MSE_loc: Simple MSE function
In CorReg: Linear Regression Based on Linear Structure Between Variables

Description Usage Arguments Value Examples

This function computes the MSE (Mean Squared Error) of prediction associated to a vector of coefficients A used to predict a response variable Y by linear regression on X, with an intercept or not.

1	MSE_loc(Y = Y, X = X, A = A, intercept = TRUE)

`Y`	the response variable (vector)
`X`	the dataset (matrix of covariates)
`A`	the vector of coefficients
`intercept`	(boolean) to add a column of 1 to `X` if `A` contains an intercept and `X` doesn't.

the Mean Squared Error observed on X when using A coefficients to predict Y.

# dataset generation
base = mixture_generator(n = 15, p = 5, valid = 100, scale = TRUE)
X_appr = base$X_appr # learning sample
Y_appr = base$Y_appr # response variable
X_test = base$X_test # validation sample
Y_test = base$Y_test # response variable (validation sample)
A = lm(Y_appr ~ X_appr)$coefficients
MSE_loc(Y = Y_appr, X = X_appr, A = A) # MSE on the learning dataset
MSE_loc(Y = Y_test, X = X_test, A = A) # MSE on the validation sample