coda4microbiome (version 0.2.4)

coda_coxnet: 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
)

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".

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)

Author

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)