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.

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