Main function implementing the MASTA algorithm

Source: R/masta-fit.R

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

masta.fit(
  object,
  cov_group = NULL,
  thresh = 0.7,
  PCAthresh = 0.9,
  seed = 1234,
  seed2 = 100
)

Arguments

object

results returned by the masta.fpca function
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 1234.
seed2

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