Simple MSE function

Description

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.

Usage

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

Arguments

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.

Value

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

@examples require(CorReg) #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