# 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 0.836808Tuning 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
```