latrend (version 1.2.1)

predictForCluster: lcModel prediction conditional on a cluster

Description

Predicts the expected trajectory observations at the given time under the assumption that the trajectory belongs to the specified cluster.

The same result can be obtained by calling predict() with the newdata data.frame having a "Cluster" assignment column. The main purpose of this function is to make it easier to implement the prediction computations for custom lcModel classes.

Usage

# S4 method for lcModel
predictForCluster(object, newdata = NULL, cluster, ..., what = "mu")

Arguments

object

The lcModel object.

newdata

Optional data.frame for which to compute the model predictions. If omitted, the model training data is used. Cluster trajectory predictions are made when ids are not specified.

cluster

The cluster name (as character) to predict for.

...

Additional arguments.

what

The distributional parameter to predict. By default, the mean response 'mu' is predicted. The cluster membership predictions can be obtained by specifying what = 'mb'.

Value

A vector with the predictions per newdata observation, or a data.frame with the predictions and newdata alongside.

Implementation

Classes extending lcModel should override this method, unless predict.lcModel() is preferred.

setMethod("predictForCluster", "lcModelExt",
 function(object, newdata = NULL, cluster, ..., what = "mu") {
  # return model predictions for the given data under the
  # assumption of the data belonging to the given cluster
})

Details

The default predictForCluster() method makes use of predict.lcModel(), and vice versa. For this to work, any extending lcModel classes, e.g., lcModelExample, should implement either predictForCluster(lcModelExample) or predict.lcModelExample(). When implementing new models, it is advisable to implement predictForCluster as the cluster-specific computation generally results in shorter and simpler code.

See Also

predict.lcModel

Other model-specific methods: clusterTrajectories(), coef.lcModel(), converged(), deviance.lcModel(), df.residual.lcModel(), fitted.lcModel(), fittedTrajectories(), lcModel-class, logLik.lcModel(), model.frame.lcModel(), nobs.lcModel(), postprob(), predict.lcModel(), predictAssignments(), predictPostprob(), residuals.lcModel(), sigma.lcModel(), time.lcModel()

Examples

# NOT RUN {
data(latrendData)
method <- lcMethodKML("Y", id = "Id", time = "Time", nClusters = 3)
model <- latrend(method, latrendData)

predictForCluster(model,
  newdata = data.frame(Time = c(0, 1)),
  cluster = "B")

# all fitted values under cluster B
predictForCluster(model, cluster = "B")
# }