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)
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)
tidy_stats(htest)
: tidy_stats method for class 'htest'tidy_stats(lm)
: tidy_stats method for class 'lm'tidy_stats(glm)
: tidy_stats method for class 'glm'tidy_stats(anova)
: tidy_stats method for class 'anova'tidy_stats(aov)
: tidy_stats method for class 'aov'tidy_stats(aovlist)
: tidy_stats method for class 'aovlist'tidy_stats(tidystats_descriptives)
: tidy_stats method for class 'tidystats_descriptives'tidy_stats(tidystats_counts)
: tidy_stats method for class 'tidystats_counts'tidy_stats(lmerMod)
: tidy_stats method for class 'lmerMod'tidy_stats(lmerModLmerTest)
: tidy_stats method for class 'lmerModLmerTest'tidy_stats(BFBayesFactor)
: tidy_stats method for class 'BayesFactor'tidy_stats(afex_aov)
: tidy_stats method for class 'afex_aov'tidy_stats(emmGrid)
: tidy_stats method for class 'emmGrid'tidy_stats(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 "4.2.2"