coda_coxnet {coda4microbiome} | R Documentation |
coda_coxnet
Description
Microbial signatures in survival studies The algorithm performs variable selection through an elastic-net penalized Cox regression conveniently adapted to CoDA. The result is expressed as the (weighted) balance between two groups of taxa. It allows the use of non-compositional covariates.
Usage
coda_coxnet(
x,
time,
status,
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)) |
time |
time to event or follow up time for right censored data. Must be a numericvector. |
status |
event occurrence. Vector (type: numeric or logical) specifying 0, or FALSE, for no event occurrence, and 1, or TRUE, for event occurrence. |
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
list with "taxa.num","taxa.name","log-contrast coefficients","risk.score","apparent Cindex","mean cv-Cindex","sd cv-Cindex","risk score plot","signature plot".
Author(s)
M. Calle, M. Pujolassos, T. Susin
Examples
data(data_survival, package = "coda4microbiome")
time <- Event_time
status <- Event
set.seed(12345)
coda_coxnet(x = x,
time = Event_time,
status = Event,
covar = NULL,
lambda = "lambda.1se", nvar = NULL,
alpha = 0.9, nfolds = 10, showPlots = TRUE, coef_threshold = 0)