Conditional predictions of a lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for specified latent process values.

The function computes the predicted values of the longitudinal markers in their natural scale for specified values of the latent process. For splines and Beta links, a Gauss-Hermite integration is used to numerically compute the predictions. In addition, for any type of link function, confidence bands (and median) can be computed by a Monte Carlo approximation of the posterior distribution of the predicted values.

Usage

predictYcond(
  x,
  lprocess,
  condRE_Y = FALSE,
  nsim = 200,
  draws = FALSE,
  ndraws = 2000,
  ...
)

Arguments

x

an object inheriting from class lcmm, Jointlcmm or multlcmm representing a general latent class mixed model.

lprocess

numeric vector containing the latent process values at which the predictions should be computed.

condRE_Y

for multlcmm objects only, logical indicating if the predictions are conditional to the outcome specific random effects or not. Default to FALSE, the predictions are marginal to these random effects.

nsim

number of points used in the numerical integration (Monte-Carlo) with splines or Beta link functions. nsim should be relatively important (nsim=200 by default).

draws

optional boolean specifying whether median and confidence bands of the predicted values should be computed (TRUE) - whatever the type of link function. A Monte Carlo approximation of the posterior distribution of the predicted values is computed and the median, 2.5% and 97.5% percentiles are given. Otherwise, the predicted values are computed at the point estimate. By default, draws=FALSE.

ndraws

if draws=TRUE, ndraws specifies the number of draws that should be generated to approximate the posterior distribution of the predicted values. By default, ndraws=2000.

...

further arguments to be passed to or from other methods. They are ignored in this function.

Value

An object of class predictYcond with values :

- pred : If draws=FALSE, returns a matrix with 3 columns : the first column indicates the name of the outcome, the second indicates the latent process value and the last is the computed prediction. If draws=TRUE, returns a matrix with 5 columns : the name of the outcome, the latent process value and the 50%, 2.5% and 97.5% percentiles of the approximated posterior distribution of predicted values.

- object : the model from which the predictions are computed.

See also

predictY, predictlink

Author

Cecile Proust-Lima, Viviane Philipps

Examples

# \dontrun{
m12 <- lcmm(Ydep2~Time+I(Time^2),random=~Time,subject='ID',ng=1,
data=data_lcmm,link="3-equi-splines")
predm12 <- predictYcond(m12,lprocess=seq(-8,2,length.out=100),draws=TRUE)
plot(predm12)

# }