Helper function for custom lcModel classes implementing predict.lcModel()

A helper function for implementing the predict.lcModel() method as part of your own lcModel class, ensuring the correct output type and format (see the Value section). Note that this function has no use outside of ensuring valid output for predict.lcModel. For implementing lcModel predictions from scratch, it is advisable to implement predictForCluster instead of predict.lcModel.

The prediction ordering corresponds to the observation ordering of the newdata argument.

By default, transformPredict() accepts one of the following inputs:

data.frame

A data.frame in long format providing a cluster-specific prediction for each observation per row, with column names "Fit" and "Cluster". This data.frame therefore has nrow(model.data(object)) * nClusters(object) rows.

matrix

An N-by-K matrix where each row provides the cluster-specific predictions for the respective observations in newdata. Here, N = nrow(newdata) and K = nClusters(object).

vector

A vector of length nrow(newdata) with predictions corresponding to the rows of newdata.

Users can implement support for other prediction formats by defining the transformPredict() method with other signatures.

transformPredict(pred, model, newdata)

# S4 method for NULL,lcModel
transformPredict(pred, model, newdata)

# S4 method for vector,lcModel
transformPredict(pred, model, newdata)

# S4 method for matrix,lcModel
transformPredict(pred, model, newdata)

# S4 method for data.frame,lcModel
transformPredict(pred, model, newdata)

Arguments

pred

The (per-cluster) predictions for newdata.

model

The lcModel for which the prediction was made.

newdata

A data.frame containing the input data to predict for.

Value

A data.frame with the predictions, or a list of cluster-specific prediction data.frames.

Example implementation

In case we have a custom lcModel class based on an existing internal model representation with a predict() function, we can use transformPredict() to easily transform the internal model predictions to the right format. A common output is a matrix with the cluster-specific predictions.

predict.lcModelExample <- function(object, newdata) {
  predictionMatrix <- predict(object@model, newdata)
  transformPredict(
    pred = predictionMatrix,
    model = object,
    newdata = newdata
  )
}

However, for ease of implementation it is generally advisable to implement predictForCluster instead of predict.lcModel.

For a complete and runnable example, see the custom models vignette accessible via vignette("custom", package = "latrend").

See also

predictForCluster, predict.lcModel