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 |
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
.
1 2 3 4 5 6 7 8 9 | # 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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