R/run_permutation_test_cor.R
run_permutation_test_cor.RdThis function performs a permutation test to assess the significance of the correlation between two variables in a data frame. It computes the observed correlation, generates a null distribution of correlations by permuting one of the variables, and calculates an empirical p-value.
run_permutation_test_cor(
df,
x_var,
y_var,
B = 1000,
method = "pearson",
use = "complete.obs",
workers = 1,
seed = 1,
drop_duplicates = FALSE,
save_path = NULL,
force_new = FALSE
)A data frame containing the variables of interest.
A string specifying the name of the first variable (e.g., "info_content").
A string specifying the name of the second variable (e.g., "n_cell_types_sig").
An integer specifying the number of permutations to perform (default is 1000).
A string specifying the correlation method to use ("pearson", "spearman", etc.; default is "pearson").
an optional character string giving a
method for computing covariances in the presence
of missing values. This must be (an abbreviation of) one of the strings
"everything", "all.obs", "complete.obs",
"na.or.complete", or "pairwise.complete.obs".
An integer specifying the number of parallel workers to use (default is 1).
An integer specifying the random seed for reproducibility (default is 1).
A logical indicating whether to drop duplicate (x_var, y_var) pairs before analysis (default is TRUE).
File path to cache results.
Ignore cached results.
A data table containing the observed correlation, empirical p-value, and null distribution statistics.
# Example usage:
df <- data.frame(
info_content = rnorm(100),
n_cell_types_sig = rnorm(100)
)
result <- run_permutation_test_cor(
df, x_var="info_content", y_var="n_cell_types_sig", B = 1000)
#>
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#>
print(result)
#> x_var y_var method B obs_p obs_cor p_emp
#> <char> <char> <char> <num> <num> <num> <num>
#> 1: info_content n_cell_types_sig pearson 1000 0.8221516 -0.0227611 0.805
#> null_mean null_ci_lo null_ci_hi
#> <num> <num> <num>
#> 1: -0.003629792 -0.1955825 0.192586