predictYback: Marginal predictions in the natural scale of a pre-trandormed...

In CecileProust-Lima/lcmm: Extended Mixed Models Using Latent Classes and Latent Processes

Marginal predictions in the natural scale of a pre-trandormed outcome

Description

The function computes the predicted values of the longitudinal marker (in each latent class of ng>1) for a specified profile of covariates, when a pre-transformation is applied. A Gauss-Hermite or Monte-Carlo integration is used to numerically compute the back-transformed predictions.

Usage

predictYback(
  x,
  newdata,
  var.time,
  methInteg = 0,
  nsim = 20,
  draws = FALSE,
  ndraws = 2000,
  na.action = 1,
  back,
  ...
)

Arguments

x	an object inheriting from class hlme representing a general latent class mixed model.
newdata	data frame containing the data from which predictions are to be computed. The data frame should include at least all the covariates listed in x$Xnames2. Names in the data frame should be exactly x$Xnames2 that are the names of covariates specified in hlme calls.
var.time	A character string containing the name of the variable that corresponds to time in the data frame (x axis in the plot).
methInteg	optional integer specifying the type of numerical integration required only for predictions with splines or Beta link functions. Value 0 (by default) specifies a Gauss-Hermite integration which is very rapid but neglects the correlation between the predicted values (in presence of random-effects). Value 1 refers to a Monte-Carlo integration which is slower but correctly account for the correlation between the predicted values.
nsim	number of points used in the numerical integration. For methInteg=0, nsim should be chosen among the following values: 5, 7, 9, 15, 20, 30, 40 or 50 (nsim=20 by default). If methInteg=1, nsim should be relatively important (more than 200).
draws	boolean specifying whether confidence bands should be computed. If draws=TRUE, 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	integer. 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.
na.action	Integer indicating how NAs are managed. The default is 1 for 'na.omit'. The alternative is 2 for 'na.fail'. Other options such as 'na.pass' or 'na.exclude' are not implemented in the current version.
back	function to back-transform the outcome in the original scale.

Value

An object of class predictY.

Examples

data_lcmm$transfYdep2 <- sqrt(30 - data_lcmm$Ydep2)
m1 <- hlme(transfYdep2 ~ Time, random= ~ Time, subject = "ID", data = data_lcmm)
pred1 <- predictYback(m1, newdata = data.frame(Time = seq(0, 3, 0.1)), var.time = "Time", back = function(x) {30 - x^2})
plot(preed1)

CecileProust-Lima/lcmm documentation built on March 3, 2024, 5:23 p.m.