pcor_matrix: Calculates a partial correlation matrix controlling for one...

In LukasWallrich/timesaveR: Functions to Accelerate (Academic) Data Analysis and Reporting

Calculates a partial correlation matrix controlling for one or more variables

Description

This returns a matrix aligned with the cor_matrix() function after parceling out the effect of one or more other variables. This function requires complete data; incomplete cases are dropped with a warning.

Usage

pcor_matrix(data, given, ...)

Arguments

data	Dataframe. Only numeric variables are included into correlation matrix.
given	A character vector with one or multiple variables in data. It/they will be parceled out from all other variables in data, before the correlation table is calculated.
...	Arguments passed on to cor_matrix var_names A named character vector with new variable names or a tibble as provided by get_rename_tribbles() for variables. If NULL, then the variables are not renamed. If names are provided, only the variables included here are retained. This is most helpful when the results are passed to some print function, such as report_cor_table() conf_level Confidence level to use for confidence intervals, defaults to .95 bootstrap When using FIML estimation (with missing = "fiml"), significance tests and confidence intervals can be bootstrapped. If you want to do that, pass the number of desired bootstrap resamples (e.g., 5000) to this parameter, but beware that this can take a while. seed Pass an integer to set the seed for bootstrapping and thus make this reproducible method method="pearson" is the default value. The alternatives to be passed to cor are "spearman" and "kendall". These last two are much slower, particularly for big data sets. adjust What adjustment for multiple tests should be used? ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"). See p.adjust for details about why to use "holm" rather than "bonferroni").

Examples

# One might want to estimate correlations between health and possible predictors in the ESS
# after parceling out / controling for key demographic attributes:
pcor_matrix(ess_health, given = c("agea", "gndr"), 
   var_names = c("health" = "Health", "weight" = "Weight", "dosprt" = "Sport")) %>% 
   tidy(both_directions = FALSE)

LukasWallrich/timesaveR documentation built on Nov. 29, 2024, 4:47 a.m.