masta.fit: Main function implementing the MASTA algorithm

In celehs/PETLER: Multi-modal Automated Survival Time Annotation

Description

This function builds an algorithm to identify the occurrence of event outcome from trajectories of several predictors.

Usage

masta.fit(
  object,
  survival,
  follow_up_time,
  Tend = 1,
  cov_group = NULL,
  thresh = 0.7,
  PCAthresh = 0.9,
  seed = 100
)

Arguments

object	The object returned by the fpca.combine function
survival	the labeled data. The columns should be 1) id, 2) event indicator, 3) event time, followed by baseline predictors.
follow_up_time	the follow-up data
Tend	a scalar value. NA in SX is replaced by this. The default is 1.
cov_group	a vector of consecutive integers describing the grouping only for covariates. When NULL is specified (default), each covariate will be in different group.
thresh	a default is 0.7, which means if there are codes with >70% patients no codes, only use first code time.
PCAthresh	a threshold value for PCA. Default is 0.9.
seed	random seed used for the sampling. Default is 100.

Value

A list with components:

bgbbest_FromChengInit_BFGS	Details of the fitted model
Cstat_BrierSc_ChengInit_BFGS	Performance of the derived algorithm. C-statistics, etc.
group	A vector of consecutive integers describing the grouping coefficients

celehs/PETLER documentation built on Sept. 3, 2021, 8:21 a.m.