predict.lcModel: lcModel predictions

Description

Predicts the expected trajectory observations at the given time for each cluster.

Usage

# S3 method for lcModel
predict(object, newdata = NULL, what = "mu", ..., useCluster = NA)

Value

If newdata specifies the cluster membership; a data.frame of cluster-specific predictions. Otherwise, a list of data.frame of cluster-specific predictions is returned.

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.
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'.
...: Additional arguments.
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.

Implementation

Note: Subclasses of lcModel should preferably implement predictForCluster() instead of overriding predict.lcModel as that function is designed to be easier to implement because it is single-purpose.

The predict.lcModelExt function should be able to handle the case where newdata = NULL by returning the fitted values. After post-processing the non-NULL newdata input, the observation- and cluster-specific predictions can be computed. Lastly, the output logic is handled by the transformPredict() function. It converts the computed predictions (e.g., matrix or data.frame) to the appropriate output format.


predict.lcModelExt <- function(object, newdata = NULL, what = "mu", ...) {
  if (is.null(newdata)) {
    newdata = model.data(object)
    if (hasName(newdata, 'Cluster')) {
      # allowing the Cluster column to remain would break the fitted() output.
      newdata[['Cluster']] = NULL
    }
  }
  # compute cluster-specific predictions for the given newdata
  pred <- NEWDATA_COMPUTATIONS_HERE
  transformPredict(pred = pred, model = object, newdata = newdata)
})

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(), predictAssignments(), predictForCluster(), predictPostprob(), qqPlot(), residuals.lcModel(), sigma.lcModel(), strip(), time.lcModel(), trajectoryAssignments()

Examples

Run this code

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

predFitted <- predict(model) # same result as fitted(model)

# Cluster trajectory of cluster A
predCluster <- predict(model, newdata = data.frame(Cluster = "A", Time = time(model)))

# Prediction for id S1 given cluster A membership
predId <- predict(model, newdata = data.frame(Cluster = "A", Id = "S1", Time = time(model)))

# Prediction matrix for id S1 for all clusters
predIdAll <- predict(model, newdata = data.frame(Id = "S1", Time = time(model)))