Last updated on 2022-08-15 08:50:06 CEST.
| Flavor | Version | Tinstall | Tcheck | Ttotal | Status | Flags |
|---|---|---|---|---|---|---|
| r-devel-linux-x86_64-debian-clang | 0.5.1 | 11.15 | 122.81 | 133.96 | ERROR | |
| r-devel-linux-x86_64-debian-gcc | 0.5.1 | 9.31 | 90.46 | 99.77 | ERROR | |
| r-devel-linux-x86_64-fedora-clang | 0.5.1 | 166.53 | ERROR | |||
| r-devel-linux-x86_64-fedora-gcc | 0.5.1 | 182.32 | ERROR | |||
| r-devel-windows-x86_64 | 0.5.1 | 46.00 | 151.00 | 197.00 | ERROR | |
| r-patched-linux-x86_64 | 0.5.1 | 11.51 | 117.48 | 128.99 | ERROR | |
| r-release-linux-x86_64 | 0.5.1 | 10.52 | 115.96 | 126.48 | ERROR | |
| r-release-macos-arm64 | 0.5.1 | 39.00 | OK | |||
| r-release-macos-x86_64 | 0.5.1 | 64.00 | OK | |||
| r-release-windows-x86_64 | 0.5.1 | 63.00 | 155.00 | 218.00 | ERROR | |
| r-oldrel-macos-arm64 | 0.5.1 | 37.00 | OK | |||
| r-oldrel-macos-x86_64 | 0.5.1 | 56.00 | OK | |||
| r-oldrel-windows-ix86+x86_64 | 0.5.1 | 19.00 | 146.00 | 165.00 | ERROR |
Version: 0.5.1
Check: R code for possible problems
Result: NOTE
.find_regression_estimate: no visible global function definition for
'data_findcols'
report_table.MixMod: no visible global function definition for
'data_findcols'
report_table.anova: no visible global function definition for
'data_findcols'
report_table.aov: no visible global function definition for
'data_findcols'
report_table.aovlist: no visible global function definition for
'data_findcols'
report_table.brmsfit: no visible global function definition for
'data_findcols'
report_table.glm: no visible global function definition for
'data_findcols'
report_table.glmmTMB: no visible global function definition for
'data_findcols'
report_table.ivreg: no visible global function definition for
'data_findcols'
report_table.lavaan: no visible global function definition for
'data_findcols'
report_table.lm: no visible global function definition for
'data_findcols'
report_table.lme: no visible global function definition for
'data_findcols'
report_table.merMod: no visible global function definition for
'data_findcols'
report_table.stanreg: no visible global function definition for
'data_findcols'
report_table.survreg: no visible global function definition for
'data_findcols'
report_table.zeroinfl: no visible global function definition for
'data_findcols'
Undefined global functions or variables:
data_findcols
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-patched-linux-x86_64, r-release-linux-x86_64, r-release-windows-x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.5.1
Check: examples
Result: ERROR
Running examples in 'report-Ex.R' failed
The error most likely occurred in:
> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: report.default
> ### Title: Template to add report support for new objects
> ### Aliases: report.default report_effectsize.default report_table.default
> ### report_statistics.default report_parameters.default
> ### report_intercept.default report_model.default report_random.default
> ### report_priors.default report_performance.default report_info.default
> ### report_text.default
>
> ### ** Examples
>
> library(report)
>
> # Add a reproducible example instead of the following
> model <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
> r <- report(model)
Error in data_findcols(table, candidates) :
could not find function "data_findcols"
Calls: report ... report_statistics -> report_statistics.lm -> .find_regression_estimate
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running 'spelling.R' [0s/0s]
Running 'testthat.R' [43s/46s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
== Skipped tests ===============================================================
* On CRAN (11)
== Failed tests ================================================================
-- Failure (test-report.aov.R:4:3): report.aov ---------------------------------
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
-- Failure (test-report.aov.R:9:3): report.aov ---------------------------------
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
-- Failure (test-report.aov.R:13:3): report.aov --------------------------------
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
-- Failure (test-report.aov.R:22:3): report.aov --------------------------------
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
-- Failure (test-report.aov.R:27:3): report.aov --------------------------------
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
-- Failure (test-report.bayesfactor_models.R:15:5): models ---------------------
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
x
1. \-testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. \-testthat::expect_match(...)
3. \-testthat:::expect_match_(...)
-- Failure (test-report.bayesfactor_models.R:16:5): models ---------------------
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
x
1. \-testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. \-testthat::expect_match(...)
3. \-testthat:::expect_match_(...)
-- Failure (test-report.bayesfactor_models.R:33:5): inclusion ------------------
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
x
1. \-testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. \-testthat::expect_match(...)
3. \-testthat:::expect_match_(...)
-- Error (test-report.ivreg.R:13:5): report-survreg ----------------------------
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:6:3): report.lm - lm --------------------------------
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:10:3): report.lm - lm -------------------------------
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:17:3): report.lm - glm ------------------------------
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:20:3): report.lm - glm ------------------------------
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lmer.R:19:5): report-lmer --------------------------------
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lmer.R:21:5): report-lmer --------------------------------
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.survreg.R:13:5): report-survreg --------------------------
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report_intercept.R:36:5): reflevel ------------------------------
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
x
1. +-testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. | \-testthat::quasi_label(enquo(object), label, arg = "object")
3. | \-rlang::eval_bare(expr, quo_get_env(quo))
4. +-report::report_intercept(m1)
5. \-report:::report_intercept.lm(m1)
6. +-base::paste0(...)
7. +-report::report_statistics(x, intercept, include_effectsize = FALSE)
8. \-report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. \-report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-clang
Version: 0.5.1
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
...
--- re-building 'cite_packages.Rmd' using rmarkdown
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
--- finished re-building 'cite_packages.Rmd'
--- re-building 'new_models.Rmd' using rmarkdown
--- finished re-building 'new_models.Rmd'
--- re-building 'report.Rmd' using rmarkdown
Warning: Following variable(s) were not found: n_Obs
Warning: Following variable(s) were not found: n_Obs
Warning: Following variable(s) were not found: n_Obs
Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...) :
Unable to retrieve data from htest object. Using t_to_d() approximation.
Quitting from lines 105-108 (report.Rmd)
Error: processing vignette 'report.Rmd' failed with diagnostics:
could not find function "data_findcols"
--- failed re-building 'report.Rmd'
SUMMARY: processing the following file failed:
'report.Rmd'
Error: Vignette re-building failed.
Execution halted
Flavors: r-devel-linux-x86_64-debian-clang, r-devel-linux-x86_64-debian-gcc, r-patched-linux-x86_64, r-release-linux-x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running ‘spelling.R’ [0s/1s]
Running ‘testthat.R’ [30s/46s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (11)
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure (test-report.aov.R:4:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:9:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:13:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:22:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
── Failure (test-report.aov.R:27:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
── Failure (test-report.bayesfactor_models.R:15:5): models ─────────────────────
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:16:5): models ─────────────────────
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:33:5): inclusion ──────────────────
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Error (test-report.ivreg.R:13:5): report-survreg ────────────────────────────
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:6:3): report.lm - lm ────────────────────────────────
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:10:3): report.lm - lm ───────────────────────────────
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:17:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:20:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:19:5): report-lmer ────────────────────────────────
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:21:5): report-lmer ────────────────────────────────
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.survreg.R:13:5): report-survreg ──────────────────────────
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report_intercept.R:36:5): reflevel ──────────────────────────────
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
▆
1. ├─testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─report::report_intercept(m1)
5. └─report:::report_intercept.lm(m1)
6. ├─base::paste0(...)
7. ├─report::report_statistics(x, intercept, include_effectsize = FALSE)
8. └─report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. └─report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-debian-gcc
Version: 0.5.1
Check: examples
Result: ERROR
Running examples in ‘report-Ex.R’ failed
The error most likely occurred in:
> ### Name: report.default
> ### Title: Template to add report support for new objects
> ### Aliases: report.default report_effectsize.default report_table.default
> ### report_statistics.default report_parameters.default
> ### report_intercept.default report_model.default report_random.default
> ### report_priors.default report_performance.default report_info.default
> ### report_text.default
>
> ### ** Examples
>
> library(report)
>
> # Add a reproducible example instead of the following
> model <- lm(Sepal.Length ~ Petal.Length * Species, data = iris)
> r <- report(model)
Error in data_findcols(table, candidates) :
could not find function "data_findcols"
Calls: report ... report_statistics -> report_statistics.lm -> .find_regression_estimate
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-release-windows-x86_64, r-oldrel-windows-ix86+x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running ‘spelling.R’
Running ‘testthat.R’ [47s/54s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (11)
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure (test-report.aov.R:4:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:9:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:13:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:22:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
── Failure (test-report.aov.R:27:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
── Failure (test-report.bayesfactor_models.R:15:5): models ─────────────────────
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:16:5): models ─────────────────────
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:33:5): inclusion ──────────────────
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Error (test-report.ivreg.R:13:5): report-survreg ────────────────────────────
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:6:3): report.lm - lm ────────────────────────────────
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:10:3): report.lm - lm ───────────────────────────────
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:17:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:20:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:19:5): report-lmer ────────────────────────────────
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:21:5): report-lmer ────────────────────────────────
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.survreg.R:13:5): report-survreg ──────────────────────────
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report_intercept.R:36:5): reflevel ──────────────────────────────
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
▆
1. ├─testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─report::report_intercept(m1)
5. └─report:::report_intercept.lm(m1)
6. ├─base::paste0(...)
7. ├─report::report_statistics(x, intercept, include_effectsize = FALSE)
8. └─report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. └─report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-clang
Version: 0.5.1
Check: re-building of vignette outputs
Result: ERROR
Error(s) in re-building vignettes:
--- re-building ‘cite_packages.Rmd’ using rmarkdown
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
--- finished re-building ‘cite_packages.Rmd’
--- re-building ‘new_models.Rmd’ using rmarkdown
--- finished re-building ‘new_models.Rmd’
--- re-building ‘report.Rmd’ using rmarkdown
Warning: Following variable(s) were not found: n_Obs
Warning: Following variable(s) were not found: n_Obs
Warning: Following variable(s) were not found: n_Obs
Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...) :
Unable to retrieve data from htest object. Using t_to_d() approximation.
Quitting from lines 105-108 (report.Rmd)
Error: processing vignette 'report.Rmd' failed with diagnostics:
could not find function "data_findcols"
--- failed re-building ‘report.Rmd’
SUMMARY: processing the following file failed:
‘report.Rmd’
Error: Vignette re-building failed.
Execution halted
Flavors: r-devel-linux-x86_64-fedora-clang, r-devel-linux-x86_64-fedora-gcc, r-devel-windows-x86_64, r-release-windows-x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running ‘spelling.R’
Running ‘testthat.R’ [55s/207s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (11)
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure (test-report.aov.R:4:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:9:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:13:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:22:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
── Failure (test-report.aov.R:27:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
── Failure (test-report.bayesfactor_models.R:15:5): models ─────────────────────
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:16:5): models ─────────────────────
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:33:5): inclusion ──────────────────
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Error (test-report.ivreg.R:13:5): report-survreg ────────────────────────────
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:6:3): report.lm - lm ────────────────────────────────
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:10:3): report.lm - lm ───────────────────────────────
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:17:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:20:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:19:5): report-lmer ────────────────────────────────
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:21:5): report-lmer ────────────────────────────────
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.survreg.R:13:5): report-survreg ──────────────────────────
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report_intercept.R:36:5): reflevel ──────────────────────────────
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
▆
1. ├─testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─report::report_intercept(m1)
5. └─report:::report_intercept.lm(m1)
6. ├─base::paste0(...)
7. ├─report::report_statistics(x, intercept, include_effectsize = FALSE)
8. └─report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. └─report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavor: r-devel-linux-x86_64-fedora-gcc
Version: 0.5.1
Check: tests
Result: ERROR
Running 'spelling.R' [0s]
Running 'testthat.R' [45s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (11)
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure (test-report.aov.R:4:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:9:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:13:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:22:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
── Failure (test-report.aov.R:27:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
── Failure (test-report.bayesfactor_models.R:15:5): models ─────────────────────
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:16:5): models ─────────────────────
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:33:5): inclusion ──────────────────
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Error (test-report.ivreg.R:13:5): report-survreg ────────────────────────────
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:6:3): report.lm - lm ────────────────────────────────
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:10:3): report.lm - lm ───────────────────────────────
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:17:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:20:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:19:5): report-lmer ────────────────────────────────
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:21:5): report-lmer ────────────────────────────────
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.survreg.R:13:5): report-survreg ──────────────────────────
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report_intercept.R:36:5): reflevel ──────────────────────────────
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
▆
1. ├─testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─report::report_intercept(m1)
5. └─report:::report_intercept.lm(m1)
6. ├─base::paste0(...)
7. ├─report::report_statistics(x, intercept, include_effectsize = FALSE)
8. └─report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. └─report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavors: r-devel-windows-x86_64, r-release-windows-x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running ‘spelling.R’ [0s/0s]
Running ‘testthat.R’ [41s/48s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (11)
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure (test-report.aov.R:4:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:9:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:13:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:22:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
── Failure (test-report.aov.R:27:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
── Failure (test-report.bayesfactor_models.R:15:5): models ─────────────────────
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:16:5): models ─────────────────────
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:33:5): inclusion ──────────────────
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Error (test-report.ivreg.R:13:5): report-survreg ────────────────────────────
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:6:3): report.lm - lm ────────────────────────────────
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:10:3): report.lm - lm ───────────────────────────────
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:17:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:20:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:19:5): report-lmer ────────────────────────────────
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:21:5): report-lmer ────────────────────────────────
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.survreg.R:13:5): report-survreg ──────────────────────────
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report_intercept.R:36:5): reflevel ──────────────────────────────
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
▆
1. ├─testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─report::report_intercept(m1)
5. └─report:::report_intercept.lm(m1)
6. ├─base::paste0(...)
7. ├─report::report_statistics(x, intercept, include_effectsize = FALSE)
8. └─report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. └─report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavor: r-patched-linux-x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running ‘spelling.R’ [0s/1s]
Running ‘testthat.R’ [40s/46s]
Running the tests in ‘tests/testthat.R’ failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
══ Skipped tests ═══════════════════════════════════════════════════════════════
• On CRAN (11)
══ Failed tests ════════════════════════════════════════════════════════════════
── Failure (test-report.aov.R:4:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:9:3): report.aov ─────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:13:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
── Failure (test-report.aov.R:22:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
── Failure (test-report.aov.R:27:3): report.aov ────────────────────────────────
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
── Failure (test-report.bayesfactor_models.R:15:5): models ─────────────────────
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:16:5): models ─────────────────────
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Failure (test-report.bayesfactor_models.R:33:5): inclusion ──────────────────
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
▆
1. └─testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. └─testthat::expect_match(...)
3. └─testthat:::expect_match_(...)
── Error (test-report.ivreg.R:13:5): report-survreg ────────────────────────────
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:6:3): report.lm - lm ────────────────────────────────
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:10:3): report.lm - lm ───────────────────────────────
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:17:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lm.R:20:3): report.lm - glm ──────────────────────────────
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:19:5): report-lmer ────────────────────────────────
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.lmer.R:21:5): report-lmer ────────────────────────────────
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report.survreg.R:13:5): report-survreg ──────────────────────────
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
── Error (test-report_intercept.R:36:5): reflevel ──────────────────────────────
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
▆
1. ├─testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. │ └─testthat::quasi_label(enquo(object), label, arg = "object")
3. │ └─rlang::eval_bare(expr, quo_get_env(quo))
4. ├─report::report_intercept(m1)
5. └─report:::report_intercept.lm(m1)
6. ├─base::paste0(...)
7. ├─report::report_statistics(x, intercept, include_effectsize = FALSE)
8. └─report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. └─report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavor: r-release-linux-x86_64
Version: 0.5.1
Check: tests
Result: ERROR
Running 'spelling.R' [0s]
Running 'testthat.R' [47s]
Running the tests in 'tests/testthat.R' failed.
Complete output:
> library(testthat)
> library(report)
>
> is_dev_version <- length(strsplit(packageDescription("report")$Version, "\\.")[[1]]) > 3
>
> if (is_dev_version) {
+ Sys.setenv("RunAllreportTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportTests" = "no")
+ }
>
> si <- Sys.info()
>
> osx <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ si["sysname"] == "Darwin" || grepl("^darwin", R.version$os)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> solaris <- tryCatch(
+ {
+ if (!is.null(si["sysname"])) {
+ grepl("SunOS", si["sysname"], ignore.case = TRUE)
+ } else {
+ FALSE
+ }
+ },
+ error = function(e) {
+ FALSE
+ }
+ )
>
> # disable / enable if needed
> if (.Platform$OS.type == "unix" && is_dev_version) {
+ Sys.setenv("RunAllreportStanTests" = "yes")
+ } else {
+ Sys.setenv("RunAllreportStanTests" = "no")
+ }
>
> if (!osx && !solaris) {
+ test_check("report")
+ }
Loading required package: lme4
Loading required package: Matrix
Loading required package: rstanarm
Loading required package: Rcpp
This is rstanarm version 2.21.3
- See https://mc-stan.org/rstanarm/articles/priors for changes to default priors!
- Default priors may change, so it's safest to specify priors, even if equivalent to the defaults.
- For execution on a local, multicore CPU with excess RAM we recommend calling
options(mc.cores = parallel::detectCores())
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
For one-way between subjects designs, partial eta squared is equivalent to eta squared.
Returning eta squared.
Loading required package: bayestestR
Loading required package: dplyr
Attaching package: 'dplyr'
The following object is masked from 'package:testthat':
matches
The following objects are masked from 'package:stats':
filter, lag
The following objects are masked from 'package:base':
intersect, setdiff, setequal, union
Loading required package: ivreg
Loading required package: lavaan
This is lavaan 0.6-12
lavaan is FREE software! Please report any bugs.
Loading required package: effectsize
boundary (singular) fit: see help('isSingular')
Loading required package: survival
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
Package 'merDeriv' needs to be installed to compute confidence intervals
for random effect parameters.
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
== Skipped tests ===============================================================
* On CRAN (11)
== Failed tests ================================================================
-- Failure (test-report.aov.R:4:3): report.aov ---------------------------------
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
-- Failure (test-report.aov.R:9:3): report.aov ---------------------------------
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
-- Failure (test-report.aov.R:13:3): report.aov --------------------------------
c(...) (`actual`) not equal to c(7, 2) (`expected`).
`actual`: 9 2
`expected`: 7 2
-- Failure (test-report.aov.R:22:3): report.aov --------------------------------
c(...) (`actual`) not equal to c(7, 8) (`expected`).
`actual`: 9 8
`expected`: 7 8
-- Failure (test-report.aov.R:27:3): report.aov --------------------------------
c(...) (`actual`) not equal to c(8, 5) (`expected`).
`actual`: 10 5
`expected`: 8 5
-- Failure (test-report.bayesfactor_models.R:15:5): models ---------------------
`print\(r\)` does not match "\\(Intercept only\\) model \\(the least supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
x
1. \-testthat::expect_output(print(r), "\\(Intercept only\\) model \\(the least supported model\\)") at test-report.bayesfactor_models.R:15:4
2. \-testthat::expect_match(...)
3. \-testthat:::expect_match_(...)
-- Failure (test-report.bayesfactor_models.R:16:5): models ---------------------
`print\(r\)` does not match "Species \\+ Petal.Length model \\(the most supported model\\)".
Actual value: "Bayes factors were computed using the BIC approximation, by which BF10 =\\nexp\(\(BIC0 - BIC1\)/2\)\. Compared to the \(Intercept only\) model \(the least\\nsupported model\), we found extreme evidence \(BF = 1\.70e\+29\) in favour of the\\nSpecies model; extreme evidence \(BF = 5\.84e\+55\) in favour of the Species \+\\nPetal\.Length model \(the most supported model\); extreme evidence \(BF = 2\.20e\+54\)\\nin favour of the Species \* Petal\.Length model\."
Backtrace:
x
1. \-testthat::expect_output(print(r), "Species \\+ Petal.Length model \\(the most supported model\\)") at test-report.bayesfactor_models.R:16:4
2. \-testthat::expect_match(...)
3. \-testthat:::expect_match_(...)
-- Failure (test-report.bayesfactor_models.R:33:5): inclusion ------------------
`print\(r\)` does not match "subjective prior odds".
Actual value: "Bayesian model averaging \(BMA\) was used to obtain the average evidence for each\\npredictor\. Since each model has a prior probability \(here we used subjective\\nprior odds of 1, 2, 3\), it is possible to sum the prior probability of all\\nmodels that include a predictor of interest \(the prior inclusion probability\),\\nand of all models that do not include that predictor \(the prior exclusion\\nprobability\)\. After the data are observed, we can similarly consider the sums\\nof the posterior models' probabilities to obtain the posterior inclusion\\nprobability and the posterior exclusion probability\. The change from prior to\\nposterior inclusion odds is the Inclusion Bayes factor\. For each predictor,\\naveraging was done only across models that did not include any interactions\\nwith that predictor; additionally, for each interaction predictor, averaging\\nwas done only across models that contained the main effect from which the\\ninteraction predictor was comprised\. This was done to prevent Inclusion Bayes\\nfactors from being contaminated with non-relevant evidence \(see Mathot, 2017\)\.\\nWe found extreme evidence \(BF = 3\.90e\+55\) in favour of including Species, with\\nmodels including Species having an overall posterior probability of 95%;\\nextreme evidence \(BF = 6\.89e\+26\) in favour of including Petal\.Length, with\\nmodels including Petal\.Length having an overall posterior probability of 95%;\\nstrong evidence \(BF = 1/26\.52\) against including Petal\.Length:Species, with\\nmodels including Petal\.Length:Species having an overall posterior probability\\nof 5%\."
Backtrace:
x
1. \-testthat::expect_output(print(r), "subjective prior odds") at test-report.bayesfactor_models.R:33:4
2. \-testthat::expect_match(...)
3. \-testthat:::expect_match_(...)
-- Error (test-report.ivreg.R:13:5): report-survreg ----------------------------
`report(ivr)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:6:3): report.lm - lm --------------------------------
`report(lm(Sepal.Width ~ Species, data = iris))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:10:3): report.lm - lm -------------------------------
`report(lm(wt ~ as.factor(am) * as.factor(cyl), data = mtcars))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:17:3): report.lm - glm ------------------------------
`report(glm(vs ~ disp, data = mtcars, family = binomial(link = "probit")))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lm.R:20:3): report.lm - glm ------------------------------
`report(glm(vs ~ mpg, data = mtcars, family = "poisson"))` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lmer.R:19:5): report-lmer --------------------------------
`report(m1)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.lmer.R:21:5): report-lmer --------------------------------
`report(m2)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report.survreg.R:13:5): report-survreg --------------------------
`report(mod_survreg)` threw an unexpected error.
Message: could not find function "data_findcols"
Class: simpleError/error/condition
-- Error (test-report_intercept.R:36:5): reflevel ------------------------------
Error in `data_findcols(table, candidates)`: could not find function "data_findcols"
Backtrace:
x
1. +-testthat::expect_equal(as.character(report_intercept(m1)), "The model's intercept, corresponding to f = 3, is at 0.07 (95% CI [-0.57, 0.71], t(27) = 0.23, p = 0.819).") at test-report_intercept.R:36:4
2. | \-testthat::quasi_label(enquo(object), label, arg = "object")
3. | \-rlang::eval_bare(expr, quo_get_env(quo))
4. +-report::report_intercept(m1)
5. \-report:::report_intercept.lm(m1)
6. +-base::paste0(...)
7. +-report::report_statistics(x, intercept, include_effectsize = FALSE)
8. \-report:::report_statistics.lm(x, intercept, include_effectsize = FALSE)
9. \-report:::.find_regression_estimate(table)
[ FAIL 17 | WARN 7 | SKIP 11 | PASS 96 ]
Error: Test failures
Execution halted
Flavor: r-oldrel-windows-ix86+x86_64
Version: 0.5.1
Check: re-building of vignette outputs
Result: WARN
Error(s) in re-building vignettes:
--- re-building 'cite_packages.Rmd' using rmarkdown
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
--- finished re-building 'cite_packages.Rmd'
--- re-building 'new_models.Rmd' using rmarkdown
--- finished re-building 'new_models.Rmd'
--- re-building 'report.Rmd' using rmarkdown
Warning: Following variable(s) were not found: n_Obs
Warning: Following variable(s) were not found: n_Obs
Warning: Following variable(s) were not found: n_Obs
Warning in .effectsize_t.test(model, type = type, verbose = verbose, ...) :
Unable to retrieve data from htest object. Using t_to_d() approximation.
Quitting from lines 105-108 (report.Rmd)
Error: processing vignette 'report.Rmd' failed with diagnostics:
could not find function "data_findcols"
--- failed re-building 'report.Rmd'
SUMMARY: processing the following file failed:
'report.Rmd'
Error: Vignette re-building failed.
Execution halted
Flavor: r-oldrel-windows-ix86+x86_64