hlme,
lcmm, multlcmm or Jointlcmm object in the natural scale
of the longitudinal outcome(s) computed from a profile of covariates (marginal) or
individual data (subject specific in case of hlme).For hlme and Jointlcmm objects, the function computes the
predicted values of the longitudinal marker (in each latent class of ng>1) for a
specified profile of covariates. For lcmm and multlcmm
objects, the function computes predicted values in the natural scale of the
outcomes for a specified profile of covariates. For linear and threshold
links, the predicted values are computed analytically. For splines and Beta
links, a Gauss-Hermite or Monte-Carlo integration are 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.
# S3 method for Jointlcmm
predictY(
x,
newdata,
var.time,
methInteg = 0,
nsim = 20,
draws = FALSE,
ndraws = 2000,
na.action = 1,
...
)# S3 method for hlme
predictY(
x,
newdata,
var.time,
draws = FALSE,
na.action = 1,
marg = TRUE,
subject = NULL,
...
)
# S3 method for lcmm
predictY(
x,
newdata,
var.time,
methInteg = 0,
nsim = 20,
draws = FALSE,
ndraws = 2000,
na.action = 1,
...
)
predictY(x, newdata, var.time, ...)
# S3 method for multlcmm
predictY(
x,
newdata,
var.time,
methInteg = 0,
nsim = 20,
draws = FALSE,
ndraws = 2000,
na.action = 1,
...
)
An object of class predictY with values :
- pred : a matrix with the same rows (number and order) as in
newdata.
For hlme objects and lcmm or Jointlcmm with
draws=FALSE, returns a matrix with ng columns corresponding to the ng
class-specific vectors of predicted values computed at the point estimate
For objects of class lcmm or Jointlcmm with draws=TRUE,
returns a matrix with ng*3 columns representing the ng class-specific 50%,
2.5% and 97.5% percentiles of the approximated posterior distribution of
the class-specific predicted values.
For objects of class multlcmm with draws=FALSE, returns a
matrix with ng+1 columns: the first column indicates the name of the outcome
which is predicted and the ng subsequent columns correspond to the ng
class-specific vectors of predicted values computed at the point estimate
For objects of class multlcmm with draws=TRUE, returns a
matrix with ng*3+1 columns: the first column indicates the name of the
outcome which is predicted and the ng*3 subsequent columns correspond to the
ng class-specific 50%, 2.5% and 97.5% percentiles of the approximated
posterior distribution of the class-specific predicted values.
For objects of class hlme with marg=FALSE, returns a matrix
with 2+ng columns : the grouping structure, subject-specific predictions (pred_ss) averaged
over classes and the class-specific subject-specific predictions (with the
number of the latent class: pred_ss_1,pred_ss_2,...)
- times : the var.time variable from newdata
an object inheriting from class lcmm, hlme,
Jointlcmm or multlcmm representing a general latent class
mixed model.
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 lcmm, hlme,
Jointlcmm or multlcmm calls. For hlme object and marg=FALSE,
the grouping structure and values for the outcome should also be specified.
A character string containing the name of the variable that corresponds to time in the data frame (x axis in the plot).
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.
For a lcmm, multlcmm or Jointlcmm object
only; optional number of points used in the numerical integration with
splines or Beta link functions. 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).
optional boolean specifying whether median and confidence bands
of the predicted values should be computed (TRUE) - whatever the type of
link function. For a lcmm, multlcmm or Jointlcmm
object, 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.
For a lcmm, multlcmm or Jointlcmm object
only; 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.
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.
further arguments to be passed to or from other methods. Only the argument 'median' will be used, other are ignored. 'median' should be a logical indicating whether the median should be computed. By default, the mean value is computed.
Optional boolean specifying whether the
predictions are marginal (the default) or subject-specific (marg=FALSE). marge=FALSE
only works with hlme objects.
For a hlme object with marg=FALSE only, character specifying
the name of the grouping strucuture. If NULL (the default), the same as in the model
(argument x) will be used.
Cecile Proust-Lima, Viviane Philipps, Sasha Cuau
lcmm, multlcmm, hlme,
Jointlcmm