# 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 |

### 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