-pca_truncated.R:134:1', 'test-pca_truncated.R:217:1', 'test-pca_truncated.R:239:1', 'test-umap.R:218:1', 'test-umap.R:282:1', 'test-umap.R:334:1', 'test-umap.R:350:1', 'test-umap.R:427:1', 'test-umap.R:444:1', 'test-woe.R:18:1', 'test-woe.R:25:1', 'test-woe.R:146:1', 'test-woe.R:189:1', 'test-woe.R:206:1', 'test-woe.R:280:1', 'test-woe.R:326:1', 'test-woe.R:342:1', 'test-woe.R:361:1', 'test-woe.R:437:1', 'test-woe.R:455:1' • empty test (1): ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-lencode_bayes.R:446:3'): bake method errors when needed non-standard role columns are missing ── Error in `step_lencode_bayes()`: Error in `step_lencode_bayes()`: Caused by error in `purrr::map()`: i In index: 1. i With name: x3. Caused by error: ! unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' ── Error ('test-lencode_bayes.R:504:3'): printing ────────────────────────────── Error in `step_lencode_bayes()`: Error in `step_lencode_bayes()`: Caused by error in `purrr::map()`: i In index: 1. i With name: x3. Caused by error: ! unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─testthat::expect_snapshot(prep(rec), transform = omit_warning("^(Bulk Effective|Tail Effective|The largest)")) at test-lencode_bayes.R:504:3 2. └─testthat:::expect_snapshot_(...) 3. ├─base::withRestarts(cnd_signal(state$error), muffle_expectation = function() NULL) 4. │ └─base (local) withOneRestart(expr, restarts[[1L]]) 5. │ └─base (local) doWithOneRestart(return(expr), restart) 6. └─rlang::cnd_signal(state$error) [ FAIL 2 | WARN 0 | SKIP 102 | PASS 366 ] Error: ! Test failures. Execution halted Package: fullfact Check: examples New result: ERROR Running examples in ‘fullfact-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: observLmer3 > ### Title: Variance components for normal data 3 > ### Aliases: observLmer3 > > ### ** Examples > > data(chinook_length) #Chinook salmon offspring length > #just a few iterations for the p-value of fixed effect > length_mod3<- observLmer3(observ=chinook_length,dam="dam",sire="sire",response="length", + remain="egg_size + (1|tray)",iter=5) [1] "2026-02-09 22:32:15 CET" boundary (singular) fit: see help('isSingular') Contrasts set to contr.sum for the following variables: dam, sire, tray Formula (the first argument) converted to formula. Numerical variables NOT centered on 0: egg_size If in interactions, interpretation of lower order (e.g., main) effects difficult. REML argument to lmer() set to FALSE for method = 'PB' or 'LRT' boundary (singular) fit: see help('isSingular') Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Calls: observLmer3 ... mixed -> eval -> eval -> -> as_lmerModLT Execution halted Package: fullfact Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘v1_simple_normal.Rmd’ using rmarkdown Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘v1_simple_normal.Rmd’ --- re-building ‘v2_advanced_normal.Rmd’ using rmarkdown Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped Warning in doTryCatch(return(expr), name, parentenv, handler) : zero-length arrow is of indeterminate angle and so skipped [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘v2_advanced_normal.Rmd’ --- re-building ‘v3_expert_normal.Rmd’ using rmarkdown Quitting from v3_expert_normal.Rmd:56-62 [observed-vc] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `forceNewMerMod()`: ! could not find function "forceNewMerMod" --- Backtrace: ▆ 1. └─fullfact::observLmer3(...) 2. └─afex::mixed(m, data = observ, method = "PB", args_test = list(nsim = iter)) 3. ├─base::eval(mf) 4. │ └─base::eval(mf) 5. └─lmerTest::lmer(...) 6. └─lmerTest:::as_lmerModLT(model, devfun) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'v3_expert_normal.Rmd' failed with diagnostics: could not find function "forceNewMerMod" --- failed re-building ‘v3_expert_normal.Rmd’ --- re-building ‘v4_simple_non_normal.Rmd’ using rmarkdown [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘v4_simple_non_normal.Rmd’ --- re-building ‘v5_advanced_non_normal.Rmd’ using rmarkdown [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘v5_advanced_non_normal.Rmd’ --- re-building ‘v6_expert_non_normal.Rmd’ using rmarkdown [WARNING] Deprecated: --highlight-style. Use --syntax-highlighting instead. --- finished re-building ‘v6_expert_non_normal.Rmd’ SUMMARY: processing the following file failed: ‘v3_expert_normal.Rmd’ Error: Vignette re-building failed. Execution halted Package: galamm Check: examples New result: ERROR Running examples in ‘galamm-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: VarCorr > ### Title: Extract variance and correlation components from model > ### Aliases: VarCorr VarCorr.galamm > > ### ** Examples > > # Linear mixed model with heteroscedastic residuals > mod <- galamm( + formula = y ~ x + (1 | id), + dispformula = ~ (1 | item), + data = hsced + ) > > # Extract information on variance and covariance > VarCorr(mod) Error: unable to find an inherited method for function ‘getTheta’ for signature ‘object = "NULL"’ Execution halted Package: galamm Check: tests New result: ERROR Running ‘testthat.R’ [34s/34s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(galamm) > > test_check("galamm") Saving _problems/test-galamm-glmm-34.R Saving _problems/test-galamm-heteroscedastic-8.R Saving _problems/test-galamm-heteroscedastic-74.R Saving _problems/test-galamm-latent-covariates-interaction-18.R Saving _problems/test-galamm-latent-covariates-interaction-69.R Saving _problems/test-galamm-latent-covariates-interaction-92.R Saving _problems/test-galamm-lmm-100.R Saving _problems/test-galamm-lmm-175.R Saving _problems/test-galamm-mixed-resp-184.R Saving _problems/test-galamm-semiparametric-11.R Saving _problems/test-galamm-semiparametric-231.R Saving _problems/test-galamm-semiparametric-351.R Saving _problems/test-galamm-semiparametric-432.R Saving _problems/test-galamm-setup-343.R Saving _problems/test-gratia-functions-4.R Saving _problems/test-parameter-recovery-38.R Saving _problems/test-parameter-recovery-62.R [ FAIL 17 | WARN 0 | SKIP 4 | PASS 146 ] ══ Skipped tests (4) ═══════════════════════════════════════════════════════════ • On CRAN (1): 'test-galamm-mixed-resp.R:132:1' • Skipping extended tests (3): 'test-galamm-lmm.R:340:3', 'test-parameter-recovery.R:116:3', 'test-parameter-recovery.R:133:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-galamm-glmm.R:34:3'): Logistic GLMM with simple factor works ─── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::summary(mod) 2. └─galamm:::summary.galamm(mod) 3. ├─nlme::VarCorr(ret) 4. └─galamm:::VarCorr.galamm(ret) 5. ├─base::structure(...) 6. └─lme4::mkVarCorr(...) 7. └─base::lapply(...) 8. └─lme4 (local) FUN(X[[i]], ...) 9. ├─base::structure(...) 10. └─lme4:::getTheta(reCovs[[i]]) 11. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-heteroscedastic.R:8:3'): Heteroscedastic model works ──── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::print(summary(mod), digits = 3) 2. ├─base::summary(mod) 3. └─galamm:::summary.galamm(mod) 4. ├─nlme::VarCorr(ret) 5. └─galamm:::VarCorr.galamm(ret) 6. ├─base::structure(...) 7. └─lme4::mkVarCorr(...) 8. └─base::lapply(...) 9. └─lme4 (local) FUN(X[[i]], ...) 10. ├─base::structure(...) 11. └─lme4:::getTheta(reCovs[[i]]) 12. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-heteroscedastic.R:74:3'): Heteroscedastic model works with more than one group ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::print(summary(mod), digits = 3) 2. ├─base::summary(mod) 3. └─galamm:::summary.galamm(mod) 4. ├─nlme::VarCorr(ret) 5. └─galamm:::VarCorr.galamm(ret) 6. ├─base::structure(...) 7. └─lme4::mkVarCorr(...) 8. └─base::lapply(...) 9. └─lme4 (local) FUN(X[[i]], ...) 10. ├─base::structure(...) 11. └─lme4:::getTheta(reCovs[[i]]) 12. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-latent-covariates-interaction.R:18:3'): Interaction between latent and observed covariates works ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::print(summary(mod), digits = 2) 2. ├─base::summary(mod) 3. └─galamm:::summary.galamm(mod) 4. ├─nlme::VarCorr(ret) 5. └─galamm:::VarCorr.galamm(ret) 6. ├─base::structure(...) 7. └─lme4::mkVarCorr(...) 8. └─base::lapply(...) 9. └─lme4 (local) FUN(X[[i]], ...) 10. ├─base::structure(...) 11. └─lme4:::getTheta(reCovs[[i]]) 12. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-latent-covariates-interaction.R:69:3'): Crossed latent-observed interaction models work ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::print(summary(mod), digits = 2) 2. ├─base::summary(mod) 3. └─galamm:::summary.galamm(mod) 4. ├─nlme::VarCorr(ret) 5. └─galamm:::VarCorr.galamm(ret) 6. ├─base::structure(...) 7. └─lme4::mkVarCorr(...) 8. └─base::lapply(...) 9. └─lme4 (local) FUN(X[[i]], ...) 10. ├─base::structure(...) 11. └─lme4:::getTheta(reCovs[[i]]) 12. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-latent-covariates-interaction.R:85:3'): Latent-observed interaction with smooths ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─galamm::galamm(...) at test-galamm-latent-covariates-interaction.R:85:3 2. └─galamm:::gamm4.wrapup(gobj, ret, final_model) 3. ├─nlme::VarCorr(ret) 4. └─galamm:::VarCorr.galamm(ret) 5. ├─base::structure(...) 6. └─lme4::mkVarCorr(...) 7. └─base::lapply(...) 8. └─lme4 (local) FUN(X[[i]], ...) 9. ├─base::structure(...) 10. └─lme4:::getTheta(reCovs[[i]]) 11. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-lmm.R:93:3'): LMM with simple factor works ────────────── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-galamm-lmm.R:93:3 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─base::summary(mod) 5. └─galamm:::summary.galamm(mod) 6. ├─nlme::VarCorr(ret) 7. └─galamm:::VarCorr.galamm(ret) 8. ├─base::structure(...) 9. └─lme4::mkVarCorr(...) 10. └─base::lapply(...) 11. └─lme4 (local) FUN(X[[i]], ...) 12. ├─base::structure(...) 13. └─lme4:::getTheta(reCovs[[i]]) 14. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-lmm.R:169:3'): LMM with simple factor works with Nelder-Mead ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-galamm-lmm.R:169:3 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─base::summary(mod) 5. └─galamm:::summary.galamm(mod) 6. ├─nlme::VarCorr(ret) 7. └─galamm:::VarCorr.galamm(ret) 8. ├─base::structure(...) 9. └─lme4::mkVarCorr(...) 10. └─base::lapply(...) 11. └─lme4 (local) FUN(X[[i]], ...) 12. ├─base::structure(...) 13. └─lme4:::getTheta(reCovs[[i]]) 14. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-mixed-resp.R:184:3'): Mixed response and heteroscedastic error works ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::print(summary(mod), digits = 2) 2. ├─base::summary(mod) 3. └─galamm:::summary.galamm(mod) 4. ├─nlme::VarCorr(ret) 5. └─galamm:::VarCorr.galamm(ret) 6. ├─base::structure(...) 7. └─lme4::mkVarCorr(...) 8. └─base::lapply(...) 9. └─lme4 (local) FUN(X[[i]], ...) 10. ├─base::structure(...) 11. └─lme4:::getTheta(reCovs[[i]]) 12. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-semiparametric.R:11:3'): galamm reproduces gamm4 ──────── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─galamm::galamm(formula = y ~ s(x), data = dat) at test-galamm-semiparametric.R:11:3 2. └─galamm:::gamm4.wrapup(gobj, ret, final_model) 3. ├─nlme::VarCorr(ret) 4. └─galamm:::VarCorr.galamm(ret) 5. ├─base::structure(...) 6. └─lme4::mkVarCorr(...) 7. └─base::lapply(...) 8. └─lme4 (local) FUN(X[[i]], ...) 9. ├─base::structure(...) 10. └─lme4:::getTheta(reCovs[[i]]) 11. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-semiparametric.R:225:3'): Basic GAMM with factor structures works ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─galamm::galamm(...) at test-galamm-semiparametric.R:225:3 2. └─galamm:::gamm4.wrapup(gobj, ret, final_model) 3. ├─nlme::VarCorr(ret) 4. └─galamm:::VarCorr.galamm(ret) 5. ├─base::structure(...) 6. └─lme4::mkVarCorr(...) 7. └─base::lapply(...) 8. └─lme4 (local) FUN(X[[i]], ...) 9. ├─base::structure(...) 10. └─lme4:::getTheta(reCovs[[i]]) 11. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-semiparametric.R:344:3'): GAMM with factor structures and random effects works ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─galamm::galamm(...) at test-galamm-semiparametric.R:344:3 2. └─galamm:::gamm4.wrapup(gobj, ret, final_model) 3. ├─nlme::VarCorr(ret) 4. └─galamm:::VarCorr.galamm(ret) 5. ├─base::structure(...) 6. └─lme4::mkVarCorr(...) 7. └─base::lapply(...) 8. └─lme4 (local) FUN(X[[i]], ...) 9. ├─base::structure(...) 10. └─lme4:::getTheta(reCovs[[i]]) 11. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-semiparametric.R:421:3'): galamm with by variables and loadings works ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─galamm::galamm(...) at test-galamm-semiparametric.R:421:3 2. └─galamm:::gamm4.wrapup(gobj, ret, final_model) 3. ├─nlme::VarCorr(ret) 4. └─galamm:::VarCorr.galamm(ret) 5. ├─base::structure(...) 6. └─lme4::mkVarCorr(...) 7. └─base::lapply(...) 8. └─lme4 (local) FUN(X[[i]], ...) 9. ├─base::structure(...) 10. └─lme4:::getTheta(reCovs[[i]]) 11. └─methods (local) ``(``, ``, ``) ── Error ('test-galamm-setup.R:343:3'): multiple factors and factors in fixed effects are allowed ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::print(summary(kyps.model), digits = 2) 2. ├─base::summary(kyps.model) 3. └─galamm:::summary.galamm(kyps.model) 4. ├─nlme::VarCorr(ret) 5. └─galamm:::VarCorr.galamm(ret) 6. ├─base::structure(...) 7. └─lme4::mkVarCorr(...) 8. └─base::lapply(...) 9. └─lme4 (local) FUN(X[[i]], ...) 10. ├─base::structure(...) 11. └─lme4:::getTheta(reCovs[[i]]) 12. └─methods (local) ``(``, ``, ``) ── Error ('test-gratia-functions.R:4:3'): gratia functions work ──────────────── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─galamm::galamm(y ~ s(x) + (1 | id), data = dat) at test-gratia-functions.R:4:3 2. └─galamm:::gamm4.wrapup(gobj, ret, final_model) 3. ├─nlme::VarCorr(ret) 4. └─galamm:::VarCorr.galamm(ret) 5. ├─base::structure(...) 6. └─lme4::mkVarCorr(...) 7. └─base::lapply(...) 8. └─lme4 (local) FUN(X[[i]], ...) 9. ├─base::structure(...) 10. └─lme4:::getTheta(reCovs[[i]]) 11. └─methods (local) ``(``, ``, ``) ── Error ('test-parameter-recovery.R:38:5'): LMM parameters are within a tolerance of their generated value ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─base::lapply(...) at test-parameter-recovery.R:36:3 2. └─galamm (local) FUN(X[[i]], ...) 3. └─galamm (local) lmm_simulator_function(n_ids = 100, repetition = 1) at test-parameter-recovery.R:38:5 4. ├─base::as.data.frame(VarCorr(mod)) at test-parameter-recovery.R:26:3 5. ├─nlme::VarCorr(mod) 6. └─galamm:::VarCorr.galamm(mod) 7. ├─base::structure(...) 8. └─lme4::mkVarCorr(...) 9. └─base::lapply(...) 10. └─lme4 (local) FUN(X[[i]], ...) 11. ├─base::structure(...) 12. └─lme4:::getTheta(reCovs[[i]]) 13. └─methods (local) ``(``, ``, ``) ── Error ('test-parameter-recovery.R:59:5'): LMM parameters are recovered with increasing precision ── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. └─base::Map(lmm_simulator_function, n_ids = sim_params$n_ids, repetition = sim_params$repetition) at test-parameter-recovery.R:59:5 2. └─base::mapply(FUN = f, ..., SIMPLIFY = FALSE) 3. └─galamm (local) ``(n_ids = dots[[1L]][[1L]], repetition = dots[[2L]][[1L]]) 4. ├─base::as.data.frame(VarCorr(mod)) at test-parameter-recovery.R:26:3 5. ├─nlme::VarCorr(mod) at test-parameter-recovery.R:26:3 6. └─galamm:::VarCorr.galamm(mod) 7. ├─base::structure(...) 8. └─lme4::mkVarCorr(...) 9. └─base::lapply(...) 10. └─lme4 (local) FUN(X[[i]], ...) 11. ├─base::structure(...) 12. └─lme4:::getTheta(reCovs[[i]]) 13. └─methods (local) ``(``, ``, ``) [ FAIL 17 | WARN 0 | SKIP 4 | PASS 146 ] Error: ! Test failures. Execution halted Package: GimmeMyStats Check: tests New result: ERROR Running ‘testthat.R’ [8s/8s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(GimmeMyStats) Loading required package: magrittr Attaching package: 'magrittr' The following objects are masked from 'package:testthat': equals, is_less_than, not Loading required package: tidyverse ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── ✔ dplyr 1.2.0 ✔ readr 2.1.6 ✔ forcats 1.0.1 ✔ stringr 1.6.0 ✔ ggplot2 4.0.2 ✔ tibble 3.3.1 ✔ lubridate 1.9.5 ✔ tidyr 1.3.2 ✔ purrr 1.2.1 ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── ✖ readr::edition_get() masks testthat::edition_get() ✖ magrittr::equals() masks testthat::equals() ✖ tidyr::extract() masks magrittr::extract() ✖ dplyr::filter() masks stats::filter() ✖ magrittr::is_less_than() masks testthat::is_less_than() ✖ dplyr::lag() masks stats::lag() ✖ readr::local_edition() masks testthat::local_edition() ✖ magrittr::not() masks testthat::not() ✖ purrr::set_names() masks magrittr::set_names() ℹ Use the conflicted package () to force all conflicts to become errors > > test_check("GimmeMyStats") Saving _problems/test-print-test-16.R [ FAIL 1 | WARN 2 | SKIP 0 | PASS 102 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-print-test.R:16:5'): mean_test test works ────────────────────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─lmer(Reaction ~ Days + (Days | Subject), sleepstudy) %>% ... at test-print-test.R:16:5 2. ├─GimmeMyStats::print_test(.) 3. └─lmerTest::lmer(Reaction ~ Days + (Days | Subject), sleepstudy) 4. └─lmerTest:::as_lmerModLT(model, devfun) [ FAIL 1 | WARN 2 | SKIP 0 | PASS 102 ] Error: ! Test failures. Execution halted Package: grafify Check: examples New result: ERROR Running examples in ‘grafify-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: mixed_anova > ### Title: ANOVA table from linear mixed effects analysis. > ### Aliases: mixed_anova > > ### ** Examples > > #Usage with one fixed (Student) and random factor (Experiment) > mixed_anova(data = data_doubling_time, + Y_value = "Doubling_time", + Fixed_Factor = "Student", + Random_Factor = "Experiment") The new argument `AvgRF` is set to TRUE by default in >=5.0.0). Response variable is averaged over levels of Fixed and Random factors. Use help for details. boundary (singular) fit: see help('isSingular') Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Calls: mixed_anova -> mixed_model -> as_lmerModLmerTest -> as_lmerModLT Execution halted Package: insight Check: tests New result: ERROR Running ‘testthat.R’ [142s/69s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(insight) > test_check("insight") Starting 2 test processes. > test-find_transformation.R: boundary (singular) fit: see help('isSingular') > test-gamlss.R: GAMLSS-RS iteration 1: Global Deviance = 365.2328 > test-gamlss.R: GAMLSS-RS iteration 2: Global Deviance = 365.1292 > test-gamlss.R: GAMLSS-RS iteration 3: Global Deviance = 365.1269 > test-gamlss.R: GAMLSS-RS iteration 4: Global Deviance = 365.1268 > test-gamlss.R: GAMLSS-RS iteration 1: Global Deviance = 5779.746 > test-gamlss.R: GAMLSS-RS iteration 2: Global Deviance = 5779.746 > test-gamlss.R: GAMLSS-RS iteration 1: Global Deviance = 703.1164 > test-gamlss.R: GAMLSS-RS iteration 2: Global Deviance = 703.1164 > test-get_model.R: Loading required namespace: GPArotation > test-get_random.R: boundary (singular) fit: see help('isSingular') Saving _problems/test-get_datagrid-383.R > test-glmmPQL.R: iteration 1 > test-is_converged.R: boundary (singular) fit: see help('isSingular') Saving _problems/test-lmer-69.R Saving _problems/test-lmer-545.R > test-mmrm.R: mmrm() registered as emmeans extension > test-mmrm.R: mmrm() registered as car::Anova extension Saving _problems/test-mmrm-48.R > test-model_info.R: boundary (singular) fit: see help('isSingular') > test-nestedLogit.R: list(work = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 1L, > test-nestedLogit.R: 0L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, > test-nestedLogit.R: 0L, 1L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, > test-nestedLogit.R: 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, > test-nestedLogit.R: 1L, 1L, 0L, 0L, 0L, 1L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 1L, > test-nestedLogit.R: 1L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, > test-nestedLogit.R: 1L, 1L, 0L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 0L, 0L > test-nestedLogit.R: ), full = c(1L, 1L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, > test-nestedLogit.R: 1L, 0L, 1L, 1L, 0L, 1L, 0L, 0L, 0L, 1L, 1L, 0L, 1L, 1L, 0L, 0L, > test-nestedLogit.R: 0L, 1L, 1L, 1L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L)) > test-polr.R: > test-polr.R: Re-fitting to get Hessian > test-polr.R: > test-polr.R: > test-polr.R: Re-fitting to get Hessian > test-polr.R: > test-survey_coxph.R: Stratified Independent Sampling design (with replacement) > test-survey_coxph.R: dpbc <- survey::svydesign( > test-survey_coxph.R: id = ~1, > test-survey_coxph.R: prob = ~randprob, > test-survey_coxph.R: strata = ~edema, > test-survey_coxph.R: data = subset(pbc, randomized) > test-survey_coxph.R: ) > test-survey_coxph.R: Stratified Independent Sampling design (with replacement) > test-survey_coxph.R: dpbc <- survey::svydesign( > test-survey_coxph.R: id = ~1, > test-survey_coxph.R: prob = ~randprob, > test-survey_coxph.R: strata = ~edema, > test-survey_coxph.R: data = subset(pbc, randomized) > test-survey_coxph.R: ) > test-survey_coxph.R: Stratified Independent Sampling design (with replacement) > test-survey_coxph.R: dpbc <- survey::svydesign( > test-survey_coxph.R: id = ~1, > test-survey_coxph.R: prob = ~randprob, > test-survey_coxph.R: strata = ~edema, > test-survey_coxph.R: data = subset(pbc, randomized) > test-survey_coxph.R: ) [ FAIL 4 | WARN 0 | SKIP 96 | PASS 3512 ] ══ Skipped tests (96) ══════════════════════════════════════════════════════════ • On CRAN (89): 'test-GLMMadaptive.R:2:1', 'test-averaging.R:1:1', 'test-bias_correction.R:1:1', 'test-betareg.R:197:5', 'test-brms.R:1:1', 'test-brms_aterms.R:1:1', 'test-brms_gr_random_effects.R:1:1', 'test-brms_missing.R:1:1', 'test-brms_mm.R:1:1', 'test-brms_von_mises.R:1:1', 'test-blmer.R:262:3', 'test-clean_names.R:109:3', 'test-clean_parameters.R:1:1', 'test-coxme.R:1:1', 'test-cpglmm.R:152:3', 'test-clmm.R:170:3', 'test-export_table.R:6:3', 'test-export_table.R:18:3', 'test-export_table.R:152:3', 'test-export_table.R:273:3', 'test-export_table.R:327:1', 'test-export_table.R:814:3', 'test-export_table.R:858:3', 'test-export_table.R:918:1', 'test-export_table.R:939:3', 'test-export_table.R:1003:3', 'test-find_smooth.R:39:3', 'test-fixest.R:2:1', 'test-find_random.R:43:3', 'test-format_table.R:2:1', 'test-format_table_ci.R:73:3', 'test-gam.R:2:1', 'test-get_data.R:507:1', 'test-get_loglikelihood.R:143:3', 'test-get_loglikelihood.R:223:3', 'test-get_loglikelihood.R:320:3', 'test-get_predicted.R:2:1', 'test-get_priors.R:1:1', 'test-get_residuals.R:68:3', 'test-get_residuals.R:97:3', 'test-get_varcov.R:43:3', 'test-get_varcov.R:57:3', 'test-get_datagrid.R:1092:3', 'test-get_datagrid.R:1129:5', 'test-is_converged.R:47:1', 'test-iv_robust.R:120:3', 'test-lavaan.R:1:1', 'test-lcmm.R:1:1', 'test-lme.R:28:3', 'test-lme.R:212:3', 'test-glmmTMB.R:67:3', 'test-glmmTMB.R:767:3', 'test-glmmTMB.R:803:3', 'test-glmmTMB.R:1142:3', 'test-marginaleffects.R:1:1', 'test-mgcv.R:1:1', 'test-mipo.R:1:1', 'test-mlogit.R:1:1', 'test-modelbased.R:1:1', 'test-model_info.R:106:3', 'test-mvrstanarm.R:1:1', 'test-panelr-asym.R:165:3', 'test-null_model.R:85:3', 'test-phylolm.R:1:1', 'test-panelr.R:301:3', 'test-print_parameters.R:1:1', 'test-r2_nakagawa_bernoulli.R:1:1', 'test-r2_nakagawa_beta.R:1:1', 'test-r2_nakagawa_binomial.R:1:1', 'test-r2_nakagawa_gamma.R:1:1', 'test-r2_nakagawa_linear.R:1:1', 'test-r2_nakagawa_negbin.R:1:1', 'test-r2_nakagawa_negbin_zi.R:1:1', 'test-r2_nakagawa_ordered_beta.R:1:1', 'test-r2_nakagawa_poisson.R:1:1', 'test-r2_nakagawa_poisson_zi.R:1:1', 'test-r2_nakagawa_truncated_poisson.R:1:1', 'test-r2_nakagawa_tweedie.R:1:1', 'test-rms.R:1:1', 'test-rqss.R:1:1', 'test-rstanarm.R:1:1', 'test-sdmTMB.R:1:1', 'test-selection.R:2:1', 'test-spatial.R:2:1', 'test-svylme.R:1:1', 'test-tidymodels.R:1:1', 'test-vgam.R:2:1', 'test-weightit.R:1:1', 'test-rlmer.R:276:3' • On Linux (3): 'test-BayesFactorBF.R:1:1', 'test-MCMCglmm.R:1:1', 'test-get_data.R:161:3' • TRUE is TRUE (1): 'test-feis.R:3:1' • works interactively (2): 'test-coxph-panel.R:34:3', 'test-coxph.R:38:3' • {bigglm} is not installed (1): 'test-model_info.R:24:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-get_datagrid.R:376:3'): get_datagrid - models ────────────────── Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Backtrace: ▆ 1. ├─base::suppressWarnings(...) at test-get_datagrid.R:376:3 2. │ └─base::withCallingHandlers(...) 3. └─rstanarm::stan_gamm4(...) 4. └─rstanarm::stan_glm.fit(...) 5. └─base::apply(...) 6. └─rstanarm (local) FUN(newX[, i], ...) 7. └─lme4::mkVarCorr(sc = 1, cnms, nc, theta, nms) 8. └─base::lapply(...) 9. └─lme4 (local) FUN(X[[i]], ...) 10. ├─base::structure(...) 11. └─lme4:::getTheta(reCovs[[i]]) 12. └─methods (local) ``(``, ``, ``) ── Error ('test-lmer.R:64:3'): get_df ────────────────────────────────────────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─testthat::expect_equal(...) at test-lmer.R:64:3 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. ├─insight::get_df(m1, type = "satterthwaite") 5. └─insight:::get_df.lmerMod(m1, type = "satterthwaite") 6. ├─insight:::.degrees_of_freedom_satterthwaite(x) 7. └─insight:::.degrees_of_freedom_satterthwaite.lmerMod(x) 8. └─lmerTest::as_lmerModLmerTest(x) 9. └─lmerTest:::as_lmerModLT(model, devfun, tol = tol) ── Error ('test-lmer.R:545:3'): satterthwaite dof vs. emmeans ────────────────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─insight::get_predicted(...) at test-lmer.R:545:3 2. └─insight:::get_predicted.lmerMod(...) 3. ├─insight::get_predicted_ci(...) 4. └─insight:::get_predicted_ci.default(...) 5. └─insight (local) ci_function(...) 6. └─insight:::.satterthwaite_kr_df_per_obs(x, type = ci_method, data = data) 7. └─base::sapply(...) 8. └─base::lapply(X = X, FUN = FUN, ...) 9. └─insight (local) FUN(X[[i]], ...) 10. ├─base::suppressMessages(...) 11. │ └─base::withCallingHandlers(...) 12. ├─lmerTest::contestMD(x, mm[i, , drop = FALSE], ddf = type) 13. └─lmerTest:::contestMD.lmerMod(x, mm[i, , drop = FALSE], ddf = type) 14. └─lmerTest::as_lmerModLmerTest(model) 15. └─lmerTest:::as_lmerModLT(model, devfun, tol = tol) ── Failure ('test-mmrm.R:48:3'): get_df ──────────────────────────────────────── Expected `get_df(mod_mmrm, type = "model")` to equal 12. Differences: `actual`: 10.0 `expected`: 12.0 [ FAIL 4 | WARN 0 | SKIP 96 | PASS 3512 ] Error: ! Test failures. Execution halted Package: iSTAY Check: examples New result: ERROR Running examples in ‘iSTAY-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: ggiSTAY_analysis > ### Title: ggplot2 extension for plotting diversity–stability and > ### diversity–synchrony relationships. > ### Aliases: ggiSTAY_analysis > > ### ** Examples > > data("Data_Jena_20_metacommunities") > data("Data_Jena_76_metapopulations") > data("Data_Jena_462_populations") > data("Data_Jena_hierarchical_structure") > > ## Single time series analysis > # Analyze the stability of individual plots and diversity-stability > # relationship based on 76 plots > # See Example 2 in the iSTAY vignette for the output. > individual_plots <- do.call(rbind, Data_Jena_20_metacommunities) > output_individual_plots_div <- iSTAY_Single(data=individual_plots, order.q=c(1,2), Alltime=TRUE) > output_individual_plots_div <- data.frame(output_individual_plots_div, + log2_sowndiv = log2(as.numeric(do.call(rbind, + strsplit(output_individual_plots_div[,1],"[._]+"))[,2])), + block = do.call(rbind, + strsplit(output_individual_plots_div[,1],"[._]+"))[,1]) > > ggiSTAY_analysis(output = output_individual_plots_div, x_variable = "log2_sowndiv", + by_group="block", model="LMM") boundary (singular) fit: see help('isSingular') Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Calls: ggiSTAY_analysis -> -> as_lmerModLT Execution halted Package: iSTAY Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘iStay.Rmd’ using rmarkdown Quitting from iStay.Rmd:278-281 [unnamed-chunk-25] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `forceNewMerMod()`: ! could not find function "forceNewMerMod" --- Backtrace: ▆ 1. └─iSTAY::ggiSTAY_analysis(...) 2. └─lmerTest::lmer(...) 3. └─lmerTest:::as_lmerModLT(model, devfun) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'iStay.Rmd' failed with diagnostics: could not find function "forceNewMerMod" --- failed re-building ‘iStay.Rmd’ SUMMARY: processing the following file failed: ‘iStay.Rmd’ Error: Vignette re-building failed. Execution halted Package: jstable Check: tests New result: ERROR Running ‘testthat.R’ [51s/52s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(jstable) > > test_check("jstable") Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Independent Sampling design (with replacement) svydesign(id = ~1, data = lung) Independent Sampling design (with replacement) subset(data, get(var_subgroup) == .) Saving _problems/test-forestglm-19.R Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) Stratified Independent Sampling design (with replacement) dpbc <- survey::svydesign(id = ~1, prob = ~randprob, strata = ~edema, data = subset(pbc, randomized)) [ FAIL 1 | WARN 37 | SKIP 0 | PASS 122 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-forestglm.R:19:3'): Run TableSubgroupMultiGLM ────────────────── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─testthat::expect_is(...) at test-forestglm.R:19:3 2. │ └─testthat::quasi_label(enquo(object), label) 3. │ └─rlang::eval_bare(expr, quo_get_env(quo)) 4. └─jstable::TableSubgroupMultiGLM(...) 5. └─jstable::TableSubgroupGLM(...) 6. └─lmerTest::lmer(formula, data = data) 7. └─lmerTest:::as_lmerModLT(model, devfun) [ FAIL 1 | WARN 37 | SKIP 0 | PASS 122 ] Error: ! Test failures. Execution halted Package: lucid Check: tests New result: ERROR Running ‘testthat.R’ [2s/2s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(lucid) > > test_check("lucid") id P1 P2 P3 1 A a -1 3.3175e-140 2 B b 0 1.0637e-165 3 C c 1 1.1279e-157 id P1 P2 1 A a -1 2 B b 0 3 C c 1 P3 1 0.0000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000000332 2 0 3 0 effect variance stddev (Intercept) 615.3 24.81 Residual 16.17 4.021 grp var1 var2 vcov sdcor Rail (Intercept) 615.3 24.81 Residual 16.17 4.021 Saving _problems/test_vc-66.R grp var1 var2 vcov sdcor Rail (Intercept) 0.1474 0.3839 Residual 11.11 3.333 iteration LogLik wall cpu(sec) restrained 1 -5.78344 23:17:2 0 0 2 1.8756 23:17:2 0 0 3 8.51187 23:17:2 0 0 4 9.74181 23:17:2 0 0 5 9.75215 23:17:2 0 0 6 9.75225 23:17:2 0 0 effect VarComp VarCompSE Zratio Constr Rail.travel-travel 615.3 392.3 1.6 P units.travel-travel 16.17 6.602 2.4 P [ FAIL 1 | WARN 1 | SKIP 2 | PASS 21 ] ══ Skipped tests (2) ═══════════════════════════════════════════════════════════ • empty test (2): 'test_lucid.r:150:1', 'test_vc.r:74:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test_vc.r:57:3'): glmer ─────────────────────────────────────────── Expected `vc(m1g)` to equal `structure(...)`. Differences: Component "vcov": Mean relative difference: 0.1327367 Component "sdcor": Mean relative difference: 0.2419787 [ FAIL 1 | WARN 1 | SKIP 2 | PASS 21 ] Error: ! Test failures. 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|======================================================================| 100% > # replicates study > data(bloodpressure) > mecor(creatinine ~ MeasError(sbp30, replicate = cbind(sbp60, sbp120)) + age, + data = bloodpressure, + method = "mle") Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Calls: mecor ... tryCatchOne -> doTryCatch -> -> as_lmerModLT Execution halted Package: MedianaDesigner Check: tests New result: ERROR Running ‘testthat.r’ [140s/166s] Running the tests in ‘tests/testthat.r’ failed. Complete output: > library(testthat) > library(MedianaDesigner) > > test_check("MedianaDesigner") Saving _problems/test-ClustRand-55.R [ FAIL 1 | WARN 434 | SKIP 0 | PASS 883 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-ClustRand.r:55:5'): Success runs ClustRand for additional cases ── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. └─MedianaDesigner::ClustRand(parametersNormal2ArmsRandomClusterSizeGLMEM) at test-ClustRand.r:55:5 2. └─MedianaDesigner::ClustRandSingleCore(parameters) 3. └─MedianaDesigner::ClustRandGLMEMR(params_for_run) 4. ├─base::tryCatch(...) 5. │ └─base (local) tryCatchList(expr, classes, parentenv, handlers) 6. │ └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]]) 7. │ └─base (local) doTryCatch(return(expr), name, parentenv, handler) 8. └─lmerTest::lmer(y ~ x + (1 | id), data = data_set, REML = TRUE) 9. └─lmerTest:::as_lmerModLT(model, devfun) [ FAIL 1 | WARN 434 | SKIP 0 | PASS 883 ] Error: ! Test failures. Execution halted Package: metan Check: re-building of vignette outputs New result: ERROR Error(s) in re-building vignettes: ... --- re-building ‘metan_start.Rmd’ using rmarkdown Quitting from metan_start.Rmd:139-141 [unnamed-chunk-8] ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error in `forceNewMerMod()`: ! could not find function "forceNewMerMod" --- Backtrace: ▆ 1. ├─metan::gamem_met(data_ge, ENV, GEN, REP, everything()) 2. │ ├─... %>% suppressMessages() 3. │ └─lmerTest::lmer(model_formula, data = data) 4. │ └─lmerTest:::as_lmerModLT(model, devfun) 5. ├─base::suppressMessages(.) 6. │ └─base::withCallingHandlers(...) 7. └─base::suppressWarnings(.) 8. └─base::withCallingHandlers(...) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Error: processing vignette 'metan_start.Rmd' failed with diagnostics: could not find function "forceNewMerMod" --- failed re-building ‘metan_start.Rmd’ SUMMARY: processing the following file failed: ‘metan_start.Rmd’ Error: Vignette re-building failed. Execution halted Package: mlmpower Check: examples New result: ERROR Running examples in ‘mlmpower-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: analyze > ### Title: Analyzes a single 'mp_data' using 'lme4::lmer' > ### Aliases: analyze > > ### ** Examples > > # Create Model > model <- ( + outcome('Y') + + within_predictor('X') + + effect_size(icc = 0.1) + ) > # Set seed > set.seed(198723) > # Create data set and analyze > model |> generate(5, 50) |> analyze() -> results Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Calls: analyze -> quiet -> suppressMessages -> withCallingHandlers Execution halted Package: mlr3pipelines Check: tests New result: ERROR Running ‘testthat.R’ [427s/210s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > if (requireNamespace("testthat", quietly = TRUE)) { + library("checkmate") + library("testthat") + library("mlr3") + library("paradox") + library("mlr3pipelines") + test_check("mlr3pipelines") + } Starting 2 test processes. > test_Graph.R: Training debug.multi with input list(input_1 = 1, input_2 = 1) > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_PipeOp.R: Training test_autotrain > test_PipeOp.R: Predicting test_autotrain > test_filter_ensemble.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. Saving _problems/test_filter_ensemble-294.R Saving _problems/test_filter_ensemble-307.R > test_mlr_graphs_robustify.R: 'as(, "dgTMatrix")' is deprecated. > test_mlr_graphs_robustify.R: Use 'as(., "TsparseMatrix")' instead. > test_mlr_graphs_robustify.R: See help("Deprecated") and help("Matrix-depr > test_mlr_graphs_robustify.R: ecated"). > test_multiplicities.R: > test_multiplicities.R: [[1]] > test_multiplicities.R: [1] 0 > test_multiplicities.R: > test_multiplicities.R: > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done" > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done" > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done" > test_pipeop_blsmote.R: [1] "Borderline-SMOTE done" > test_pipeop_isomap.R: 2026-02-09 23:01:33.498922: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:33.499718: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:33.512479: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:33.533598: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:33.596066: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:33.596573: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:33.605648: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:33.625046: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:33.659681: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:33.660429: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:33.678032: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:33.721253: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:33.722742: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:33.761196: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:33.761711: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:33.778739: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:33.822045: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:33.823465: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:33.930425: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:33.930914: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:33.948089: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:34.049797: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:34.120379: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:34.122463: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:34.150468: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:34.35908: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:34.361958: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:34.529414: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:34.529763: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:34.534858: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:34.547417: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:34.570399: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:34.570887: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:34.581349: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:34.610382: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:34.611201: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:34.743042: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:34.743501: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:34.750824: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:34.770648: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:34.822704: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:34.823366: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:34.837012: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:34.878379: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:34.87964: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:34.96918: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:34.969637: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:34.976951: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:34.995913: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:35.050133: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:35.05081: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.076191: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.119244: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:35.120477: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:35.210166: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:35.210615: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.217448: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.235536: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:35.287158: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:35.287798: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.300923: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.344418: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:35.34559: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:35.432631: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:35.433113: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.440308: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.460332: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:35.517588: L-Isomap embed START > test_pipeop_isomap.R: 2026-02-09 23:01:35.5183: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.532537: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.576182: embedding > test_pipeop_isomap.R: 2026-02-09 23:01:35.577412: DONE > test_pipeop_isomap.R: 2026-02-09 23:01:35.673513: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:35.674036: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.681051: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.700199: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:35.793443: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:35.793886: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.80082: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.819658: Classical Scaling > test_pipeop_isomap.R: 2026-02-09 23:01:35.848509: Isomap START > test_pipeop_isomap.R: 2026-02-09 23:01:35.848989: constructing knn graph > test_pipeop_isomap.R: 2026-02-09 23:01:35.855999: calculating geodesic distances > test_pipeop_isomap.R: 2026-02-09 23:01:35.875151: Classical Scaling > test_pipeop_nmf.R: [PipeOpNMFstate] > test_pipeop_nmf.R: [PipeOpNMFstate] > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_task_preproc.R: Training debug_affectcols > test_pipeop_textvectorizer.R: 'as(, "dgTMatrix")' is deprecated. > test_pipeop_textvectorizer.R: Use 'as(., "TsparseMatrix")' instead. > test_pipeop_textvectorizer.R: See help("Deprecated") and help("Matrix-deprecated"). > test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. > test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. > test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. > test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. > test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. > test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. > test_pipeop_tunethreshold.R: OptimInstanceSingleCrit is deprecated. Use OptimInstanceBatchSingleCrit instead. [ FAIL 2 | WARN 0 | SKIP 100 | PASS 13055 ] ══ Skipped tests (100) ═════════════════════════════════════════════════════════ • On CRAN (96): 'test_CnfFormula_simplify.R:6:3', 'test_CnfFormula.R:591:3', 'test_Graph.R:283:3', 'test_PipeOp.R:32:1', 'test_GraphLearner.R:5:3', 'test_GraphLearner.R:221:3', 'test_GraphLearner.R:343:3', 'test_GraphLearner.R:408:3', 'test_GraphLearner.R:571:3', 'test_dictionary.R:7:3', 'test_learner_weightedaverage.R:5:3', 'test_learner_weightedaverage.R:57:3', 'test_learner_weightedaverage.R:105:3', 'test_learner_weightedaverage.R:152:3', 'test_meta.R:39:3', 'test_mlr_graphs_branching.R:26:3', 'test_mlr_graphs_bagging.R:6:3', 'test_mlr_graphs_robustify.R:5:3', 'test_pipeop_adas.R:8:3', 'test_pipeop_blsmote.R:8:3', 'test_pipeop_branch.R:4:3', 'test_pipeop_chunk.R:4:3', 'test_pipeop_classbalancing.R:7:3', 'test_pipeop_boxcox.R:7:3', 'test_pipeop_classweights.R:10:3', 'test_pipeop_colapply.R:9:3', 'test_pipeop_collapsefactors.R:6:3', 'test_pipeop_copy.R:5:3', 'test_pipeop_colroles.R:6:3', 'test_pipeop_decode.R:14:3', 'test_pipeop_encode.R:21:3', 'test_pipeop_encodeimpact.R:11:3', 'test_pipeop_datefeatures.R:10:3', 'test_pipeop_encodepl.R:5:3', 'test_pipeop_encodepl.R:72:3', 'test_pipeop_encodelmer.R:15:3', 'test_pipeop_encodelmer.R:37:3', 'test_pipeop_encodelmer.R:80:3', 'test_pipeop_featureunion.R:9:3', 'test_pipeop_featureunion.R:134:3', 'test_pipeop_filter.R:7:3', 'test_pipeop_fixfactors.R:9:3', 'test_pipeop_histbin.R:7:3', 'test_pipeop_ica.R:7:3', 'test_pipeop_ensemble.R:6:3', 'test_pipeop_impute.R:4:3', 'test_pipeop_imputelearner.R:43:3', 'test_pipeop_isomap.R:10:3', 'test_pipeop_kernelpca.R:9:3', 'test_pipeop_learner.R:17:3', 'test_pipeop_info.R:6:3', 'test_pipeop_learnerpicvplus.R:163:3', 'test_pipeop_missind.R:6:3', 'test_pipeop_modelmatrix.R:7:3', 'test_pipeop_learnercv.R:31:3', 'test_pipeop_multiplicityexply.R:9:3', 'test_pipeop_mutate.R:9:3', 'test_pipeop_nearmiss.R:7:3', 'test_pipeop_multiplicityimply.R:9:3', 'test_pipeop_ovr.R:9:3', 'test_pipeop_ovr.R:48:3', 'test_pipeop_pca.R:8:3', 'test_pipeop_proxy.R:14:3', 'test_pipeop_quantilebin.R:5:3', 'test_pipeop_randomprojection.R:6:3', 'test_pipeop_randomresponse.R:5:3', 'test_pipeop_removeconstants.R:6:3', 'test_pipeop_renamecolumns.R:6:3', 'test_pipeop_replicate.R:9:3', 'test_pipeop_rowapply.R:6:3', 'test_pipeop_scale.R:6:3', 'test_pipeop_scale.R:10:3', 'test_pipeop_scalemaxabs.R:6:3', 'test_pipeop_scalerange.R:7:3', 'test_pipeop_select.R:9:3', 'test_pipeop_smote.R:10:3', 'test_pipeop_smotenc.R:8:3', 'test_pipeop_nmf.R:6:3', 'test_pipeop_spatialsign.R:6:3', 'test_pipeop_targetinvert.R:4:3', 'test_pipeop_targetmutate.R:5:3', 'test_pipeop_targettrafo.R:4:3', 'test_pipeop_targettrafoscalerange.R:5:3', 'test_pipeop_subsample.R:6:3', 'test_pipeop_task_preproc.R:4:3', 'test_pipeop_task_preproc.R:14:3', 'test_pipeop_tomek.R:7:3', 'test_pipeop_textvectorizer.R:37:3', 'test_pipeop_textvectorizer.R:186:3', 'test_pipeop_unbranch.R:10:3', 'test_pipeop_updatetarget.R:89:3', 'test_pipeop_vtreat.R:9:3', 'test_pipeop_yeojohnson.R:7:3', 'test_pipeop_tunethreshold.R:111:3', 'test_pipeop_tunethreshold.R:191:3', 'test_typecheck.R:188:3' • Skipping (1): 'test_GraphLearner.R:1278:3' • empty test (3): 'test_pipeop_isomap.R:111:1', 'test_pipeop_missind.R:101:1', 'test_ppl.R:61:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test_filter_ensemble.R:294:3'): FilterEnsemble ignores NA scores from wrapped filters ── Expected `all(is.nan(permutation_filter$scores[task$feature_names]))` to be TRUE. Differences: `actual`: FALSE `expected`: TRUE ── Failure ('test_filter_ensemble.R:307:3'): FilterEnsemble ignores NA scores from wrapped filters ── Expected `all.equal(object, expected, check.environment = FALSE, ...)` to be TRUE. Differences: `actual` is a character vector ('Mean relative difference: 0.2767692') `expected` is a logical vector (TRUE) Backtrace: ▆ 1. └─global expect_equal(combined_scores, variance_scores * weights[["variance"]]) at test_filter_ensemble.R:307:3 2. └─testthat::expect_true(...) [ FAIL 2 | WARN 0 | SKIP 100 | PASS 13055 ] Error: ! Test failures. Execution halted Package: papaja Check: tests New result: ERROR Running ‘spelling.R’ [0s/0s] Running ‘testthat.R’ [30s/31s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library("testthat") > library("papaja") Loading required package: tinylabels > > test_check("papaja") # weights: 11 (10 variable) initial value 131.004817 iter 10 value 18.950613 iter 20 value 1.688044 iter 30 value 0.191850 iter 40 value 0.157681 iter 50 value 0.144956 iter 60 value 0.140122 iter 70 value 0.126855 iter 80 value 0.121665 iter 90 value 0.119362 iter 100 value 0.114966 final value 0.114966 stopped after 100 iterations Saving _problems/test_apa_print_merMod-160.R [ FAIL 2 | WARN 0 | SKIP 10 | PASS 2327 ] ══ Skipped tests (10) ══════════════════════════════════════════════════════════ • On CRAN (7): 'test-generate_author_yml.R:18:5', 'test_apa_print_merMod.R:6:5', 'test_apa_print_merMod.R:117:5', 'test_apa_print_model_comp.R:83:5', 'test_apa_table.R:19:5', 'test_custom_effect_sizes.R:109:5', 'test_skeleton.R:4:5' • Structure of glht output not decided, yet. (2): 'test_apa_print_glht.R:7:5', 'test_apa_print_glht.R:36:5' • emtrends() is not yet supported. (1): 'test_apa_print_emm_lsm.R:936:5' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test_apa_print_merMod.R:160:5'): ANOVA tables from lmerTest::anova() ── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. └─lmerTest::lmer(Reaction ~ Days + (Days | Subject), lme4::sleepstudy) at test_apa_print_merMod.R:160:5 2. └─lmerTest:::as_lmerModLT(model, devfun) ── Error ('test_apa_print_merMod.R:217:7'): Type-3 tables from afex::mixed ───── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─testthat::capture_output(...) at test_apa_print_merMod.R:216:5 2. │ └─testthat::capture_output_lines(code, print, width = width) 3. │ └─testthat:::eval_with_output(code, print = print, width = width) 4. │ ├─withr::with_output_sink(path, withVisible(code)) 5. │ │ └─base::force(code) 6. │ └─base::withVisible(code) 7. └─afex::mixed(...) at test_apa_print_merMod.R:217:7 8. ├─base::eval(mf) 9. │ └─base::eval(mf) 10. └─lmerTest::lmer(formula = yield ~ N * P + (1 | block), data = data) 11. └─lmerTest:::as_lmerModLT(model, devfun) [ FAIL 2 | WARN 0 | SKIP 10 | PASS 2327 ] Error: ! Test failures. Execution halted Package: parameters Check: tests New result: ERROR Running ‘testthat.R’ [144s/81s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(parameters) > library(testthat) > > test_check("parameters") Starting 2 test processes. > test-include_reference.R: Your model may suffer from singularity (see `?lme4::isSingular` and > test-include_reference.R: `?performance::check_singularity`). > test-include_reference.R: Some of the confidence intervals of the random effects parameters are > test-include_reference.R: probably not meaningful! > test-include_reference.R: You may try to impose a prior on the random effects parameters, e.g. > test-include_reference.R: using the glmmTMB package. > test-include_reference.R: Your model may suffer from singularity (see `?lme4::isSingular` and > test-include_reference.R: `?performance::check_singularity`). > test-include_reference.R: Some of the confiden > test-include_reference.R: ce intervals of the random effects parameters are > test-include_reference.R: probably not meaningful! > test-include_reference.R: You may try to impose a prior on the random effects parameters, e.g. > test-include_reference.R: using the glmmTMB package. > test-model_parameters.afex_aov.R: Contrasts set to contr.sum for the following variables: condition, talk > test-model_parameters.afex_aov.R: Contrasts set to contr.sum for the following variables: condition, talk > test-model_parameters.afex_aov.R: Contrasts set to contr.sum for the following variables: treatment, gender Saving _problems/test-model_parameters_df_method-15.R > test-n_factors.R: [1] > test-n_factors.R: "# Method Agreement Procedure:" > test-n_factors.R: [2] "" > test-n_factors.R: [3] "The choice of 1 dimensions is supported by 11 (84.62%) methods out of 13 (Bartlett, Anderson, Lawley, Optimal coordinates, Acceleration factor, Parallel analysis, Kaiser criterion, Scree (SE), Scree (R2), VSS complexity 1, Velicer's MAP)." > test-n_factors.R: [1] "# Method Agreement Procedure:" > test-n_factors.R: [2] "" > test-n_factors.R: [3] "The choice of 1 dimensions is supported by 3 (60.00%) methods out of 5 (Velicer's MAP, BIC, BIC (adjusted))." [ FAIL 1 | WARN 0 | SKIP 129 | PASS 667 ] ══ Skipped tests (129) ═════════════════════════════════════════════════════════ • On CRAN (120): 'test-GLMMadaptive.R:1:1', 'test-Hmisc.R:1:1', 'test-averaging.R:1:1', 'test-backticks.R:1:1', 'test-bootstrap_emmeans.R:1:1', 'test-bootstrap_parameters.R:1:1', 'test-brms.R:1:1', 'test-compare_parameters.R:90:5', 'test-compare_parameters.R:95:5', 'test-complete_separation.R:4:1', 'test-complete_separation.R:18:1', 'test-complete_separation.R:28:1', 'test-coxph.R:69:1', 'test-efa.R:1:1', 'test-emmGrid-df_colname.R:1:1', 'test-equivalence_test.R:3:1', 'test-equivalence_test.R:13:1', 'test-equivalence_test.R:22:3', 'test-equivalence_test.R:112:3', 'test-factor_analysis.R:2:3', 'test-factor_analysis.R:124:3', 'test-format_model_parameters2.R:2:3', 'test-gam.R:30:1', 'test-get_scores.R:1:1', 'test-glmer.R:1:1', 'test-glmmTMB-2.R:1:1', 'test-glmmTMB-profile_CI.R:2:3', 'test-glmmTMB.R:1:1', 'test-group_level_total.R:2:1', 'test-helper.R:1:1', 'test-ivreg.R:45:1', 'test-lcmm.R:1:1', 'test-lmerTest.R:1:1', 'test-include_reference.R:4:1', 'test-include_reference.R:62:1', 'test-include_reference.R:110:1', 'test-mipo.R:5:1', 'test-mipo.R:23:1', 'test-mmrm.R:1:1', 'test-model_parameters.anova.R:1:1', 'test-model_parameters.aov.R:1:1', 'test-model_parameters.aov_es_ci.R:183:3', 'test-model_parameters.aov_es_ci.R:294:3', 'test-model_parameters.aov_es_ci.R:344:3', 'test-model_parameters.aov_es_ci.R:397:3', 'test-model_parameters.bracl.R:1:1', 'test-model_parameters.cgam.R:1:1', 'test-model_parameters.coxme.R:1:1', 'test-marginaleffects.R:170:1', 'test-marginaleffects.R:199:3', 'test-model_parameters.epi2x2.R:1:1', 'test-model_parameters.efa_cfa.R:30:3', 'test-model_parameters.fixest_multi.R:1:1', 'test-model_parameters.fixest.R:2:3', 'test-model_parameters.fixest.R:77:3', 'test-model_parameters.fixest.R:145:1', 'test-model_parameters.glmgee.R:1:1', 'test-model_parameters.logistf.R:1:1', 'test-model_parameters.logitr.R:1:1', 'test-model_parameters.mclogit.R:1:1', 'test-model_parameters.mediate.R:1:1', 'test-model_parameters.glm.R:35:1', 'test-model_parameters.glm.R:67:1', 'test-model_parameters.mixed.R:2:1', 'test-model_parameters.nnet.R:5:1', 'test-model_parameters.vgam.R:3:1', 'test-model_parameters_df.R:1:1', 'test-model_parameters_ordinal.R:1:1', 'test-model_parameters_random_pars.R:1:1', 'test-model_parameters_std.R:1:1', 'test-model_parameters_std_mixed.R:1:1', 'test-n_factors.R:10:3', 'test-n_factors.R:26:3', 'test-n_factors.R:76:3', 'test-p_adjust.R:1:1', 'test-p_direction.R:1:1', 'test-p_significance.R:1:1', 'test-p_value.R:14:1', 'test-panelr.R:1:1', 'test-pipe.R:1:1', 'test-pca.R:64:1', 'test-polr.R:1:1', 'test-plm.R:97:1', 'test-posterior.R:1:1', 'test-pool_parameters.R:1:1', 'test-pool_parameters.R:32:1', 'test-print_AER_labels.R:5:1', 'test-printing-stan.R:1:1', 'test-printing.R:1:1', 'test-pretty_names.R:40:1', 'test-pretty_names.R:73:5', 'test-quantreg.R:1:1', 'test-random_effects_ci-glmmTMB.R:3:1', 'test-random_effects_ci.R:1:1', 'test-robust.R:1:1', 'test-rstanarm.R:2:1', 'test-sampleSelection.R:2:1', 'test-serp.R:5:1', 'test-printing2.R:14:5', 'test-printing2.R:21:5', 'test-printing2.R:26:5', 'test-printing2.R:31:5', 'test-printing2.R:36:5', 'test-printing2.R:48:5', 'test-printing2.R:91:7', 'test-printing2.R:126:5', 'test-svylme.R:1:1', 'test-svyolr.R:1:1', 'test-visualisation_recipe.R:1:1', 'test-weightit.R:6:1', 'test-weightit.R:26:1', 'test-wrs2.R:55:1', 'test-standardize_parameters.R:28:1', 'test-standardize_parameters.R:36:3', 'test-standardize_parameters.R:61:3', 'test-standardize_parameters.R:173:3', 'test-standardize_parameters.R:297:3', 'test-standardize_parameters.R:332:3', 'test-standardize_parameters.R:425:3', 'test-standardize_parameters.R:515:3' • On Linux (4): 'test-model_parameters.BFBayesFactor.R:1:1', 'test-nestedLogit.R:78:3', 'test-simulate_model.R:1:1', 'test-simulate_parameters.R:1:1' • TODO: check this test locally, fails on CI, probably due to scoping issues? (1): 'test-marginaleffects.R:280:3' • TODO: fix this test (1): 'test-model_parameters.lqmm.R:40:3' • TODO: this one actually is not correct. (1): 'test-model_parameters_robust.R:127:3' • empty test (2): 'test-wrs2.R:69:1', 'test-wrs2.R:81:1' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-model_parameters_df_method.R:12:1'): (code run outside of `test_that()`) ── Error in `forceNewMerMod(res, reference = model)`: could not find function "forceNewMerMod" Backtrace: ▆ 1. ├─base::suppressMessages(...) at test-model_parameters_df_method.R:12:1 2. │ └─base::withCallingHandlers(...) 3. └─lmerTest::lmer(...) 4. └─lmerTest:::as_lmerModLT(model, devfun) [ FAIL 1 | WARN 0 | SKIP 129 | PASS 667 ] Error: ! Test failures. Execution halted Package: readyomics Check: examples New result: ERROR Running examples in ‘readyomics-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: dana > ### Title: Differential analysis (dana) > ### Aliases: dana > > ### ** Examples > > mock_X <- matrix( + rnorm(50 * 10) + + rep(c(rep(0, 25), rep(2, 25)), each = 10) * rep(1:10 %in% 1:3, each = 50), + nrow = 50 + ) > > rownames(mock_X) <- paste0("sample", 1:50) > colnames(mock_X) <- paste0("feat", 1:10) > > sample_data <- data.frame( + sample_id = rownames(mock_X), + group = factor(rep(c("A", "B"), each = 25)), + subject = factor(rep(1:25, each = 2)), + row.names = rownames(mock_X) + ) > > # Example with parallel computation setup (not run) > # future::plan(multisession) > # progressr::handlers(global = TRUE) > # progressr::with_progress({ > result <- dana(X = mock_X, + sample_data = sample_data, + formula_rhs = ~ group + (1 | subject), + term_LRT = c("group", "1 | subject"), # Multiple terms allowed + platform = "ms", + assay = "lipidomics", + verbose = FALSE + ) Warning: the ‘findbars’ function has moved to the reformulas package. Please update your imports, or ask an upstream package maintainter to do so. This warning is displayed once per session. Warning in value[[3L]](cond) : Model failed for feature 1 : could not find function "forceNewMerMod" boundary (singular) fit: see help('isSingular') Warning in value[[3L]](cond) : Model failed for feature 2 : could not find function "forceNewMerMod" boundary (singular) fit: see help('isSingular') Warning in value[[3L]](cond) : Model failed for feature 3 : could not find function "forceNewMerMod" boundary (singular) fit: see help('isSingular') Warning in value[[3L]](cond) : Model failed for feature 4 : could not find function "forceNewMerMod" boundary (singular) fit: see help('isSingular') Warning in value[[3L]](cond) : Model failed for feature 5 : could not find function "forceNewMerMod" boundary (singular) fit: see help('isSingular') Warning in value[[3L]](cond) : Model failed for feature 6 : could not find function "forceNewMerMod" Warning in value[[3L]](cond) : Model failed for feature 7 : could not find function "forceNewMerMod" Warning in value[[3L]](cond) : Model failed for feature 8 : could not find function "forceNewMerMod" boundary (singular) fit: see help('isSingular') Warning in value[[3L]](cond) : Model failed for feature 9 : could not find function "forceNewMerMod" boundary (singular) fit: see help('isSingular') Warning in value[[3L]](cond) : Model failed for feature 10 : could not find function "forceNewMerMod" Error in `$<-.data.frame`(x, name, value) : replacement has 1 row, data has 0 Calls: dana -> $<- -> $<-.data.table -> $<-.data.frame Execution halted Examples with CPU (user + system) or elapsed time > 5s user system elapsed build_phyloseq 7.676 0.391 8.154 Package: readyomics Check: tests New result: ERROR Running ‘testthat.R’ [26s/26s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # This file is part of the standard setup for testthat. > # It is recommended that you do not modify it. > # > # Where should you do additional test configuration? > # Learn more about the roles of various files in: > # * https://r-pkgs.org/testing-design.html#sec-tests-files-overview > # * https://testthat.r-lib.org/articles/special-files.html > > library(testthat) > library(readyomics) > > test_check("readyomics") Error in smooth.spline(lambda, pi0, df = smooth.df) : missing or infinite values in inputs are not allowed Joining with `by = join_by(feat_id)` Only 1 bin; IHW reduces to Benjamini Hochberg (uniform weights) 2 samples found in common between 2 rows in 'X' and 2 rows in 'sample_data'. boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') boundary (singular) fit: see help('isSingular') Saving _problems/test-dana-60.R PCA 10 samples x 4 variables standard scaling of predictors R2X(cum) pre ort Total 0.817 2 0 PCA 10 samples x 4 variables standard scaling of predictors R2X(cum) pre ort Total 0.797 2 0 10 samples found in common between 10 rows in 'X' and 10 rows in 'sample_data'. 10 samples found in common between 10 rows in 'X' and 10 rows in 'sample_data'. PCA 10 samples x 4 variables standard scaling of predictors R2X(cum) pre ort Total 0.813 2 0 10 samples found in common between 10 rows in 'X' and 10 rows in 'sample_data'. Permutation parameters for group have not been specified. Default 'perm_control$joint_terms' will be used. 10 samples found in common between 10 rows in 'X' and 10 rows in 'sample_data'. Permutation parameters for group have not been specified. Default 'perm_control$joint_terms' will be used. 10 samples found in common between 10 rows in 'X' and 10 rows in 'sample_data'. Permutation parameters for group have not been specified. Default 'perm_control$joint_terms' will be used. 2 out of 4 features were kept after 80 % prevalence filter. 4 out of 4 features were kept after 80 % prevalence filter. 4 out of 4 features were kept after 80 % prevalence filter. [ FAIL 1 | WARN 5 | SKIP 0 | PASS 132 ] ══ Failed tests ════════════════════════════════════════════════════════════════ ── Error ('test-dana.R:53:3'): dana handles LRT terms correctly ──────────────── Error in ``$<-.data.frame`(x, name, value)`: replacement has 1 row, data has 0 Backtrace: ▆ 1. └─readyomics::dana(...) at test-dana.R:53:3 2. ├─base::`$<-`(`*tmp*`, "platform", value = ``) 3. └─data.table:::`$<-.data.table`(`*tmp*`, "platform", value = ``) 4. └─base::`$<-.data.frame`(x, name, value) [ FAIL 1 | WARN 5 | SKIP 0 | PASS 132 ] Error: ! Test failures. Execution halted Package: REndo Check: tests New result: ERROR Running ‘testthat.R’ [121s/122s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > library(testthat) > library(REndo) > library(Formula) > > test_check("REndo") Saving _problems/test-correctness_multilevel-253.R Saving _problems/test-inputchecks_multilevel-112.R Saving _problems/test-inputchecks_multilevel-113.R | | | 0% | |=================================== | 50% | |======================================================================| 100% | | | 0% | |=================================== | 50% | |======================================================================| 100% | | | 0% | |=================================== | 50% | |======================================================================| 100% | | | 0% | |=================================== | 50% | |======================================================================| 100% [ FAIL 3 | WARN 0 | SKIP 30 | PASS 2152 ] ══ Skipped tests (30) ══════════════════════════════════════════════════════════ • On CRAN (30): 'test-correctness_copulacorrections.R:14:3', 'test-correctness_copulacorrections.R:90:3', 'test-correctness_copulacorrections.R:204:3', 'test-correctness_latentIV.R:10:3', 'test-correctness_multilevel.R:12:3', 'test-correctness_multilevel.R:62:3', 'test-correctness_multilevel.R:120:3', 'test-correctness_multilevel.R:151:3', 'test-correctness_multilevel.R:185:3', 'test-correctness_multilevel.R:213:3', 'test-correctness_multilevel.R:228:3', 'test-correctness_multilevel.R:293:3', 'test-runability_copulacorrection.R:12:3', 'test-runability_copulacorrection.R:30:3', 'test-runability_copulacorrection.R:90:3', 'test-runability_copulacorrection.R:184:3', 'test-runability_copulacorrection.R:204:3', 'test-runability_copulacorrection.R:219:3', 'test-runability_copulacorrection.R:303:3', 'test-runability_copulacorrection.R:316:3', 'test-runability_multilevel.R:22:3', 'test-runability_multilevel.R:61:3', 'test-runability_multilevel.R:75:3', 'test-runability_multilevel.R:93:3', 'test-runability_multilevel.R:103:3', 'test-runability_multilevel.R:112:3', 'test-runability_multilevel.R:124:3', 'test-runability_multilevel.R:136:3', 'test-runability_multilevel.R:157:3', 'test-s3methods_copualcorrection.R:46:3' ══ Failed tests ════════════════════════════════════════════════════════════════ ── Failure ('test-correctness_multilevel.R:251:1'): (code run outside of `test_that()`) ── Expected `... <- NULL` to run silently. Actual noise: warnings. ── Failure ('test-inputchecks_multilevel.R:112:3'): Fail if no slope provided ── `multilevelIV(...)` threw an error with unexpected message. Expected match: "The above errors were encountered!" Actual message: "error in evaluating the argument 'x' in selecting a method for function 'forceSymmetric': non-conformable matrix dimensions in .Arith.Csparse(e1, e2, .Generic, class. = \"dgCMatrix\")" Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-inputchecks_multilevel.R:112:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. ├─REndo::multilevelIV(...) 7. │ └─REndo:::multilevel_3levels(...) 8. │ ├─... + Matrix::bdiag(l.L2.V.part) 9. │ └─... + Matrix::bdiag(l.L2.V.part) 10. │ ├─Matrix::forceSymmetric(callGeneric(.M2gen(e1), .M2gen(e2))) 11. │ ├─methods::callGeneric(.M2gen(e1), .M2gen(e2)) 12. │ │ └─base::eval(call, parent.frame()) 13. │ │ └─base::eval(call, parent.frame()) 14. │ ├─.M2gen(e1) + .M2gen(e2) 15. │ └─.M2gen(e1) + .M2gen(e2) 16. │ └─Matrix:::.Arith.Csparse(e1, e2, .Generic, class. = "dgCMatrix") 17. │ └─Matrix:::.Ops.checkDim(dim(e1), dim(e2)) 18. │ └─base::stop(...) 19. └─base::.handleSimpleError(...) 20. └─base (local) h(simpleError(msg, call)) ── Failure ('test-inputchecks_multilevel.R:113:3'): Fail if no slope provided ── `multilevelIV(...)` threw an error with unexpected message. Expected match: "The above errors were encountered!" Actual message: "non-conformable matrix dimensions in .diag2T.smart(e1, e2, kind = \"d\") + e2" Backtrace: ▆ 1. ├─testthat::expect_error(...) at test-inputchecks_multilevel.R:113:3 2. │ └─testthat:::quasi_capture(...) 3. │ ├─testthat (local) .capture(...) 4. │ │ └─base::withCallingHandlers(...) 5. │ └─rlang::eval_bare(quo_get_expr(.quo), quo_get_env(.quo)) 6. └─REndo::multilevelIV(...) 7. └─REndo:::multilevel_3levels(...) 8. ├─Matrix::Diagonal(sigma.sq, n = nrow(X)) + ... 9. └─Matrix::Diagonal(sigma.sq, n = nrow(X)) + ... 10. ├─methods::callGeneric(.diag2T.smart(e1, e2, kind = "d"), e2) 11. │ └─base::eval(call, parent.frame()) 12. │ └─base::eval(call, parent.frame()) 13. ├─.diag2T.smart(e1, e2, kind = "d") + e2 14. └─.diag2T.smart(e1, e2, kind = "d") + e2 15. └─Matrix:::.Ops.checkDim(dim(e1), dim(e2)) [ FAIL 3 | WARN 0 | SKIP 30 | PASS 2152 ] Error: ! Test failures. Execution halted Package: robustlmm Check: tests New result: ERROR Running ‘DAS-scale.R’ [0s/0s] Running ‘DASvar-fallback.R’ [5s/5s] Running ‘PsiFunction.R’ [7s/8s] Running ‘asymptoticEfficiency.R’ [4s/4s] Running ‘compare-methods.R’ [10s/12s] Comparing ‘compare-methods.Rout’ to ‘compare-methods.Rout.save’ ... OK Running ‘expectations.R’ [0s/1s] Running ‘fitDatasets.R’ [20s/22s] Running ‘genericFunctions.R’ [4s/5s] Running ‘getME.R’ [4s/5s] Comparing ‘getME.Rout’ to ‘getME.Rout.save’ ... OK Running ‘multipleGroupingFactorsTestData.R’ [0s/0s] Running ‘offset.R’ [5s/6s] Running ‘psi-rho-funs.R’ [4s/4s] Running ‘rlmerMod.R’ [0s/0s] Running ‘simulationStudies.R’ [3s/4s] Running ‘subset.R’ [0s/0s] Running ‘tau.R’ [0s/0s] Running ‘testMatrices.R’ [0s/1s] Running ‘test_generateLongitudinalDatasets.R’ [0s/1s] Running ‘weights.R’ [7s/8s] Running the tests in ‘tests/genericFunctions.R’ failed. Complete output: > ## test the availability of generic functions. > ae <- function(target, current, ...) { + ret <- all.equal(target, current, ...) + if (isTRUE(ret)) return(ret) + print(ret) + stop("Objects not equal") + } > chgClass <- function(object) { + class(object) <- sub("(merMod|mer)", "rlmerMod", class(object)) + object + } > > require(robustlmm) Loading required package: robustlmm Loading required package: lme4 Loading required package: Matrix > > > set.seed(3) > sleepstudy2 <- within(sleepstudy, { + Group <- letters[1:4] + Covar <- rnorm(180) + }) > rfm <- rlmer(Reaction ~ Days + (Days|Subject) + (1|Group), sleepstudy2, + rho.e = cPsi, rho.b = cPsi, doFit=FALSE) boundary (singular) fit: see help('isSingular') > fm <- lmer(Reaction ~ Days + (Days|Subject) + (1|Group), sleepstudy2) boundary (singular) fit: see help('isSingular') > ## all three print the same: > print(rfm) Unfitted rlmerMod object. Use update(object, doFit=TRUE) to fit it. > show(rfm) Unfitted rlmerMod object. Use update(object, doFit=TRUE) to fit it. > summary(rfm) Unfitted rlmerMod object. Use update(object, doFit=TRUE) to fit it. > > ## object information > ## ae(df.residual(fm), df.residual(rfm)) > ae(formula(fm), formula(rfm)) [1] TRUE > stopifnot(isLMM(rfm)) > stopifnot(isREML(rfm)) > ae(model.frame(fm), model.frame(rfm)) [1] TRUE > ae(model.matrix(fm), model.matrix(rfm), check.attributes=FALSE) [1] TRUE > nobs(rfm) [1] 180 > ## ae(getInitial(fm), getInitial(rfm)) > ae(terms(fm), terms(rfm)) [1] TRUE > weights(rfm) [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [38] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [75] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [112] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 [149] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 > > ## basic accessors for the results > ae(chgClass(coef(fm)), coef(rfm)) [1] TRUE > ## dummy.coef(rfm) > stopifnot(inherits(try(deviance(rfm), silent=TRUE), "try-error")) > stopifnot(inherits(try(extractAIC(rfm), silent=TRUE), "try-error")) > family(rfm) Family: gaussian Link function: identity > ## after version 1.1 fitted values are named > if (packageVersion("lme4") > "1.1") ae(fitted(fm), fitted(rfm)) [1] TRUE > ae(fixef(fm), fixef(rfm)) [1] TRUE > stopifnot(inherits(try(logLik(rfm), silent=TRUE), "try-error")) > ae(chgClass(ranef(fm, condVar=FALSE)), ranef(rfm)) [1] TRUE > ## after version 1.1 fitted values are named > if (packageVersion("lme4") > "1.1") ae(resid(fm), resid(rfm)) [1] TRUE > ae(sigma(fm), sigma(rfm)) [1] TRUE > ## weighted.residuals(rfm) > > ## var-covar methods > ## VarCorr(rfm) > ae(chgClass(VarCorr(fm)), VarCorr(rfm)) [1] "Component \"Subject\": Attributes: < Component \"theta\": names for current but not for target >" [2] "Component \"Group\": Attributes: < Component \"profpar\": names for current but not for target >" [3] "Component \"Group\": Attributes: < Component \"theta\": names for current but not for target >" Error in ae(chgClass(VarCorr(fm)), VarCorr(rfm)) : Objects not equal Execution halted Package: rockchalk Check: examples New result: ERROR Running examples in ‘rockchalk-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: outreg > ### Title: Creates a publication quality result table for regression > ### models. Works with models fitted with lm, glm, as well as lme4. > ### Aliases: outreg > ### Keywords: regression > > ### ** Examples > > set.seed(2134234) > dat <- data.frame(x1 = rnorm(100), x2 = rnorm(100)) > dat$y1 <- 30 + 5 * rnorm(100) + 3 * dat$x1 + 4 * dat$x2 > dat$y2 <- rnorm(100) + 5 * dat$x2 > m1 <- lm(y1 ~ x1, data = dat) > m2 <- lm(y1 ~ x2, data = dat) > m3 <- lm(y1 ~ x1 + x2, data = dat) > gm1 <- glm(y1 ~ x1, family = Gamma, data = dat) > outreg(m1, title = "My One Tightly Printed Regression", float = TRUE) \begin{table} \caption{My One Tightly Printed Regression}\label{regrlabl} \begin{tabular}{@{}l*{2}{l}@{}} \hline &\multicolumn{1}{l}{M1 }\tabularnewline &\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** \tabularnewline &(0.618)\tabularnewline x1 & 1.546* \tabularnewline &(0.692)\tabularnewline \hline N&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121\tabularnewline $R^2$&0.048\tabularnewline \hline \hline \multicolumn{2}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > ex1 <- outreg(m1, title = "My One Tightly Printed Regression", + float = TRUE, print.results = FALSE, centering = "siunitx") > ## Show markup, Save to file with cat() > cat(ex1) \begin{table} \caption{My One Tightly Printed Regression}\label{regrlabl} \begin{tabular}{@{}l*{1}{S[ input-symbols = ( ), group-digits = false, table-number-alignment = center, %table-space-text-pre = (, table-align-text-pre = false, table-align-text-post = false, table-space-text-post = {***}, parse-units = false]}@{}} \hline &\multicolumn{1}{c}{M1 }\tabularnewline &\multicolumn{1}{c}{Estimate}\tabularnewline &\multicolumn{1}{c}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** \tabularnewline &(0.618)\tabularnewline x1 & 1.546* \tabularnewline &(0.692)\tabularnewline \hline N&\multicolumn{1}{c}{100} \tabularnewline RMSE&6.121\tabularnewline $R^2$&0.048\tabularnewline \hline \hline \multicolumn{2}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > ## cat(ex1, file = "ex1.tex") > > ex2 <- outreg(list("Fingers" = m1), tight = FALSE, + title = "My Only Spread Out Regressions", float = TRUE, + alpha = c(0.05, 0.01, 0.001)) \begin{table} \caption{My Only Spread Out Regressions}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{2}{l}{Fingers }\tabularnewline &\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & (0.618) \tabularnewline x1 & 1.546* & (0.692) \tabularnewline \hline N&\multicolumn{1}{l}{100} & \tabularnewline RMSE&6.121\tabularnewline $R^2$&0.048\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > ex3 <- outreg(list("Model A" = m1, "Model B label with Spaces" = m2), + varLabels = list(x1 = "Billie"), + title = "My Two Linear Regressions", request = c(fstatistic = "F"), + print.results = TRUE) \begin{table} \caption{My Two Linear Regressions}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{Model A } &\multicolumn{1}{l}{Model B label with Spaces }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** \tabularnewline &(0.618)&(0.522)\tabularnewline Billie & 1.546* &\multicolumn{1}{l}{\_ }\tabularnewline &(0.692) &\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** \tabularnewline &&(0.512)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{4.98(1,98)*} &\multicolumn{1}{c}{44.4(1,98)***}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > cat(ex3) \begin{table} \caption{My Two Linear Regressions}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{Model A } &\multicolumn{1}{l}{Model B label with Spaces }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** \tabularnewline &(0.618)&(0.522)\tabularnewline Billie & 1.546* &\multicolumn{1}{l}{\_ }\tabularnewline &(0.692) &\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** \tabularnewline &&(0.512)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{4.98(1,98)*} &\multicolumn{1}{c}{44.4(1,98)***}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > ex4 <- outreg(list("Model A" = m1, "Model B" = m2), + modelLabels = c("Overrides ModelA", "Overrides ModelB"), + varLabels = list(x1 = "Billie"), + title = "Note modelLabels Overrides model names") \begin{table} \caption{Note modelLabels Overrides model names}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{Overrides ModelA } &\multicolumn{1}{l}{Overrides ModelB }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** \tabularnewline &(0.618)&(0.522)\tabularnewline Billie & 1.546* &\multicolumn{1}{l}{\_ }\tabularnewline &(0.692) &\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** \tabularnewline &&(0.512)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > cat(ex4) \begin{table} \caption{Note modelLabels Overrides model names}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{Overrides ModelA } &\multicolumn{1}{l}{Overrides ModelB }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** \tabularnewline &(0.618)&(0.522)\tabularnewline Billie & 1.546* &\multicolumn{1}{l}{\_ }\tabularnewline &(0.692) &\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** \tabularnewline &&(0.512)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > ##' > ex5 <- outreg(list("Whichever" = m1, "Whatever" = m2), + title = "Still have showAIC argument, as in previous versions", + showAIC = TRUE, float = TRUE, centering = "siunitx") \begin{table} \caption{Still have showAIC argument, as in previous versions}\label{regrlabl} \begin{tabular}{@{}l*{2}{S[ input-symbols = ( ), group-digits = false, table-number-alignment = center, %table-space-text-pre = (, table-align-text-pre = false, table-align-text-post = false, table-space-text-post = {***}, parse-units = false]}@{}} \hline &\multicolumn{1}{c}{Whichever } &\multicolumn{1}{c}{Whatever }\tabularnewline &\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{Estimate}\tabularnewline &\multicolumn{1}{c}{(S.E.)}&\multicolumn{1}{c}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** \tabularnewline &(0.618)&(0.522)\tabularnewline x1 & 1.546* &\multicolumn{1}{c}{\_ }\tabularnewline &(0.692) &\tabularnewline x2 &\multicolumn{1}{c}{\_ }& 3.413*** \tabularnewline &&(0.512)\tabularnewline \hline N&\multicolumn{1}{c}{100}&\multicolumn{1}{c}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline AIC&650.109 &617.694\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > ex5s <- outreg(list("Whichever" = m1, "Whatever" = m2), + title = "Still have showAIC argument, as in previous versions", + showAIC = TRUE, float = TRUE, centering = "siunitx") \begin{table} \caption{Still have showAIC argument, as in previous versions}\label{regrlabl} \begin{tabular}{@{}l*{2}{S[ input-symbols = ( ), group-digits = false, table-number-alignment = center, %table-space-text-pre = (, table-align-text-pre = false, table-align-text-post = false, table-space-text-post = {***}, parse-units = false]}@{}} \hline &\multicolumn{1}{c}{Whichever } &\multicolumn{1}{c}{Whatever }\tabularnewline &\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{Estimate}\tabularnewline &\multicolumn{1}{c}{(S.E.)}&\multicolumn{1}{c}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** \tabularnewline &(0.618)&(0.522)\tabularnewline x1 & 1.546* &\multicolumn{1}{c}{\_ }\tabularnewline &(0.692) &\tabularnewline x2 &\multicolumn{1}{c}{\_ }& 3.413*** \tabularnewline &&(0.512)\tabularnewline \hline N&\multicolumn{1}{c}{100}&\multicolumn{1}{c}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline AIC&650.109 &617.694\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > > ex6 <- outreg(list("Whatever" = m1, "Whatever" =m2), + title = "Another way to get AIC output", + runFuns = c("AIC" = "Akaike IC")) \begin{table} \caption{Another way to get AIC output}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{Whatever } &\multicolumn{1}{l}{Whatever }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 30.245*** \tabularnewline &(0.618)&(0.618)\tabularnewline x1 & 1.546* & 1.546* \tabularnewline &(0.692)&(0.692)\tabularnewline x2 &\multicolumn{1}{l}{\_ }&\multicolumn{1}{l}{\_ }\tabularnewline & &\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline Akaike IC&\multicolumn{1}{c}{650.11} &\multicolumn{1}{c}{617.69}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > cat(ex6) \begin{table} \caption{Another way to get AIC output}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{Whatever } &\multicolumn{1}{l}{Whatever }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 30.245*** \tabularnewline &(0.618)&(0.618)\tabularnewline x1 & 1.546* & 1.546* \tabularnewline &(0.692)&(0.692)\tabularnewline x2 &\multicolumn{1}{l}{\_ }&\multicolumn{1}{l}{\_ }\tabularnewline & &\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205\tabularnewline $R^2$&0.048 &0.312\tabularnewline adj $R^2$&0.039 &0.305\tabularnewline Akaike IC&\multicolumn{1}{c}{650.11} &\multicolumn{1}{c}{617.69}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > ex7 <- outreg(list("Amod" = m1, "Bmod" = m2, "Gmod" = m3), + title = "My Three Linear Regressions", float = FALSE) \begin{table} \caption{My Three Linear Regressions}\label{regrlabl} \begin{tabular}{@{}l*{4}{l}@{}} \hline &\multicolumn{1}{l}{Amod } &\multicolumn{1}{l}{Bmod } &\multicolumn{1}{l}{Gmod }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** & 30.013*** \tabularnewline &(0.618)&(0.522)&(0.490)\tabularnewline x1 & 1.546* &\multicolumn{1}{l}{\_ }& 2.217*** \tabularnewline &(0.692) &&(0.555)\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** & 3.717*** \tabularnewline &&(0.512)&(0.483)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205 &4.849\tabularnewline $R^2$&0.048 &0.312 &0.409\tabularnewline adj $R^2$&0.039 &0.305 &0.397\tabularnewline \hline \hline \multicolumn{4}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > cat(ex7) \begin{table} \caption{My Three Linear Regressions}\label{regrlabl} \begin{tabular}{@{}l*{4}{l}@{}} \hline &\multicolumn{1}{l}{Amod } &\multicolumn{1}{l}{Bmod } &\multicolumn{1}{l}{Gmod }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** & 30.013*** \tabularnewline &(0.618)&(0.522)&(0.490)\tabularnewline x1 & 1.546* &\multicolumn{1}{l}{\_ }& 2.217*** \tabularnewline &(0.692) &&(0.555)\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** & 3.717*** \tabularnewline &&(0.512)&(0.483)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205 &4.849\tabularnewline $R^2$&0.048 &0.312 &0.409\tabularnewline adj $R^2$&0.039 &0.305 &0.397\tabularnewline \hline \hline \multicolumn{4}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > ## A new feature in 1.85 is ability to provide vectors of beta estimates > ## standard errors, and p values if desired. > ## Suppose you have robust standard errors! > if (require(car)){ + newSE <- sqrt(diag(car::hccm(m3))) + ex8 <- outreg(list("Model A" = m1, "Model B" = m2, "Model C" = m3, "Model C w Robust SE" = m3), + SElist= list("Model C w Robust SE" = newSE)) + cat(ex8) + } Loading required package: car Loading required package: carData \begin{tabular}{@{}l*{5}{l}@{}} \hline &\multicolumn{1}{l}{Model A } &\multicolumn{1}{l}{Model B } &\multicolumn{1}{l}{Model C } &\multicolumn{1}{l}{Model C w Robust SE }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** & 30.013*** & 30.013*** \tabularnewline &(0.618)&(0.522)&(0.490)&(0.481)\tabularnewline x1 & 1.546* &\multicolumn{1}{l}{\_ }& 2.217*** & 2.217*** \tabularnewline &(0.692) &&(0.555)&(0.618)\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** & 3.717*** & 3.717*** \tabularnewline &&(0.512)&(0.483)&(0.464)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205 &4.849 &4.849\tabularnewline $R^2$&0.048 &0.312 &0.409 &0.409\tabularnewline adj $R^2$&0.039 &0.305 &0.397 &0.397\tabularnewline \hline \hline \multicolumn{5}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \begin{tabular}{@{}l*{5}{l}@{}} \hline &\multicolumn{1}{l}{Model A } &\multicolumn{1}{l}{Model B } &\multicolumn{1}{l}{Model C } &\multicolumn{1}{l}{Model C w Robust SE }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 29.774*** & 30.013*** & 30.013*** \tabularnewline &(0.618)&(0.522)&(0.490)&(0.481)\tabularnewline x1 & 1.546* &\multicolumn{1}{l}{\_ }& 2.217*** & 2.217*** \tabularnewline &(0.692) &&(0.555)&(0.618)\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** & 3.717*** & 3.717*** \tabularnewline &&(0.512)&(0.483)&(0.464)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &5.205 &4.849 &4.849\tabularnewline $R^2$&0.048 &0.312 &0.409 &0.409\tabularnewline adj $R^2$&0.039 &0.305 &0.397 &0.397\tabularnewline \hline \hline \multicolumn{5}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} > > ex11 <- outreg(list("I Love Long Titles" = m1, + "Prefer Brevity" = m2, + "Short" = m3), tight = FALSE, float = FALSE) \begin{tabular}{@{}l*{7}{l}@{}} \hline &\multicolumn{2}{l}{I Love Long Titles } &\multicolumn{2}{l}{Prefer Brevity } &\multicolumn{2}{l}{Short }\tabularnewline &\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{(S.E.)}&\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{(S.E.)}&\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & (0.618) & 29.774*** & (0.522) & 30.013*** & (0.490) \tabularnewline x1 & 1.546* & (0.692) &\multicolumn{1}{l}{\_ }&& 2.217*** & (0.555) \tabularnewline x2 &\multicolumn{1}{l}{\_ }&& 3.413*** & (0.512) & 3.717*** & (0.483) \tabularnewline \hline N&\multicolumn{1}{l}{100} &&\multicolumn{1}{l}{100} &&\multicolumn{1}{l}{100} & \tabularnewline RMSE&6.121&&5.205&&4.849\tabularnewline $R^2$&0.048&&0.312&&0.409\tabularnewline adj $R^2$&0.039&&0.305&&0.397\tabularnewline \hline \hline \multicolumn{7}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} > cat(ex11) \begin{tabular}{@{}l*{7}{l}@{}} \hline &\multicolumn{2}{l}{I Love Long Titles } &\multicolumn{2}{l}{Prefer Brevity } &\multicolumn{2}{l}{Short }\tabularnewline &\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{(S.E.)}&\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{(S.E.)}&\multicolumn{1}{c}{Estimate}&\multicolumn{1}{c}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & (0.618) & 29.774*** & (0.522) & 30.013*** & (0.490) \tabularnewline x1 & 1.546* & (0.692) &\multicolumn{1}{l}{\_ }&& 2.217*** & (0.555) \tabularnewline x2 &\multicolumn{1}{l}{\_ }&& 3.413*** & (0.512) & 3.717*** & (0.483) \tabularnewline \hline N&\multicolumn{1}{l}{100} &&\multicolumn{1}{l}{100} &&\multicolumn{1}{l}{100} & \tabularnewline RMSE&6.121&&5.205&&4.849\tabularnewline $R^2$&0.048&&0.312&&0.409\tabularnewline adj $R^2$&0.039&&0.305&&0.397\tabularnewline \hline \hline \multicolumn{7}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} > ##' > ex12 <- outreg(list("GLM" = gm1), float = TRUE) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{2}{l}@{}} \hline &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 0.033*** \tabularnewline &(0.001)\tabularnewline x1 & -0.002* \tabularnewline &(0.001)\tabularnewline \hline N&\multicolumn{1}{l}{100} \tabularnewline RMSE&\tabularnewline $R^2$&\tabularnewline Deviance&4.301\tabularnewline $-2LLR (Model \chi^2)$ & 0.208 \tabularnewline \hline \hline \multicolumn{2}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > cat(ex12) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{2}{l}@{}} \hline &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 0.033*** \tabularnewline &(0.001)\tabularnewline x1 & -0.002* \tabularnewline &(0.001)\tabularnewline \hline N&\multicolumn{1}{l}{100} \tabularnewline RMSE&\tabularnewline $R^2$&\tabularnewline Deviance&4.301\tabularnewline $-2LLR (Model \chi^2)$ & 0.208 \tabularnewline \hline \hline \multicolumn{2}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > ex13 <- outreg(list("OLS" = m1, "GLM" = gm1), float = TRUE, + alpha = c(0.05, 0.01)) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{OLS } &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245** & 0.033** \tabularnewline &(0.618)&(0.001)\tabularnewline x1 & 1.546* & -0.002* \tabularnewline &(0.692)&(0.001)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &\tabularnewline $R^2$&0.048 &\tabularnewline Deviance& &4.301\tabularnewline $-2LLR (Model \chi^2)$ & & 0.208 \tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$}\tabularnewline \end{tabular} \end{table} > cat(ex13) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{OLS } &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245** & 0.033** \tabularnewline &(0.618)&(0.001)\tabularnewline x1 & 1.546* & -0.002* \tabularnewline &(0.692)&(0.001)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &\tabularnewline $R^2$&0.048 &\tabularnewline Deviance& &4.301\tabularnewline $-2LLR (Model \chi^2)$ & & 0.208 \tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$}\tabularnewline \end{tabular} \end{table} > ##' > ex14 <- outreg(list(OLS = m1, GLM = gm1), float = TRUE, + request = c(fstatistic = "F"), runFuns = c("BIC" = "BIC")) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{OLS } &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 0.033*** \tabularnewline &(0.618)&(0.001)\tabularnewline x1 & 1.546* & -0.002* \tabularnewline &(0.692)&(0.001)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &\tabularnewline $R^2$&0.048 &\tabularnewline F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{4.98(1,98)*} &\tabularnewline Deviance& &4.301\tabularnewline $-2LLR (Model \chi^2)$ & & 0.208 \tabularnewline BIC&\multicolumn{1}{c}{657.92} &\multicolumn{1}{c}{659.82}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > cat(ex14) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{OLS } &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.245*** & 0.033*** \tabularnewline &(0.618)&(0.001)\tabularnewline x1 & 1.546* & -0.002* \tabularnewline &(0.692)&(0.001)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.121 &\tabularnewline $R^2$&0.048 &\tabularnewline F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{4.98(1,98)*} &\tabularnewline Deviance& &4.301\tabularnewline $-2LLR (Model \chi^2)$ & & 0.208 \tabularnewline BIC&\multicolumn{1}{c}{657.92} &\multicolumn{1}{c}{659.82}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > ex15 <- outreg(list(OLS = m1, GLM = gm1), float = TRUE, + request = c(fstatistic = "F"), runFuns = c("BIC" = "BIC"), + digits = 5, alpha = c(0.01)) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{OLS } &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.24550* & 0.03313* \tabularnewline &(0.61763)&(0.00068)\tabularnewline x1 & 1.54553 & -0.00173 \tabularnewline &(0.69242)&(0.00078)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.12090 &\tabularnewline $R^2$&0.04838 &\tabularnewline F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{4.9821(1,98)} &\tabularnewline Deviance& &4.30066\tabularnewline $-2LLR (Model \chi^2)$ & & 0.20827 \tabularnewline BIC&\multicolumn{1}{c}{657.92} &\multicolumn{1}{c}{659.82}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.01$}\tabularnewline \end{tabular} \end{table} > > ex16 <- outreg(list("OLS 1" = m1, "OLS 2" = m2, GLM = gm1), float = TRUE, + request = c(fstatistic = "F"), + runFuns = c("BIC" = "BIC", logLik = "ll"), + digits = 5, alpha = c(0.05, 0.01, 0.001)) \begin{table} \caption{A Regression}\label{regrlabl} \begin{tabular}{@{}l*{4}{l}@{}} \hline &\multicolumn{1}{l}{OLS 1 } &\multicolumn{1}{l}{OLS 2 } &\multicolumn{1}{l}{GLM }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 30.24550*** & 29.77420*** & 0.03313*** \tabularnewline &(0.61763)&(0.52229)&(0.00068)\tabularnewline x1 & 1.54553* &\multicolumn{1}{l}{\_ }& -0.00173* \tabularnewline &(0.69242) &&(0.00078)\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.41342*** &\multicolumn{1}{l}{\_ }\tabularnewline &&(0.51222) &\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE&6.12090 &5.20508 &\tabularnewline $R^2$&0.04838 &0.31184 &\tabularnewline adj $R^2$&0.03867 &0.30482 &\tabularnewline F($df_{num}$,$df_{denom}$)&\multicolumn{1}{c}{4.9821(1,98)*} &\multicolumn{1}{c}{44.409(1,98)***} &\tabularnewline Deviance& & &4.30066\tabularnewline $-2LLR (Model \chi^2)$ & & & 0.20827 \tabularnewline BIC&\multicolumn{1}{c}{657.92} &\multicolumn{1}{c}{625.51} &\multicolumn{1}{c}{659.82}\tabularnewline ll&\multicolumn{1}{c}{-322.05(3)} &\multicolumn{1}{c}{-305.85(3)} &\multicolumn{1}{c}{-323(3)}\tabularnewline \hline \hline \multicolumn{4}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} \end{table} > > ex17 <- outreg(list("Model A" = gm1, "Model B label with Spaces" = m2), + request = c(fstatistic = "F"), + runFuns = c("BIC" = "Schwarz IC", "AIC" = "Akaike IC", + "nobs" = "N Again?")) \begin{tabular}{@{}l*{3}{l}@{}} \hline &\multicolumn{1}{l}{Model A } &\multicolumn{1}{l}{Model B label with Spaces }\tabularnewline &\multicolumn{1}{l}{Estimate}&\multicolumn{1}{l}{Estimate}\tabularnewline &\multicolumn{1}{l}{(S.E.)}&\multicolumn{1}{l}{(S.E.)}\tabularnewline \hline \hline (Intercept) & 0.033*** & 29.774*** \tabularnewline &(0.001)&(0.522)\tabularnewline x1 & -0.002* &\multicolumn{1}{l}{\_ }\tabularnewline &(0.001) &\tabularnewline x2 &\multicolumn{1}{l}{\_ }& 3.413*** \tabularnewline &&(0.512)\tabularnewline \hline N&\multicolumn{1}{l}{100}&\multicolumn{1}{l}{100} \tabularnewline RMSE& &5.205\tabularnewline $R^2$& &0.312\tabularnewline adj $R^2$& &0.305\tabularnewline F($df_{num}$,$df_{denom}$)& &\multicolumn{1}{c}{44.4(1,98)***}\tabularnewline Deviance&4.301 &\tabularnewline $-2LLR (Model \chi^2)$ & 0.208 & \tabularnewline Schwarz IC&\multicolumn{1}{c}{659.82} &\multicolumn{1}{c}{625.51}\tabularnewline Akaike IC&\multicolumn{1}{c}{652.00} &\multicolumn{1}{c}{617.69}\tabularnewline N Again?&\multicolumn{1}{c}{100} &\multicolumn{1}{c}{100}\tabularnewline \hline \hline \multicolumn{3}{l}{ ${* p}\le 0.05$${*\!\!* p}\le 0.01$${*\!\!*\!\!* p}\le 0.001$}\tabularnewline \end{tabular} > > ## Here's a fit example from lme4. > if (require(lme4) && require(car)){ + fm1 <- lmer(Reaction ~ Days + (Days | Subject), sleepstudy) + ex18 <- outreg(fm1) + cat(ex18) + ## Fit same with lm for comparison + lm1 <- lm(Reaction ~ Days, sleepstudy) + ## Get robust standard errors + lm1rse <- sqrt(diag(car::hccm(lm1))) + + if(interactive()){ + ex19 <- outreg(list("Random Effects" = fm1, + "OLS" = lm1, "OLS Robust SE" = lm1), + SElist = list("OLS Robust SE" = lm1rse), type = "html") + } + ## From the glmer examples + gm2 <- glmer(cbind(incidence, size - incidence) ~ period + (1 | herd), + data = cbpp, family = binomial) + lm2 <- lm(incidence/size ~ period, data = cbpp) + lm2rse <- sqrt(diag(car::hccm(lm2))) + ## Lets see what MASS::rlm objects do? Mostly OK + rlm2 <- MASS::rlm(incidence/size ~ period, data = cbpp) + + } Loading required package: lme4 Loading required package: Matrix Error in get(x, envir = ns, inherits = FALSE) : object 'formatVC' not found Calls: outreg ... getVCmat -> lapply -> FUN -> getFromNamespace -> get Execution halted Package: rstanarm Check: examples New result: ERROR Running examples in ‘rstanarm-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: bayes_R2.stanreg > ### Title: Compute a Bayesian version of R-squared or LOO-adjusted > ### R-squared for regression models. > ### Aliases: bayes_R2.stanreg bayes_R2 loo_R2.stanreg loo_R2 > > ### ** Examples > > if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") { + fit <- stan_glm( + mpg ~ wt + cyl, + data = mtcars, + QR = TRUE, + chains = 2, + refresh = 0 + ) + rsq <- bayes_R2(fit) + print(median(rsq)) + hist(rsq) + + loo_rsq <- loo_R2(fit) + print(median(loo_rsq)) + + # multilevel binomial model + if (!exists("example_model")) example(example_model) + print(example_model) + median(bayes_R2(example_model)) + median(bayes_R2(example_model, re.form = NA)) # exclude group-level + } [1] 0.8157955 [1] 0.7981092 exmpl_> if (.Platform$OS.type != "windows" || .Platform$r_arch != "i386") { exmpl_+ example_model <- exmpl_+ stan_glmer(cbind(incidence, size - incidence) ~ size + period + (1|herd), exmpl_+ data = lme4::cbpp, family = binomial, QR = TRUE, exmpl_+ # this next line is only to keep the example small in size! exmpl_+ chains = 2, cores = 1, seed = 12345, iter = 1000, refresh = 0) exmpl_+ example_model exmpl_+ } Error: unable to find an inherited method for function ‘getTheta’ for signature ‘object = "NULL"’ Execution halted Package: rstanarm Check: tests New result: ERROR Running ‘testthat.R’ [6s/5s] Running the tests in ‘tests/testthat.R’ failed. Complete output: > # Part of the rstanarm package for estimating model parameters > # Copyright (C) 2015 Trustees of Columbia University > # > # This program is free software; you can redistribute it and/or > # modify it under the terms of the GNU General Public License > # as published by the Free Software Foundation; either version 3 > # of the License, or (at your option) any later version. > # > # This program is distributed in the hope that it will be useful, > # but WITHOUT ANY WARRANTY; without even the implied warranty of > # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the > # GNU General Public License for more details. > # > # You should have received a copy of the GNU General Public License > # along with this program; if not, write to the Free Software > # Foundation, Inc., 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301, USA. > > library(testthat) > suppressPackageStartupMessages(library(rstanarm)) > Sys.unsetenv("R_TESTS") > o <- utils::capture.output(example(example_model, echo = FALSE)) Error: unable to find an inherited method for function 'getTheta' for signature 'object = "NULL"' Execution halted Package: rtpcr Check: examples New result: ERROR Running examples in ‘rtpcr-Ex.R’ failed The error most likely occurred in: > base::assign(".ptime", proc.time(), pos = "CheckExEnv") > ### Name: ANOVA_DCt > ### Title: Delta Ct ANOVA analysis with optional model specification > ### Aliases: ANOVA_DCt > > ### ** Examples > > # Default usage with fixed effects > result <- ANOVA_DCt(data_2factorBlock3ref, numOfFactors = 2, numberOfrefGenes = 3, + block = "block") Relative Expression gene Type Concentration dCt RE log2FC LCL UCL se 1 PO R L1 0.08550 0.94246 -0.08550 0.68199 1.30241 0.26799 2 PO S L1 1.28189 0.41126 -1.28189 0.29760 0.56833 0.30503 3 PO R L2 1.65834 0.31680 -1.65834 0.22089 0.46897 0.25149 4 PO S L2 2.20079 0.21752 -2.20079 0.14694 0.31197 0.05775 5 PO R L3 2.62828 0.16174 -2.62828 0.11704 0.22351 0.06143 6 PO S L3 3.12306 0.11478 -3.12306 0.08306 0.15862 0.21472 7 NLM R L1 -1.32600 2.50707 1.32600 1.92666 3.26233 0.15025 8 NLM S L1 -0.41248 1.33097 0.41248 0.97812 1.80481 0.40799 9 NLM R L2 -0.29364 1.22573 0.29364 0.94196 1.59498 0.17934 10 NLM S L2 1.00339 0.49883 -1.00339 0.38334 0.64910 0.09128 11 NLM R L3 2.91119 0.13294 -2.91119 0.10216 0.17298 0.10361 12 NLM S L3 3.50923 0.08782 -3.50923 0.06749 0.11428 0.11980 Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC sig 1 0.78269 1.13484 -0.10295 -0.07100 a 2 0.33288 0.50808 -1.58370 -1.03760 b 3 0.26612 0.37713 -1.97415 -1.39305 bc 4 0.20898 0.22640 -2.29068 -2.11443 cd 5 0.15499 0.16877 -2.74261 -2.51872 de 6 0.09891 0.13320 -3.62425 -2.69118 e 7 2.25910 2.78226 1.19485 1.47155 a 8 1.00312 1.76598 0.31087 0.54729 b 9 1.08244 1.38797 0.25931 0.33250 b 10 0.46824 0.53141 -1.06893 -0.94187 c 11 0.12372 0.14284 -3.12794 -2.70945 d 12 0.08083 0.09543 -3.81308 -3.22959 e Note: Using default model for statistical analysis: wDCt ~ block + Type * Concentration > > # Mixed model with random block effect > result_mixed <- ANOVA_DCt(data_2factorBlock3ref, numOfFactors = 2, numberOfrefGenes = 3, + block = "block") Relative Expression gene Type Concentration dCt RE log2FC LCL UCL se 1 PO R L1 0.08550 0.94246 -0.08550 0.68199 1.30241 0.26799 2 PO S L1 1.28189 0.41126 -1.28189 0.29760 0.56833 0.30503 3 PO R L2 1.65834 0.31680 -1.65834 0.22089 0.46897 0.25149 4 PO S L2 2.20079 0.21752 -2.20079 0.14694 0.31197 0.05775 5 PO R L3 2.62828 0.16174 -2.62828 0.11704 0.22351 0.06143 6 PO S L3 3.12306 0.11478 -3.12306 0.08306 0.15862 0.21472 7 NLM R L1 -1.32600 2.50707 1.32600 1.92666 3.26233 0.15025 8 NLM S L1 -0.41248 1.33097 0.41248 0.97812 1.80481 0.40799 9 NLM R L2 -0.29364 1.22573 0.29364 0.94196 1.59498 0.17934 10 NLM S L2 1.00339 0.49883 -1.00339 0.38334 0.64910 0.09128 11 NLM R L3 2.91119 0.13294 -2.91119 0.10216 0.17298 0.10361 12 NLM S L3 3.50923 0.08782 -3.50923 0.06749 0.11428 0.11980 Lower.se.RE Upper.se.RE Lower.se.log2FC Upper.se.log2FC sig 1 0.78269 1.13484 -0.10295 -0.07100 a 2 0.33288 0.50808 -1.58370 -1.03760 b 3 0.26612 0.37713 -1.97415 -1.39305 bc 4 0.20898 0.22640 -2.29068 -2.11443 cd 5 0.15499 0.16877 -2.74261 -2.51872 de 6 0.09891 0.13320 -3.62425 -2.69118 e 7 2.25910 2.78226 1.19485 1.47155 a 8 1.00312 1.76598 0.31087 0.54729 b 9 1.08244 1.38797 0.25931 0.33250 b 10 0.46824 0.53141 -1.06893 -0.94187 c 11 0.12372 0.14284 -3.12794 -2.70945 d 12 0.08083 0.09543 -3.81308 -3.22959 e Note: Using default model for statistical analysis: wDCt ~ block + Type * Concentration > > # Custom mixed model formula with nested random effects > result_custom <- ANOVA_DCt(data_repeated_measure_2, numOfFactors = 2, numberOfrefGenes = 1, + block = NULL, + model = wDCt ~ treatment * time + (1 | id)) Using user defined formula. Ignoring block and numOfFactors for model specification. Error in forceNewMerMod(res, reference = model) : could not find function "forceNewMerMod" Calls: ANOVA_DCt ... suppressMessages -> withCallingHandlers -> -> as_lmerModLT Execution halted Package: tram Check: tests New result: NOTE Running ‘Coxph-Ex.R’ [6s/6s] Comparing ‘Coxph-Ex.Rout’ to ‘Coxph-Ex.Rout.save’ ... OK Running ‘KaplanMeier-Ex.R’ [1s/1s] Running ‘PI_OVL-Ex.R’ [2s/2s] Running ‘Polr-Ex.R’ [6s/6s] Comparing ‘Polr-Ex.Rout’ to ‘Polr-Ex.Rout.save’ ... OK Running ‘Survreg-Ex.R’ [2s/2s] Comparing ‘Survreg-Ex.Rout’ to ‘Survreg-Ex.Rout.save’ ... OK Running ‘bugfixes.R’ [41s/41s] Running ‘intercepts-Ex.R’ [2s/2s] Comparing ‘intercepts-Ex.Rout’ to ‘intercepts-Ex.Rout.save’ ... OK Running ‘mmlt-Ex.R’ [72s/72s] Comparing ‘mmlt-Ex.Rout’ to ‘mmlt-Ex.Rout.save’ ... OK Running ‘mmlt-interface.R’ [22s/22s] Running ‘mtram-Ex.R’ [5s/5s] Comparing ‘mtram-Ex.Rout’ to ‘mtram-Ex.Rout.save’ ... 70,75d69 < attr(,"theta") < [1] 0.929 0.018 0.223 < attr(,"profpar") < [1] 0.929 0.223 0.081 < attr(,"class") < [1] "vcmat_us" "matrix" "array" Running ‘stram-Ex.R’ [16s/16s] Comparing ‘stram-Ex.Rout’ to ‘stram-Ex.Rout.save’ ... OK