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The correlation is calculated using stats::cor.test.
stats::cor.test
effect_metrics_one_cor( data, col, cross, method = "pearson", labels = TRUE, clean = TRUE, ... )
A volker table containing the requested statistics.
If method = "pearson":
method = "pearson"
R-squared: Coefficient of determination.
n: Number of cases the calculation is based on.
Pearson's r: Correlation coefficient.
ci low / ci high: Lower and upper bounds of the 95% confidence interval.
df: Degrees of freedom.
t: t-statistic.
p: p-value for the statistical test, indicating whether the correlation differs from zero.
stars: Significance stars based on the p-value (*, **, ***).
If method = "spearman":
method = "spearman"
Spearman's rho is displayed instead of Pearson's r.
S-statistic is used instead of the t-statistic.
A tibble.
The column holding metric values.
The column holding metric values to correlate.
The output metrics, TRUE or pearson = Pearson's R, spearman = Spearman's rho.
If TRUE (default) extracts labels from the attributes, see codebook.
Prepare data by data_clean.
Placeholder to allow calling the method with unused parameters from effect_metrics.
library(volker) data <- volker::chatgpt effect_metrics_one_cor(data, sd_age, use_private, metric = TRUE)
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