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,
...
)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 of dataframe outputs, see examples and this vignette.
Note that all examples are preceded by set.seed() calls for reproducibility.
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 {dplyr} should be
ungrouped before they are entered as data.
The column in data containing the explanatory variable to be
plotted on the x-axis.
The column in data containing the response (outcome) variable to
be plotted on the y-axis.
A character specifying the type of statistical approach:
"parametric"
"nonparametric"
"robust"
"bayes"
You can specify just the initial letter.
Number of digits after decimal point (should be an integer)
(Default: k = 2L).
Scalar between 0 and 1 (default: 95%
confidence/credible intervals, 0.95). If NULL, no confidence intervals
will be computed.
Trim level for the mean when carrying out robust tests. In case
of an error, try reducing the value of tr, which is by default set to
0.2. Lowering the value might help.
A number between 0.5 and 2 (default 0.707), the prior
width to use in calculating Bayes factors and posterior estimates. In
addition to numeric arguments, several named values are also recognized:
"medium", "wide", and "ultrawide", corresponding to r scale values
of 1/2, sqrt(2)/2, and 1, respectively. In case of an ANOVA, this value
corresponds to scale for fixed effects.
Additional arguments (currently ignored).
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 correlation coefficient | Yes | correlation::correlation() |
| Bayesian | Bayesian Pearson's correlation coefficient | Yes | correlation::correlation() |
# \donttest{
# for reproducibility
set.seed(123)
library(statsExpressions)
options(tibble.width = Inf, pillar.bold = TRUE, pillar.neg = TRUE)
# without changing defaults
corr_test(mtcars, x = wt, y = mpg)
# changing defaults
corr_test(mtcars, x = wt, y = mpg, type = "robust")
# }
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