
Calculate correlations between a target variable and all other numeric variables.
Source:R/cyt_corr.R
cyt_corr.Rd
This function computes Pearson or Spearman correlation coefficients between a specified target variable and all other numeric variables in the input data frame. It also calculates p-values and adjusted p-values (Bonferroni and Benjamini-Hochberg). Optionally, it can perform group-wise correlation analysis and compare correlations between two groups using Fisher's z-transformation.
Usage
cyt_corr(
data,
target,
methods = c("spearman", "pearson"),
group_var = NULL,
compare_groups = FALSE,
progress = NULL
)
Arguments
- data
A data frame containing the variables.
- target
A character string specifying the name of the target variable. Must be a numeric column in
data
.- methods
A character string indicating the correlation method to be used. Can be "pearson" (default) or "spearman".
- group_var
An optional character string specifying the name of a grouping variable in
data
. If provided, group-wise correlations will be calculated.- compare_groups
Logical. If
TRUE
andgroup_var
is provided with at least two levels, the function will compare the correlations between the first two levels ofgroup_var
.
Value
A list containing:
- results
A data frame with overall correlation results, including variable names, correlation coefficients (
r
), p-values (p
), sample size (n
), method, and adjusted p-values (p_bonf
,p_bh
), sorted by absolute correlation coefficient in descending order.- results_groupwise
A data frame with group-wise correlation results, if
group_var
is provided. Includes similar columns asresults
, plus thegroup
identifier.- results_diff
A data frame with results of correlation comparison between the first two groups, if
compare_groups
isTRUE
andgroup_var
has at least two levels. Includesvariable
,r_diff
(difference in correlations),z
(Fisher's z-score),p_diff
(p-value for the difference), and adjusted p-values (p_diff_bonf
,p_diff_bh
).