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

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)
#> Error in plot(preed1): object 'preed1' not found