Description Usage Arguments Value
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 = T)
|
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 |
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
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