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.

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)

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'

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 "4.0.2"