AmoudSurv (version 0.1.0)

MLEGO: General Odds (GO) Model.

Description

A Tractable Parametric General Odds (GO) model's Log-likelihood, MLE and information criterion values. Baseline hazards: NGLL,GLL,MLL,PGW, GG, EW, MKW, LL, TLL, SLL,CLL,SCLL,ATLL, and ASLL

Usage

MLEGO(
  init,
  times,
  status,
  n,
  basehaz,
  z,
  zt,
  method = "BFGS",
  hessian = TRUE,
  conf.int = 0.95,
  maxit = 1000,
  log = FALSE
)

Value

a list containing the output of the optimisation (OPT) and the log-likelihood function (loglik)

Arguments

init

: initial points for optimisation

times

: survival times

status

: vital status (1 - dead, 0 - alive)

n

: The number of the data set

basehaz

: baseline hazard structure including baseline (New generalized log-logistic general odds "NGLLGO" model, generalized log-logisitic general odds "GLLGO" model, modified log-logistic general odds "MLLGO" model,exponentiated Weibull general odds "EWGO" model, power generalized weibull general odds "PGWGO" model, generalized gamma general odds "GGGO" model, modified kumaraswamy Weibull general odds "MKWGO" model, log-logistic general odds "LLGO" model, tangent-log-logistic general odds "TLLGO" model, sine-log-logistic general odds "SLLGO" model, cosine log-logistic general odds "CLLGO" model,secant-log-logistic general odds "SCLLGO" model, arcsine-log-logistic general odds "ASLLGO" model, arctangent-log-logistic general odds "ATLLGO" model, Weibull general odds "WGO" model, gamma general odds "WGO" model, and log-normal general odds "ATLNGO" model.)

z

: design matrix for odds-level effects (p x n), p >= 1

zt

: design matrix for time-dependent effects (q x n), q >= 1

method

:"optim" or a method from "nlminb".The methods supported are: BFGS (default), "L-BFGS", "Nelder-Mead", "SANN", "CG", and "Brent".

hessian

:A function to return (as a matrix) the hessian for those methods that can use this information.

conf.int

: confidence level

maxit

:The maximum number of iterations. Defaults to 1000

log

:log scale (TRUE or FALSE)

Author

Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Christophe Chesneau

Examples

#Example #1
data(alloauto)
time<-alloauto$time
delta<-alloauto$delta
z<-alloauto$type
MLEGO(init = c(1.0,0.50,0.50,0.5,0.5),times = time,status = delta,n=nrow(z),
basehaz = "PGWGO",z = z,zt=z,method = "BFGS",hessian=TRUE, conf.int=0.95,maxit = 1000,log=FALSE)

#Example #2
data(bmt)
time<-bmt$Time
delta<-bmt$Status
z<-bmt$TRT
MLEGO(init = c(1.0,0.50,0.45,0.5),times = time,status = delta,n=nrow(z),
basehaz = "TLLGO",z = z,zt=z,method = "BFGS",hessian=TRUE, conf.int=0.95,maxit = 1000,
log=FALSE)

#Example #3
data("gastric")
time<-gastric$time
delta<-gastric$status
z<-gastric$trt
MLEGO(init = c(1.0,1.0,0.50,0.5,0.5),times = time,status = delta,n=nrow(z),
basehaz = "GLLGO",z = z,zt=z,method = "BFGS",hessian=TRUE, conf.int=0.95,maxit = 1000,log=FALSE)