FlexVarJM (version 0.1.0)

goodness_of_fit: 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)

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

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

Examples

# \donttest{


#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
# }