fmrs.gendata-methods: fmrs.gendata method
In fmrs: Variable Selection in Finite Mixture of AFT Regression and FMR

Description Usage Arguments Value Author(s) See Also Examples

Generates a data set from Finite Mixture of AFT regression models or Finite Mixture of Regression models under the specified setting.

fmrs.gendata(nObs, nComp, nCov, coeff, dispersion, mixProp, rho, umax, ...)

## S4 method for signature 'ANY'
fmrs.gendata(
  nObs,
  nComp,
  nCov,
  coeff,
  dispersion,
  mixProp,
  rho,
  umax,
  disFamily = "lnorm"
)

`nObs`	A numeric value represents sample size
`nComp`	A numeric value represents the order mixture in `FMRs`
`nCov`	A numeric value represents the number of covariates in design matrix
`coeff`	A vector of all regression coefficients including intercepts. It must be a vector of length `nComp` *(`nCov+1`).
`dispersion`	A vector of positive values for dispersion parameters of sub-distributions in `FMRs` models
`mixProp`	A vector of mixing proportions which their sum must be one
`rho`	A numeric value in [-1, 1] which represents the correlation between covariates of design matrix
`umax`	A numeric value represents the upper bound in Uniform distribution for censoring
`...`	Other possible options
`disFamily`	A sub-distribution family. The options are `'norm'` for `FMR` models, `'lnorm'` for mixture of AFT regression models with Log-Normal sub-distributions,`'weibull'` for mixture of AFT regression models with Weibull sub-distributions

A list including reponse, covariates and cenroing variables

Farhad Shokoohi <shokoohi@icloud.com>

Other lnorm, norm, weibull: fmrs.mle(), fmrs.tunsel(), fmrs.varsel()

set.seed(1980)
nComp = 2
nCov = 10
nObs = 500
REP = 500
dispersion = c(1, 1)
mixProp = c(0.4, 0.6)
rho = 0.5
coeff1 = c( 2,  2, -1, -2, 1, 2, 0, 0,  0, 0,  0)
coeff2 = c(-1, -1,  1,  2, 0, 0, 0, 0, -1, 2, -2)
umax = 40

dat <- fmrs.gendata(nObs = nObs, nComp = nComp, nCov = nCov,
 coeff = c(coeff1, coeff2), dispersion = dispersion,
 mixProp =mixProp, rho = rho, umax = umax,
 disFamily = 'lnorm')