| Title: | Bivariate Correlated Frailty Models with Varied Variances |
|---|---|
| Description: | Fit and simulate bivariate correlated frailty models with proportional hazard structure. Frailty distributions, such as gamma and lognormal models are supported for semiparametric procedures. Frailty variances of the two subjects can be varied or equal. Details on the models are available in book of Wienke (2011,ISBN:978-1-4200-7388-1). Bivariate gamma fit is obtained using the approach given in Iachine (1995) with modifications. Lognormal fit is based on the approach by Ripatti and Palmgren (2000) <doi:10.1111/j.0006-341X.2000.01016.x>. Frailty distributions, such as gamma, inverse gaussian and power variance frailty models are supported for parametric approach. |
| Authors: | Mesfin Tsegaye [aut, cre], Yehenew Kifle [aut, ctb] |
| Maintainer: | Mesfin Tsegaye <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 0.1.1 |
| Built: | 2024-11-26 06:27:14 UTC |
| Source: | CRAN |
Fit a semiparametric Bivariate correlated frailty model with Proportional Hazard structure. Here, frailty variances of pairs can be different.
bcfraildv( formula, data, initfrailp = NULL, frailty = c("gamma", "lognormal"), control = bcfraildv.control(), comonvar = FALSE, ... )bcfraildv( formula, data, initfrailp = NULL, frailty = c("gamma", "lognormal"), control = bcfraildv.control(), comonvar = FALSE, ... )
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. If not specified, initial frailty parameters will be obtained from bcfrailph fit for gamma model and from coxph with univariate frailty model and for correlation c(0) for lognormal model. |
frailty |
A type of frailty distribution to be used in fit. Either gamma or lognormal. The default is gamma. |
control |
Arguments to control bcfraildv fit. The default is |
comonvar |
An argument whether to assume common frailty variance. The default is |
... |
further arguments |
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.
stderr-A vector containing the Standard error of the Estimated parameters both covariate coefficients and frailty parameters.
loglilk0- Log likelihood of without frailty model or loglik of coxph fit.
loglilk-Log likelihood of Cox PH model with frailty.
Iloglilk- Log likelihood of with frailty. For gamma fit it is I-likelihood or the likelihood after integrating out the frailty term.For lognormal fit it is the approximate likelihood.
bhaz- an array containing unique event times and estimated baseline hazard.
X-Matrix of observed covariates.
time-the observed survival time.
censor-censoring indicator.
resid-the martingale residuals.
lin.prid-the vector of linear predictors.
frail-estimated Frailty values.
iteration-Number of outer iterations.
e.time-the vector of unique event times.
n.event- the number of events at each of the unique event times.
convergence-an indicator, 0 if converge and 1 otherwise.
Parameters of Bivariate correlated gamma frailty model was estimated using a modified EM approach given in Kifle et al (2022) with modification for different frailty variances in a pair. Parameters of Bivariate correlated lognormal frailty model is based on the penalized partial likelihood approach by Rippatti and Palmgren (2000).
Kifle YG, Chen DG, Haileyesus MT (2022). Multivariate Frailty Models using Survey Weights with Applications to Twins Infant Mortality in Ethiopia. Statistics and Its Interface,106(4), 1\-10.
Rippatti, S. and Palmgren, J (2000). Estimation of multivariate frailty models using penalized partial likelihood. Biometrics, 56: 1016-1022.
set.seed(4) simdata<-simbcfraildv(psize=300, 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$data fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa,frailty="gamma") fitbcfrail # for lognormal set.seed(18) simdata<-simbcfraildv(psize=100, cenr= c(0.2),beta=c(1,-0.7,0.5), frailty=c("lognormal"),frailpar=c(0.5,0.5,-0.25),bhaz=c("exponential"), bhazpar=list(scale = c(0.1)),covartype= c("N","N","B"), covarpar=list(fargs=c(0,0,1),sargs=c(1,1,0.5)),comncovar=2) dataa<-simdata$data #fit fitbcfrlogn=bcfraildv(Surv(time,censor)~ X1+X2+X3+frailty(PID) ,data=dataa,frailty="lognormal") fitbcfrlogn set.seed(4) simdata<-simbcfraildv(psize=300, 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$data ## one can set the initial parameter for the frailty parameters fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa, frailty="gamma",initfrailp = c(0.2,0.2,0.4)) fitbcfrail # Not run #if covariates are not included fitmoe=try(bcfraildv(Surv(time,censor)~0+frailty(PID),data=dataa, frailty="lognormal"),silent = TRUE) fitmoe=try(bcfraildv(Surv(time,censor)~1+frailty(PID),data=dataa),silent = TRUE) # if control is not specified correctly. # if one needs to change only max.iter to be 100, fitmoe=try(bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, control=c(max.iter=100)),silent = TRUE) #the correct way is fitmoe=bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, control=bcfraildv.control(max.iter=100)) fitmoe #if initial frailty parameters are in the boundary of parameter space fitmoe=try(bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, initfrailp=c(0.2,0.3,1)),silent = TRUE) fitmoe=try(bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, initfrailp=c(0,0.5,0.1)),silent = TRUE) #if a frailty distribution other than gamma and lognormal are specified fitmoe=try(bcfraildv(Surv(time,censor)~ X1,data=dataa,frailty="exp"),silent = TRUE) # End Not runset.seed(4) simdata<-simbcfraildv(psize=300, cenr= c(0.3),beta=c(2),frailty=c("gamma"), frailpar=c(0.5,0.5,0.5),bhaz=c("weibull"), bhazpar=list(shape =c(5), scale = c(0.1)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa,frailty="gamma") fitbcfrail # for lognormal set.seed(18) simdata<-simbcfraildv(psize=100, cenr= c(0.2),beta=c(1,-0.7,0.5), frailty=c("lognormal"),frailpar=c(0.5,0.5,-0.25),bhaz=c("exponential"), bhazpar=list(scale = c(0.1)),covartype= c("N","N","B"), covarpar=list(fargs=c(0,0,1),sargs=c(1,1,0.5)),comncovar=2) dataa<-simdata$data #fit fitbcfrlogn=bcfraildv(Surv(time,censor)~ X1+X2+X3+frailty(PID) ,data=dataa,frailty="lognormal") fitbcfrlogn set.seed(4) simdata<-simbcfraildv(psize=300, cenr= c(0.3),beta=c(2),frailty=c("gamma"), frailpar=c(0.5,0.5,0.5),bhaz=c("weibull"), bhazpar=list(shape =c(5), scale = c(0.1)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data ## one can set the initial parameter for the frailty parameters fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa, frailty="gamma",initfrailp = c(0.2,0.2,0.4)) fitbcfrail # Not run #if covariates are not included fitmoe=try(bcfraildv(Surv(time,censor)~0+frailty(PID),data=dataa, frailty="lognormal"),silent = TRUE) fitmoe=try(bcfraildv(Surv(time,censor)~1+frailty(PID),data=dataa),silent = TRUE) # if control is not specified correctly. # if one needs to change only max.iter to be 100, fitmoe=try(bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, control=c(max.iter=100)),silent = TRUE) #the correct way is fitmoe=bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, control=bcfraildv.control(max.iter=100)) fitmoe #if initial frailty parameters are in the boundary of parameter space fitmoe=try(bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, initfrailp=c(0.2,0.3,1)),silent = TRUE) fitmoe=try(bcfraildv(Surv(time,censor)~ X1+frailty(PID),data=dataa, initfrailp=c(0,0.5,0.1)),silent = TRUE) #if a frailty distribution other than gamma and lognormal are specified fitmoe=try(bcfraildv(Surv(time,censor)~ X1,data=dataa,frailty="exp"),silent = TRUE) # End Not run
This is used to set various numeric parameters controlling a bcfraildv model fit as a single list.
bcfraildv.control( max.iter = 500, tol = 1e-04, eval.max = 500, iter.max = 500, trace = 0, abs.tol = 1e-20, rel.tol = 1e-10, x.tol = 1.5e-08, xf.tol = 2.2e-14, step.min = 1, step.max = 1 )bcfraildv.control( max.iter = 500, tol = 1e-04, eval.max = 500, iter.max = 500, trace = 0, abs.tol = 1e-20, rel.tol = 1e-10, x.tol = 1.5e-08, xf.tol = 2.2e-14, step.min = 1, step.max = 1 )
max.iter |
Maximum number of iterations allowed. The default is 500. |
tol |
A tolerance for convergence i.e the maximum differences of loglikelihood between succssive iterations.The default is 1e-04. |
eval.max |
argument used to control nlminb fits used. |
iter.max |
argument used to control nlminb fits used. |
trace |
argument used to control nlminb fits used. |
abs.tol |
argument used to control nlminb fits used. |
rel.tol |
argument used to control nlminb fits used. |
x.tol |
argument used to control nlminb fits used. |
xf.tol |
argument used to control nlminb fits used. |
step.min |
argument used to control nlminb fits used. |
step.max |
argument used to control nlminb fits used. |
A list of control parameters.
Fit a parametric Bivariate correlated gamma, inverse gaussian and power variance frailty models with Proportional Hazard structure.
bcfrailpar( formula, data, initfrailp = NULL, inithazp = NULL, initbeta = NULL, haz = c("weibull", "gompertz", "exponential"), frailty = c("gamma", "invgauss", "pv"), comonvar = TRUE, ... )bcfrailpar( formula, data, initfrailp = NULL, inithazp = NULL, initbeta = NULL, haz = c("weibull", "gompertz", "exponential"), frailty = c("gamma", "invgauss", "pv"), comonvar = TRUE, ... )
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. |
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.
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 runset.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
Generics to print the S3 class bcfraildv.
## S3 method for class 'bcfraildv' print(x, ...)## S3 method for class 'bcfraildv' print(x, ...)
x |
A class |
... |
ignored |
Calls print.bcfraildv().
An object of print.bcfraildv, with some more human-readable results from bcfraildv object.
The summary function is currently identical to the print function.
set.seed(4) simdata<-simbcfraildv(psize=300, 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$data fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa) fitbcfrail summary(fitbcfrail)set.seed(4) simdata<-simbcfraildv(psize=300, cenr= c(0.3),beta=c(2),frailty=c("gamma"), frailpar=c(0.5,0.5,0.5),bhaz=c("weibull"), bhazpar=list(shape =c(5), scale = c(0.1)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa) fitbcfrail summary(fitbcfrail)
Generics to print the S3 class bcfrailpar.
## S3 method for class 'bcfrailpar' print(x, ...)## S3 method for class 'bcfrailpar' print(x, ...)
x |
A class |
... |
ignored |
Calls print.bcfrailpar().
An object of print.bcfrailpar, with some more human-readable results from bcfrailpar object.
The summary function is currently identical to the print function.
set.seed(4) simdata<-simbcfraildv(psize=500, cenr= c(0),beta=c(-1),frailty=c("gamma"), frailpar=c(0.4,0.4,0.5),bhaz=c("weibull"), bhazpar=list(shape =c(0.9), scale = c(2)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data ## the simulated data set #fit parbcfit=bcfrailpar(Surv(time, censor) ~ X1+frailty(PID),data=dataa) parbcfitset.seed(4) simdata<-simbcfraildv(psize=500, cenr= c(0),beta=c(-1),frailty=c("gamma"), frailpar=c(0.4,0.4,0.5),bhaz=c("weibull"), bhazpar=list(shape =c(0.9), scale = c(2)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data ## the simulated data set #fit parbcfit=bcfrailpar(Surv(time, censor) ~ X1+frailty(PID),data=dataa) parbcfit
Simulate data from bivariate correlated gamma or lognormal frailty models with one covariate.
simbcfraildv( psize, cenr = c(0), beta = c(0.5), frailty, frailpar = c(0.5, 0.5, 0.25), bhaz = c("weibull"), bhazpar = list(shape = c(0.5), scale = c(0.01)), covartype = c("B"), covarpar = list(fargs = c(1), sargs = c(0.5)), inpcovar = NULL, inpcen = NULL, comncovar = NULL )simbcfraildv( psize, cenr = c(0), beta = c(0.5), frailty, frailpar = c(0.5, 0.5, 0.25), bhaz = c("weibull"), bhazpar = list(shape = c(0.5), scale = c(0.01)), covartype = c("B"), covarpar = list(fargs = c(1), sargs = c(0.5)), inpcovar = NULL, inpcen = NULL, comncovar = NULL )
psize |
pair size. |
cenr |
censored rate. The default is zero.. |
beta |
Covariate coefficient. |
frailty |
A type of frailty distribution to be used. Either gamma or lognormal. |
frailpar |
vector of frailty parameters, variance and correlation respectively. The default is c(0.5,0.5,0.25) meaning both variances are 0.5 and correlation 0.25. |
bhaz |
A type of baseline hazard distribution to be used. it can be weibull, gompertz or exponential. |
bhazpar |
is a |
covartype |
specified the distribution from which covariate(s) are goining to be sampled. covartype can be c("B","N","U")denoting binomial, normal or uniform, respectively. For example, |
covarpar |
is a |
inpcovar |
is a |
inpcen |
is a |
comncovar |
if common covariates are needed. |
An object of class simbcfraildv that contain the following:
data A data frame i.e, the simulated data set. IID is individual Id, PID is pair ID, time is the simulated survival time, censor is censoring indicator and X1 denote the simulated covariate.
numberofpair The specified number of pairs.
censoredrate The specified censored rate.
fraildist The specified frailty distribution.
frailpar The specified frailty parameters.
set.seed(4) simdata<-simbcfraildv(psize=300, 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$data head(dataa) # If data generation is from bivariate correlated lognormal frailty model, set.seed(18) simdata<-simbcfraildv(psize=100, cenr= c(0.2),beta=c(1,-0.7,0.5),frailty=c("lognormal"), frailpar=c(0.5,0.8,-0.25),bhaz=c("exponential"), bhazpar=list(scale = c(0.1)),covartype= c("N","N","B"), covarpar=list(fargs=c(0,0,1),sargs=c(1,1,0.5))) dataa<-simdata$data head(dataa) # If common covariate is desired, i.e., here out of the three covariates #covariate 2 is common for the pair. set.seed(18) simdata<-simbcfraildv(psize=100, cenr= c(0.2),beta=c(1,-0.7,0.5),frailty=c("lognormal"), frailpar=c(0.5,0.8,-0.25),bhaz=c("exponential"), bhazpar=list(scale = c(0.1)),covartype= c("N","N","B"), covarpar=list(fargs=c(0,0,1),sargs=c(1,1,0.5)),comncovar=2) dataa<-simdata$data head(dataa) # If the data generation is from bivariate correlated gamma frailty model, #weibull baseline and without covariate, set.seed(4) simdata<-simbcfraildv(psize=300, cenr= c(0.3),beta=NULL,frailty=c("gamma"), frailpar=c(0.5,0.6,0.5),bhaz=c("weibull"),bhazpar=list(shape =c(5), scale = c(0.1))) dataa<-simdata$data head(dataa)set.seed(4) simdata<-simbcfraildv(psize=300, 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$data head(dataa) # If data generation is from bivariate correlated lognormal frailty model, set.seed(18) simdata<-simbcfraildv(psize=100, cenr= c(0.2),beta=c(1,-0.7,0.5),frailty=c("lognormal"), frailpar=c(0.5,0.8,-0.25),bhaz=c("exponential"), bhazpar=list(scale = c(0.1)),covartype= c("N","N","B"), covarpar=list(fargs=c(0,0,1),sargs=c(1,1,0.5))) dataa<-simdata$data head(dataa) # If common covariate is desired, i.e., here out of the three covariates #covariate 2 is common for the pair. set.seed(18) simdata<-simbcfraildv(psize=100, cenr= c(0.2),beta=c(1,-0.7,0.5),frailty=c("lognormal"), frailpar=c(0.5,0.8,-0.25),bhaz=c("exponential"), bhazpar=list(scale = c(0.1)),covartype= c("N","N","B"), covarpar=list(fargs=c(0,0,1),sargs=c(1,1,0.5)),comncovar=2) dataa<-simdata$data head(dataa) # If the data generation is from bivariate correlated gamma frailty model, #weibull baseline and without covariate, set.seed(4) simdata<-simbcfraildv(psize=300, cenr= c(0.3),beta=NULL,frailty=c("gamma"), frailpar=c(0.5,0.6,0.5),bhaz=c("weibull"),bhazpar=list(shape =c(5), scale = c(0.1))) dataa<-simdata$data head(dataa)
Generics to print the S3 class bcfraildv.
## S3 method for class 'bcfraildv' summary(object, ...)## S3 method for class 'bcfraildv' summary(object, ...)
object |
A class |
... |
ignored |
Calls print.bcfraildv().
An object of print.bcfraildv, with some more human-readable results from bcfraildv object.
The summary function is currently identical to the print function.
set.seed(4) simdata<-simbcfraildv(psize=300, 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$data fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa) fitbcfrail summary(fitbcfrail)set.seed(4) simdata<-simbcfraildv(psize=300, cenr= c(0.3),beta=c(2),frailty=c("gamma"), frailpar=c(0.5,0.5,0.5),bhaz=c("weibull"), bhazpar=list(shape =c(5), scale = c(0.1)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data fitbcfrail=bcfraildv(Surv(time,censor)~ X1+frailty(PID) ,data=dataa) fitbcfrail summary(fitbcfrail)
Generics to print the S3 class bcfrailpar.
## S3 method for class 'bcfrailpar' summary(object, ...)## S3 method for class 'bcfrailpar' summary(object, ...)
object |
A class |
... |
ignored |
Calls print.bcfrailpar().
An object of summary.bcfrailpar, with some more human-readable results from bcfrailpar object.
The summary function is currently identical to the print function.
set.seed(40) simdata<-simbcfraildv(psize=500, cenr= c(0.3),beta=c(-1),frailty=c("gamma"), frailpar=c(0.4,0.4,0.5),bhaz=c("gompertz"), bhazpar=list(shape =c(0.09), scale = c(0.2)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data fitbcfrail=bcfrailpar(Surv(time,censor)~ X1+frailty(PID) , data=dataa,haz="gompertz") fitbcfrail summary(fitbcfrail)set.seed(40) simdata<-simbcfraildv(psize=500, cenr= c(0.3),beta=c(-1),frailty=c("gamma"), frailpar=c(0.4,0.4,0.5),bhaz=c("gompertz"), bhazpar=list(shape =c(0.09), scale = c(0.2)), covartype= c("B"),covarpar=list(fargs=c(1),sargs=c(0.5))) dataa<-simdata$data fitbcfrail=bcfrailpar(Surv(time,censor)~ X1+frailty(PID) , data=dataa,haz="gompertz") fitbcfrail summary(fitbcfrail)