Resampling-based Markov Network
Usage
ensemble_ggm(
dat,
num_iteration = 5,
boot = FALSE,
sub_ratio = NULL,
sample_class = NULL,
correlated = FALSE,
cluster_ratio = 1,
estimate_CI = FALSE,
method = c("D-S_NW_SL", "B_NW_SL"),
alpha = 0.05,
global = TRUE,
n_cores = NULL
)Arguments
- dat
a n x p data frame/matrix with row and column names, n = number of samples, and p = number of features
- num_iteration
an integer, indicating the number of iteration of resampling
- boot
a logical variable, if TRUE, then perform bootstrap resampling, else, perform subsampling
- sub_ratio
a numerical value between 0 and 1, indicating the subsampling ratio
- sample_class
a atomic vector, indicating the class of each sample, if != NULL, then stratified sampling is performed
a boolean variable indicating if samples are correlated
- cluster_ratio
the ratio of clusters to be bootstrapped
- estimate_CI
if TRUE, save the partial correlation of each edge over the iteration, else, return the average partial correlation
- method
Methods for statistical inference
- alpha
A user-supplied sequence of pre-specified alpha levels for FDR control
- global
global == TRUE -> ± partial correlations; global == FALSE -> only positive correlations
- n_cores
number of cores for parallel computing