r – Is there a simple command to do leave-one-out cross validation with the lm() function?

r – Is there a simple command to do leave-one-out cross validation with the lm() function?

Another solution is using caret

library(caret)

data <- data.frame(x = rnorm(1000, 3, 2), y = 2*x + rnorm(1000))

train(y ~ x, method = lm, data = data, trControl = trainControl(method = LOOCV))

Linear Regression

1000 samples 1 predictor

No pre-processing Resampling: Leave-One-Out Cross-Validation Summary
of sample sizes: 999, 999, 999, 999, 999, 999, … Resampling
results:

RMSE Rsquared MAE
1.050268 0.940619 0.836808

Tuning parameter intercept was held constant at a value of TRUE

You can just use a custom function using a statistical trick that avoids actually computing all the N models:

loocv=function(fit){
  h=lm.influence(fit)$h
  mean((residuals(fit)/(1-h))^2)
}

This is explained in here: https://gerardnico.com/wiki/lang/r/cross_validation
It only works with linear models
And I guess you might want to add a square root after the mean in the formula.

r – Is there a simple command to do leave-one-out cross validation with the lm() function?

You can try cv.lm from the DAAG package:

cv.lm(data = DAAG::houseprices, form.lm = formula(sale.price ~ area),
              m = 3, dots = FALSE, seed = 29, plotit = c(Observed,Residual),
              main=Small symbols show cross-validation predicted values,
              legend.pos=topleft, printit = TRUE)

Arguments

data        a data frame
form.lm,    a formula or lm call or lm object
m           the number of folds
dots        uses pch=16 for the plotting character
seed        random number generator seed
plotit      This can be one of the text strings Observed, Residual, or a logical value. The logical TRUE is equivalent to Observed, while FALSE is equivalent to  (no plot)
main        main title for graph
legend.pos      position of legend: one of bottomright, bottom, bottomleft, left, topleft, top, topright, right, center.
printit     if TRUE, output is printed to the screen

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