bs_pm,mina,ANY-method {mina} | R Documentation |
Inferring the network of different group of samples and test significance by permutation.
## S4 method for signature 'mina,ANY' bs_pm( x, group, g_size = 88, s_size = 30, rm = TRUE, per = 0.1, sig = TRUE, bs = 6, pm = 6, individual = FALSE, out_dir = "./", ... ) ## S4 method for signature 'mina,character' bs_pm( x, group, g_size = 88, s_size = 30, rm = TRUE, per = 0.1, sig = TRUE, bs = 6, pm = 6, individual = FALSE, out_dir = "./", ... )
x |
An object of class 'mina' with @norm and @des defined. |
group |
The column name of descriptive file @des for comparison. |
g_size |
The cutoff of group size used for filtering, default is 88. |
s_size |
The number of samples used for network inference during bootstrap and permutation (when 'sig' is TRUE), it should be smaller than g_size / 2 to make sure the randomness; default is 30. |
rm |
Filtering the components present in less than 'per' of the samples from compared groups, default TRUE. |
per |
The percentage of present samples for filtering, default is 0.1. |
sig |
Whether to test the significance, skip the permutation when set as FALSE, default is TRUE. |
bs |
The times for bootstrap network inference, default is 6. |
pm |
The times for permuatated samples network inference, default is 6. |
individual |
Whether to output the bootstrap and permutation results of each comparison individually, default is FALSE. |
out_dir |
The output directory if 'individual' is TRUE, default is the current working directory |
... |
Additional parameters. |
x The same object with @multi and @perm defined.
maize <- new("mina", tab = maize_asv2, des = maize_des2) maize <- norm_tab(maize, method = "raref") maize <- fit_tabs(maize) maize <- bs_pm(maize, group = "Compartment", per = 0.5)