corr_test {statsExpressions} | R Documentation |
Parametric, non-parametric, robust, and Bayesian correlation test.
corr_test( data, x, y, type = "parametric", k = 2L, conf.level = 0.95, tr = 0.2, bf.prior = 0.707, ... )
data |
A data frame (or a tibble) from which variables specified are to
be taken. Other data types (e.g., matrix,table, array, etc.) will not
be accepted. Additionally, grouped data frames from |
x |
The column in |
y |
The column in |
type |
A character specifying the type of statistical approach:
You can specify just the initial letter. |
k |
Number of digits after decimal point (should be an integer)
(Default: |
conf.level |
Scalar between |
tr |
Trim level for the mean when carrying out |
bf.prior |
A number between |
... |
Additional arguments (currently ignored). |
The returned tibble data frame can contain some or all of the following columns (the exact columns will depend on the statistical test):
statistic
: the numeric value of a statistic
df
: the numeric value of a parameter being modeled (often degrees
of freedom for the test)
df.error
and df
: relevant only if the statistic in question has
two degrees of freedom (e.g. anova)
p.value
: the two-sided p-value associated with the observed statistic
method
: the name of the inferential statistical test
estimate
: estimated value of the effect size
conf.low
: lower bound for the effect size estimate
conf.high
: upper bound for the effect size estimate
conf.level
: width of the confidence interval
conf.method
: method used to compute confidence interval
conf.distribution
: statistical distribution for the effect
effectsize
: the name of the effect size
n.obs
: number of observations
expression
: pre-formatted expression containing statistical details
For examples, see data frame output vignette.
The table below provides summary about:
statistical test carried out for inferential statistics
type of effect size estimate and a measure of uncertainty for this estimate
functions used internally to compute these details
Hypothesis testing and Effect size estimation
Type | Test | CI available? | Function used |
Parametric | Pearson's correlation coefficient | Yes | correlation::correlation() |
Non-parametric | Spearman's rank correlation coefficient | Yes | correlation::correlation() |
Robust | Winsorized Pearson's correlation coefficient | Yes | correlation::correlation() |
Bayesian | Bayesian Pearson's correlation coefficient | Yes | correlation::correlation() |
# for reproducibility set.seed(123) # ----------------------- parametric ----------------------- corr_test(mtcars, wt, mpg) # ----------------------- non-parametric ------------------- corr_test(mtcars, wt, mpg, type = "n") # ----------------------- robust --------------------------- corr_test(mtcars, wt, mpg, type = "r") # ----------------------- Bayesian ------------------------- corr_test(mtcars, wt, mpg, type = "b")