AHMLE {AHSurv} | R Documentation |
Overall Survival AH model.
Description
The flexible parametric accelerated hazards (AH) model's maximum likelihood estimation, log-likelihood, and information criterion. Baseline hazards: NGLL, GLL,KW, EW, MLL, PGW, GG, MKW, Log-logistic, Weibull, Log-normal, Burr-XII, and Gamma
Usage
AHMLE(
init,
time,
delta,
n,
basehaz,
z,
method = "Nelder-Mead",
maxit = 1000,
log = FALSE
)
Arguments
init |
: initial points for optimisation |
time |
: survival times |
delta |
: vital indicator (0-alive,1 - dead,) |
n |
: The number of the observations of the data set |
basehaz |
: baseline hazard structure including baseline (NGLLAH,GLLAH,EWAH,KWAH,MLLAH,PGWAH,GGAH, MKWAH,LLAH,WAH,GAH,LNAH,BXIIAH) |
z |
: design matrix for covariates (p x n), p >= 1 |
method |
:"nlminb" or a method from "optim" |
maxit |
:The maximum number of iterations. Defaults to 1000 |
log |
:log scale (TRUE or FALSE) |
Format
By default the function calculates the following values:
AIC: Akaike Information Criterion;
CAIC: Consistent Akaikes Information Criterion;
BIC: Bayesian Information Criterion;
BCAIC: Bozdogan’s Consistent Akaike Information Criterion;
HQIC: Hannan-Quinn information criterion;
par: maximum likelihood estimates;
Value: value of the likelihood function;
Convergence: 0 indicates successful completion and 1 indicates that the iteration limit maxit.
Details
The function AHMLE returns MLE estimates and information criterion.
Value
a list containing the output of the optimisation (OPT) and the information criterion including (AIC, BIC, CAIC, BCAIC, and HQIC).
Author(s)
Abdisalam Hassan Muse, Samuel Mwalili, Oscar Ngesa, Mutua Kilai, abdisalam.hassan@amoud.edu.so
Examples
#Example #1
data(ipass)
time<-ipass$time
delta<-ipass$status
z<-ipass$arm
AHMLE(init = c(1.0,1.0,1.0,0.5),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAH",z = z,method = "Nelder-Mead",
maxit = 1000)
#Example #2
data(bmt)
time<-bmt$Time
delta<-bmt$Status
z<-bmt$TRT
AHMLE(init = c(1.0,1.0,1.0,0.5),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAH",z = z,method = "Nelder-Mead",
maxit = 1000)
#Example #3
data("e1684")
time<-e1684$FAILTIME
delta<-e1684$FAILCENS
TRT<-e1684$TRT
AGE<-e1684$TRT
z<-as.matrix(cbind(scale(TRT), scale(AGE) ))
AHMLE(init = c(1.0,1.0,1.0,0.5,0.75),time = time,delta = delta,n=nrow(z),
basehaz = "GLLAH",z = z,method = "Nelder-Mead",maxit = 1000)
#Example #4
data("LeukSurv")
time<-LeukSurv$time
delta<-LeukSurv$cens
age<-LeukSurv$age
wbc<-LeukSurv$wbc
tpi<-LeukSurv$tpi
z<-as.matrix(cbind(scale(age), scale(tpi),scale(wbc) ))
AHMLE(init = c(1.0,1.0,1.0,1.0,0.5,0.65,0.85),time = time,delta = delta,n=nrow(z),
basehaz = "NGLLAH",z = z,method = "Nelder-Mead",maxit = 1000)