R2oosse: Estimate out-of-sample R² and its standard error

In oosse: Out-of-Sample R² with Standard Error Estimation

Estimate out-of-sample R² and its standard error

Description

Estimate out-of-sample R² and its standard error

Usage

R2oosse(
  y,
  x,
  fitFun,
  predFun,
  methodMSE = c("CV", "bootstrap"),
  methodCor = c("nonparametric", "jackknife"),
  printTimeEstimate = TRUE,
  nFolds = 10L,
  nInnerFolds = nFolds - 1L,
  cvReps = 200L,
  nBootstraps = 200L,
  nBootstrapsCor = 50L,
  ...
)

Arguments

y	The vector of outcome values
x	The matrix of predictors
fitFun	The function for fitting the prediction model
predFun	The function for evaluating the prediction model
methodMSE	The method to estimate the MSE, either "CV" for cross-validation or "bootstrap" for .632 bootstrap
methodCor	The method to estimate the correlation between MSE and MST estimators, either "nonparametric" or "jackknife"
printTimeEstimate	A boolean, should an estimate of the running time be printed?
nFolds	The number of outer folds for cross-validation
nInnerFolds	The number of inner cross-validation folds
cvReps	The number of repeats for the cross-validation
nBootstraps	The number of .632 bootstraps
nBootstrapsCor	The number of bootstraps to estimate the correlation
...	passed onto fitFun and predFun

Details

Implements the calculation of the R² and its standard error by \insertCiteHawinkel2023oosse. Multithreading is used as provided by the BiocParallel or doParallel packages, A rough estimate of expected computation time is printed when printTimeEstimate is true, but this is purely indicative. The options to estimate the mean squared error (MSE) are cross-validation \insertCiteBates2023oosse or the .632 bootstrap \insertCiteEfron1997oosse.

Value

A list with components

R2	Estimate of the R² with standard error
MSE	Estimate of the MSE with standard error
MST	Estimate of the MST with standard error
corMSEMST	Estimated correlation between MSE and MST estimators
params	List of parameters used
fullModel	The model trained on the entire dataset using fitFun
n	The sample size of the training data

References

\insertAllCited

See Also

buildConfInt

Examples

data(Brassica)
#Linear model
fitFunLM = function(y, x){lm.fit(y = y, x = cbind(1, x))}
predFunLM = function(mod, x) {cbind(1,x) %*% mod$coef}
y = Brassica$Pheno$Leaf_8_width
R2lm = R2oosse(y = Brassica$Pheno$Leaf_8_width, x = Brassica$Expr[, 1:10],
fitFun = fitFunLM, predFun = predFunLM, nFolds = 10)

oosse documentation built on May 29, 2024, 10:35 a.m.