coda_glmnet {coda4microbiome} | R Documentation |
coda_glmnet
Description
Microbial signatures in cross-sectional studies. The algorithm performs variable selection through penalized regression on the set of all pairwise log-ratios. The result is expressed as the (weighted) balance between two groups of taxa. It allows the use of non-compositional covariates.
Usage
coda_glmnet(
x,
y,
covar = NULL,
lambda = "lambda.1se",
nvar = NULL,
alpha = 0.9,
nfolds = 10,
showPlots = TRUE,
coef_threshold = 0
)
Arguments
x |
abundance matrix or data frame (rows are samples, columns are variables (taxa)) |
y |
outcome (binary or continuous); data type: numeric, character or factor vector |
covar |
data frame with covariates (default = NULL) |
lambda |
penalization parameter (default = "lambda.1se") |
nvar |
number of variables to use in the glmnet.fit function (default = NULL) |
alpha |
elastic net parameter (default = 0.9) |
nfolds |
number of folds |
showPlots |
if TRUE, shows the plots (default = TRUE) |
coef_threshold |
coefficient threshold, minimum absolute value of the coefficient for a variable to be included in the model (default =0) |
Value
if y is binary: list with "taxa.num","taxa.name","log-contrast coefficients","predictions","apparent AUC","mean cv-AUC","sd cv-AUC","predictions plot","signature plot" if not:list with "taxa.num","taxa.name","log-contrast coefficients","predictions","apparent Rsq","mean cv-MSE","sd cv-MSE","predictions plot","signature plot"
Author(s)
M. Calle - T. Susin
Examples
data(Crohn, package = "coda4microbiome")
set.seed(123)
coda_glmnet(x_Crohn[,(1:10)],y_Crohn,showPlots= FALSE)