Marginal predictions (possibly class-specific) of a hlme, lcmm, multlcmm or Jointlcmm object in the natural scale of the longitudinal outcome(s) for a specified profile of covariates.
For hlme and Jointlcmm objects, the function computes the
predicted values of the longitudinal marker in each latent class 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 class 'Jointlcmm' predictY(x, newdata, var.time, methInteg = 0, nsim = 20, draws = FALSE, ndraws = 2000, na.action = 1, ...) ## S3 method for class 'hlme' predictY(x, newdata, var.time, draws = FALSE, na.action = 1, ...) ## S3 method for class '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 class 'multlcmm' predictY(x, newdata, var.time, methInteg = 0, nsim = 20, draws = FALSE, ndraws = 2000, na.action = 1, ...)
x |
an object inheriting from class |
newdata |
data frame containing the data from which predictions are
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 |
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 |
For a |
draws |
optional boolean specifying whether median and confidence bands
of the predicted values should be computed (TRUE) - whatever the type of
link function. For a |
ndraws |
For a |
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. |
... |
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. |
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.
- times : the var.time variable from newdata
Cecile Proust-Lima, Viviane Philipps
#### Prediction from a 2-class model with a Splines link function
## Not run:
## fitted model
m<-lcmm(Ydep2~Time*X1,mixture=~Time,random=~Time,classmb=~X2+X3,
subject='ID',ng=2,data=data_lcmm,link="splines",B=c(
-0.175, -0.191, 0.654, -0.443,
-0.345, -1.780, 0.913, 0.016,
0.389, 0.028, 0.083, -7.349,
0.722, 0.770, 1.376, 1.653,
1.640, 1.285))
summary(m)
## predictions for times from 0 to 5 for X1=0
newdata<-data.frame(Time=seq(0,5,length=100),
X1=rep(0,100),X2=rep(0,100),X3=rep(0,100))
pred0 <- predictY(m,newdata,var.time="Time")
head(pred0)
## Option draws=TRUE to compute a MonteCarlo
# approximation of the predicted value distribution
# (quite long with ndraws=2000 by default)
\dontrun{
pred0MC <- predictY(m,newdata,draws=TRUE,var.time="Time")
}
## predictions for times from 0 to 5 for X1=1
newdata$X1 <- 1
pred1 <- predictY(m,newdata,var.time="Time")
## Option draws=TRUE to compute a MonteCarlo
# approximation of the predicted value distribution
# (quite long with ndraws=2000 by default)
\dontrun{
pred1MC <- predictY(m,newdata,draws=TRUE,var.time="Time")
}
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.