latrend (version 1.6.2)

predictForCluster: Predict trajectories conditional on cluster membership

Description

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

For lcModel objects, 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

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

Value

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

Arguments

object

The model.

newdata

A data.frame of trajectory data for which to compute trajectory assignments.

cluster

The cluster name (as character) to predict for.

...

Arguments passed on to predict.lcModel

useCluster

Whether to use the "Cluster" column in the newdata argument for computing predictions conditional on the respective cluster. For useCluster = NA (the default), the feature is enabled if newdata contains the "Cluster" column.

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'.

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(lcModel) 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 lcModel functions: clusterNames(), clusterProportions(), clusterSizes(), clusterTrajectories(), coef.lcModel(), converged(), deviance.lcModel(), df.residual.lcModel(), estimationTime(), externalMetric(), fitted.lcModel(), fittedTrajectories(), getCall.lcModel(), getLcMethod(), ids(), lcModel-class, metric(), model.frame.lcModel(), nClusters(), nIds(), nobs.lcModel(), plot-lcModel-method, plotClusterTrajectories(), plotFittedTrajectories(), postprob(), predict.lcModel(), predictAssignments(), predictPostprob(), qqPlot(), residuals.lcModel(), sigma.lcModel(), strip(), time.lcModel(), trajectoryAssignments()

Examples

data(latrendData)
method <- lcMethodLMKM(Y ~ Time, id = "Id", time = "Time")
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")