JMbayes2 (version 0.6-0)

ppcheck: Posterior Predictive Checks for Joint Models

Description

It computes various posterior predictive checks for joint models.

Usage

ppcheck(object, nsim = 40L, newdata = NULL, seed = 123L,
    process = c("longitudinal", "event", "joint"),
    type = c("ecdf", "average-evolution", "variance-function",
             "variogram", "surv-uniform"),
    CI_ecdf = c("none", "binomial", "Dvoretzky-Kiefer-Wolfowitz"), CI_loess = FALSE,
    outcomes = Inf, percentiles = c(0.025, 0.975),
    random_effects = c("posterior_means", "mcmc", "prior"),
    params_mcmc = NULL, Fforms_fun = NULL, plot = TRUE, add_legend = TRUE,
    pos_legend = c("bottomright", "right"), main = "", xlab = NULL,
    ylab = NULL, col_obs = "black", col_rep = "lightgrey", lty_obs = 1,
    lty_rep = 1, lwd_obs = 1.5, lwd_rep = 1, line_main = NA,
    cex.main = 1.2, ylim = NULL, ...)

Value

a figure with the checks.

Arguments

object

an object inheriting from class "jm".

nsim

a numeric scalar denoting the number of replicated data.

newdata

a data.frame based on which to simulate replicated data. Relevant for cross-validated posterior predictive checks.

seed

the seed to use.

process

a character string indicating for which process to do the checks.

type

a character string indicating the type of checks. The default ecdf compares the empirical distribution function of the replicated data with the one of the observed data. For continous longitudinal outcomes, the average evolution, variance function and the sample variogram can also be used.

CI_ecdf

character string indicating the type of confidence interval for the emprical distribution function.

CI_loess

logical; if TRUE a 0.95 confidence interval for the loess curve is plotted.

outcomes

a numeric vector of indices indicating for which longitudinal outcomes to do the checks. The deault value Inf implies checking all longitudinal outcomes.

percentiles

a numeric vector of length two indicating the percentiles of the observed data to use when depicting the checks.

random_effects

a character string indicating what to do with the random effects.

params_mcmc

a list with an MCMC sample of the model parameters.

Fforms_fun

a function that calculates the functional forms.

plot

logical; if TRUE a plot is produced, otherwise the function returns a list with the values to create the figure.

add_legend

logical; if TRUE a legend is added.

pos_legend

character string indicating the position of the legend.

main, xlab, ylab, col_obs, col_rep, lty_obs, lty_rep, lwd_obs, lwd_rep, line_main, cex.main, ylim

graphical parameters.

...

extra argument passed to plot method.

Author

Dimitris Rizopoulos

Examples

# \donttest{
# Cox model for the composite event death or transplantation
pbc2.id$status2 <- as.numeric(pbc2.id$status != 'alive')
pbc2$status2 <- as.numeric(pbc2$status != 'alive')
CoxFit <- coxph(Surv(years, status2) ~ sex, data = pbc2.id)

# a linear mixed model for log serum bilirubin
fm1 <- lme(log(serBilir) ~ ns(year, 3) * sex, data = pbc2,
           random = list(id = pdDiag(~ ns(year, 3))))

# the joint model
jointFit <- jm(CoxFit, fm1, time_var = "year", save_random_effects = TRUE)

ppcheck(jointFit)

FF <- function (t, betas, bi, data) {
    sex <- as.numeric(data$sex == "female")
    NS <- ns(t, k = c(0.9911, 3.9863), B = c(0, 14.10579))
    X <- cbind(1, NS, sex, NS * sex)
    Z <- cbind(1, NS)
    eta <- c(X %*% betas[[1]]) + rowSums(Z * bi)
    cbind(eta)
}

ppcheck(jointFit, process = "event", Fforms_fun = FF)

# }