flassomsm (version 0.1.0)

covselec: covselec

Description

This function is used for state-wise weighted biomarker selection for both lasso and fused type penalty and is applicable for a multi-state model encompassing finite number of states and transitions.

Usage

covselec(
  data,
  time_cols,
  status_cols,
  covariate_range,
  alphas,
  lambdas,
  method = "lasso",
  p_cut = 0.2,
  verbose = TRUE
)

Value

list containing selected biomarkers for each state

Arguments

data

Multi state dataset with multiple state and time column in the data

time_cols

time-to-event columns for each state in a MSM

status_cols

status columns for corresponding states in a MSM

covariate_range

range of high-dimensional biomarkers in the MSM dataset

alphas

penalty parameter for lasso type penalty

lambdas

penalty parameter for fused type penalty

method

method to be selected for covariate selction either "lasso" or "fused"

p_cut

association between variables

verbose

Logical indicating whether to print progress messages

Author

Atanu Bhattacharjee,Gajendra Kumar Vishwakarma,Abhipsa Tripathy

Details

This function deals with biomarker selection for each state of a multi-state model each phase involves selecting significant biomarkers for that state from two sets: one comprising covariates from the preceding state, and another consisting of all covariates from the current state that were previously unselected.If the total number of biomarkers is referred to as the overall count, and each state in a multi-state model has its own number of selected biomarkers, then in a model with four states, the number of selected biomarkers for the fourth state is determined using a weighted combination. Specifically, it depends on the number of biomarkers selected in the third state, a modified set from the second state, another modified set from the first state

Examples

##
set.seed(123)
covselec(data= highDmsm,time_cols = c("state1", "state2","state3"),
status_cols = c("status1", "status2","status3"),
covariate_range = 8:107,alphas=c(0.40,0.45,0.60),
lambdas = c(0.1,0.15,0.20),method = "lasso", p_cut = 0.2,verbose = TRUE)
##