strip: Reduce the memory footprint of an object for serialization

In latrend: A Framework for Clustering Longitudinal Data

Reduce the memory footprint of an object for serialization

Description

Reduce the (serialized) memory footprint of an object.

Usage

strip(object, ...)

## S4 method for signature 'lcMethod'
strip(object, ..., classes = "formula")

## S4 method for signature 'ANY'
strip(object, ..., classes = "formula")

## S4 method for signature 'lcModel'
strip(object, ..., classes = "formula")

Arguments

object	The model.
...	Not used.
classes	The object classes for which to remove their assigned environment. By default, only environments from formula are removed.

Details

Serializing references to environments results in the serialization of the object together with any associated environments and references. This method removes those environments and references, greatly reducing the serialized object size.

Value

The stripped (i.e., updated) object.

Implementation

Classes extending lcModel can override this method to remove additional non-essentials.

setMethod("strip", "lcModelExt", function(object, ..., classes = "formula") {
  object <- callNextMethod()
  # further process the object
  return(object)
})

See Also

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

Examples

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

latrend documentation built on May 29, 2024, 8:51 a.m.