Estimate uncertainty in an interpolation using leave-one-out cross validation.

Calculates and returns the mean squared errors (MSEs) in the units and transformation space (e.g., log space) of the left-hand side of the model fit equation.

Usage

# S3 method for class 'loadInterp'
estimateMSE(
  load.model,
  n.out,
  n.iter = floor(nrow(getFittingData(load.model))/n.out),
  replace,
  ...
)

Arguments

load.model

A load model object, typically inheriting from loadModel and always implementing the loadModelInterface.

n.out

numeric. The number of observations in the fitting data to leave out in each iteration.

n.iter

numeric. The number of iterations to perform.

replace

logical. In each iteration, should the n.out observations that are left out be sampled with replacement of any previous sets of n.out observations (TRUE) or without replacement (FALSE)?

...

Other arguments passed to inheriting methods for estimateMSE

Details

This method is leave-one-out cross validation (LOOCV) when n.out==1, n.iter==nrow(getFittingData(load.model)), and with.replacement=FALSE. This method is k-fold cross validation when n.out*n.iter==nrow(getFittingData(load.model)) and with.replacement==FALSE.

See also

Other estimateMSE: estimateMSE(), estimateMSE.loadComp()