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tidy_stats is used to convert the output of a statistical object to a list of organized statistics. The tidy_stats function is automatically run when add_stats is used, so there is generally no need to use this function explicitly. It can be used, however, to peek at how the output of a specific analysis will be organized.

Usage

tidy_stats(x)

# S3 method for htest
tidy_stats(x)

# S3 method for lm
tidy_stats(x)

# S3 method for glm
tidy_stats(x)

# S3 method for anova
tidy_stats(x)

# S3 method for aov
tidy_stats(x)

# S3 method for aovlist
tidy_stats(x)

# S3 method for tidystats_descriptives
tidy_stats(x)

# S3 method for tidystats_counts
tidy_stats(x)

# S3 method for lmerMod
tidy_stats(x)

# S3 method for lmerModLmerTest
tidy_stats(x)

# S3 method for BFBayesFactor
tidy_stats(x)

# S3 method for afex_aov
tidy_stats(x)

# S3 method for emmGrid
tidy_stats(x)

# S3 method for emm_list
tidy_stats(x)

Arguments

x

The output of a statistical test.

Details

Please note that not all statistical tests are supported. See 'Details' below for a list of supported statistical tests.

Currently supported functions:

stats:

lme4/lmerTest:

  • lmer()

BayesFactor:

  • generalTestBF()

  • lmBF()

  • regressionBF()

  • ttestBF()

  • anovaBF()

  • correlationBF()

  • contingencyTableBF()

  • proportionBF()

  • meta.ttestBF()

tidystats:

Methods (by class)

  • htest: tidy_stats method for class 'htest'

  • lm: tidy_stats method for class 'lm'

  • glm: tidy_stats method for class 'glm'

  • anova: tidy_stats method for class 'anova'

  • aov: tidy_stats method for class 'aov'

  • aovlist: tidy_stats method for class 'aovlist'

  • tidystats_descriptives: tidy_stats method for class 'tidystats_descriptives'

  • tidystats_counts: tidy_stats method for class 'tidystats_counts'

  • lmerMod: tidy_stats method for class 'lmerMod'

  • lmerModLmerTest: tidy_stats method for class 'lmerModLmerTest'

  • BFBayesFactor: tidy_stats method for class 'BayesFactor'

  • afex_aov: tidy_stats method for class 'afex_aov'

  • emmGrid: tidy_stats method for class 'emmGrid'

  • emm_list: tidy_stats method for class 'emm_list'

Examples

# Conduct statistical tests
# t-test:
sleep_test <- t.test(extra ~ group, data = sleep, paired = TRUE)

# lm:
ctl <- c(4.17, 5.58, 5.18, 6.11, 4.50, 4.61, 5.17, 4.53, 5.33, 5.14)
trt <- c(4.81, 4.17, 4.41, 3.59, 5.87, 3.83, 6.03, 4.89, 4.32, 4.69)
group <- gl(2, 10, 20, labels = c("Ctl", "Trt"))
weight <- c(ctl, trt)
lm_D9 <- lm(weight ~ group)

# ANOVA:
npk_aov <- aov(yield ~ block + N*P*K, npk)

# Tidy the statistics and store each analysis in a separate variable
list_sleep_test <- tidy_stats(sleep_test)
list_lm_D9 <- tidy_stats(lm_D9)
list_npk_aov <- tidy_stats(npk_aov)

# Now you can inspect each of these variables, e.g.,:
names(list_sleep_test)
#> [1] "method"      "var_equal"   "name"        "statistics"  "alternative"
#> [6] "package"    
str(list_sleep_test)
#> List of 6
#>  $ method     : chr "Paired t-test"
#>  $ var_equal  : logi TRUE
#>  $ name       : chr "extra by group"
#>  $ statistics :List of 6
#>   ..$ estimate :List of 2
#>   .. ..$ name : chr "mean difference"
#>   .. ..$ value: num -1.58
#>   ..$ SE       : num 0.389
#>   ..$ statistic:List of 2
#>   .. ..$ name : chr "t"
#>   .. ..$ value: num -4.06
#>   ..$ df       : num 9
#>   ..$ p        : num 0.00283
#>   ..$ CI       :List of 3
#>   .. ..$ CI_level: num 0.95
#>   .. ..$ CI_lower: num -2.46
#>   .. ..$ CI_upper: num -0.7
#>  $ alternative:List of 2
#>   ..$ direction : chr "two.sided"
#>   ..$ null_value: num 0
#>  $ package    :List of 2
#>   ..$ name   : chr "stats"
#>   ..$ version: chr "4.1.3"