MLEPO: Proportional Odds (PO) model.

In AmoudSurv: Tractable Parametric Odds-Based Regression Models

Proportional Odds (PO) model.

Description

Tractable Parametric Proportional Odds (PO) model's maximum likelihood estimation, log-likelihood, and information criterion. Baseline hazards: NGLL,GLL,MLL,PGW, GG, EW, MKW, LL, TLL, SLL,CLL,SCLL,ATLL, and ASLL

Usage

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

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 proportional odds "NGLLPO" model, generalized log-logisitic proportional odds "GLLPO" model, modified log-logistic proportional odds "MLLPO" model, exponentiated Weibull proportional odds "EWPO" model, power generalized weibull proportional odds "PGWPO" model, generalized gamma proportional odds "GGPO" model, modified kumaraswamy Weibull proportional odds "MKWPO" model, log-logistic proportional odds "PO" model, tangent-log-logistic proportional odds "TLLPO" model, sine-log-logistic proportional odds "SLLPO" model, cosine log-logistic proportional odds "CLLPO" model, secant-log-logistic proportional odds "SCLLPO" model, arcsine-log-logistic proportional odds "ASLLPO" model, and arctangent-log-logistic proportional odds "ATLLPO" model, Weibull proportional odds "WPO" model, gamma proportional odds "GPO" model, and log-normal proportional odds "LNPO" model.)
z	: design matrix for covariates (p x n), p >= 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)

Value

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

Author(s)

Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Christophe Chesneau [e-mail]

Examples

#Example #1
data(alloauto)
time<-alloauto$time
delta<-alloauto$delta
z<-alloauto$type
MLEPO(init = c(1.0,0.40,1.0,0.50),times = time,status = delta,n=nrow(z),
basehaz = "GLLPO",z = 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
MLEPO(init = c(1.0,1.0,0.5),times = time,status = delta,n=nrow(z),
basehaz = "SLLPO",z = 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
MLEPO(init = c(1.0,0.50,1.0,0.75),times = time,status = delta,n=nrow(z),
basehaz = "PGWPO",z = z,method = "BFGS",hessian=TRUE, conf.int=0.95,maxit = 1000,
log=FALSE)

#Example #4
data("larynx")
time<-larynx$time
delta<-larynx$delta
larynx$age<-as.numeric(scale(larynx$age))
larynx$diagyr<-as.numeric(scale(larynx$diagyr))
larynx$stage<-as.factor(larynx$stage)
z<-model.matrix(~ stage+age+diagyr, data = larynx)
MLEPO(init = c(1.0,1.0,0.5,0.5,0.5,0.5,0.5,0.5),times = time,status = delta,n=nrow(z),
basehaz = "ATLLPO",z = z,method = "BFGS",hessian=TRUE, conf.int=0.95,maxit = 1000,log=FALSE)

AmoudSurv documentation built on Sept. 8, 2022, 5:09 p.m.