goodness_of_fit: Predictions for the goodness of fit, of the random effects,...

In FlexVarJM: Estimate Joint Models with Subject-Specific Variance

Predictions for the goodness of fit, of the random effects, the current value for each individuals and the cumulative hazard function for both events

Description

Predictions for the goodness of fit, of the random effects, the current value for each individuals and the cumulative hazard function for both events

Usage

goodness_of_fit(object, graph = FALSE, break.times = NULL)

Arguments

object	an object of class lsjm
graph	a boolean to indicate to print graphics, False by default
break.times	a vector of times for the time points of longitudinal graphic

Value

A list which contains the following elements :

tables

A list with the table of the predicted random effect, the table of the predicted current value, table(s) of predictive cumulative hazard function(s)

graphs

A list with 2 or 3 graphs : one for the longitudinal adjustment and one for each risk function

Examples

#Fit a joint model with competing risks and subject-specific variability
example <- lsjm(formFixed = y~visit,
formRandom = ~ visit,
formGroup = ~ID,
formSurv = Surv(time, event ==1 ) ~ 1,
timeVar = "visit",
data.long = Data_toy,
variability_hetero = TRUE,
formFixedVar =~visit,
formRandomVar =~visit,
correlated_re = TRUE,
sharedtype = c("current value", "variability"),
hazard_baseline = "Weibull",
formSlopeFixed =~1,
formSlopeRandom = ~1,
indices_beta_slope = c(2), 
competing_risk = TRUE,
formSurv_CR = Surv(time, event ==2 ) ~ 1,
hazard_baseline_CR = "Weibull",
sharedtype_CR = c("current value", "variability"),
S1 = 100,
S2 = 1000,
nproc = 1,
maxiter = 100,
Comp.Rcpp = TRUE
)

#Assesment of the goodness of fit:
gof <- goodness_of_fit(example, graph = TRUE)
gof$tables
gof$graphs

FlexVarJM documentation built on Nov. 21, 2023, 1:06 a.m.