bcfrailpar {bcfrailphdv} | R Documentation |
Parametric bivariate correlated frailty models fit.
Description
Fit a parametric Bivariate correlated gamma, inverse gaussian and power variance frailty models with Proportional Hazard structure.
Usage
bcfrailpar(
formula,
data,
initfrailp = NULL,
inithazp = NULL,
initbeta = NULL,
haz = c("weibull", "gompertz", "exponential"),
frailty = c("gamma", "invgauss", "pv"),
comonvar = TRUE,
...
)
Arguments
formula |
A formula object, with the response on the left of a ~ operator, and the terms on the right. The response must be a survival object as returned by the Surv function. |
data |
A dataframe contain survival time, censor, covariate etc with data in columns. |
initfrailp |
Initial estimates for the frailty parameters. The default is c(0.5,0.5). |
inithazp |
Initial estimates for the baseline hazard distribution parameters. The default is c(0.05) for both scale and shape parameters. |
initbeta |
Initial estimates for the covariate coefficients if there are any included. The default is taken from coxph fit. |
haz |
A baseline hazard distribution. Either weibull, gompertz or exponential distributions are possible. |
frailty |
A type of frailty distribution. Either gamma, inverse gaussian |
comonvar |
An argument whether to assume common frailty variance. The default is |
... |
further arguments. |
Value
An object of that contains the following components.
-
coefficients
- A vector of estimated Covariate coefficients. -
frailparest
- A vector of estimated Frailty parameters i.e. frailty variance and correlation. -
basehazpar
- A vector of estimated baseline hazard parameters i.e. scale and shape. -
stderr
-A vector containing the Standard errors of the Estimated parameters with the order of frailty parameters,baseline hazard parameters and covariate coefficients. -
vcov
- Variance Covariance matrix of the Estimated parameters. -
loglik
-Log likelihood of the model. -
AIC
-AIC of the model. -
BIC
-BIC of the model. -
iterations
-Number of outer iterations.SeeconstrOptim for further. -
convergence
-An indicator of convergence. SeeconstrOptim for further.
Examples
set.seed(4)
simdata<-simbcfraildv(psize=500, cenr= c(0.3),beta=c(2),frailty=c("gamma"),
frailpar=c(0.5,0.5,0.5),bhaz=c("weibull"),
bhazpar=list(shape =c(5), scale = c(0.1)),
covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5)))
dataa<-simdata$data
fitbcfrail=bcfrailpar(Surv(time,censor)~ X1+frailty(PID) ,data=dataa,frailty="gamma")
fitbcfrail
set.seed(18)
simdata<-simbcfraildv(psize=300, cenr= c(0.3),beta=c(2),frailty=c("gamma"),
frailpar=c(0.5,0.5,0.4),bhaz=c("weibull"),
bhazpar=list(shape =c(5), scale = c(0.1)),
covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5)))
dataa<-simdata$data
#fit with power variance frailty distribution
fitbcfrail=bcfrailpar(Surv(time,censor)~ X1+frailty(PID) ,data=dataa,
frailty="pv")
fitbcfrail
## one can set the initial parameter for the frailty parameters
fitbcfrail=bcfrailpar(Surv(time,censor)~ X1+frailty(PID) ,data=dataa,initfrailp = c(0.4,0.3),
frailty="gamma")
fitbcfrail
# Not run
#if initial frailty parameters are in the boundary of parameter space
fitmoe=try(bcfrailpar(Surv(time,censor)~ X1+frailty(PID),data=dataa,
initfrailp=c(0.2,1)),silent = TRUE)
#if a frailty distribution other than gamma, invgauss or pv is specified
fitmoe=try(bcfrailpar(Surv(time,censor)~ X1,data=dataa,frailty="exp"),silent = TRUE)
# End Not run