msefun.normal: Prediction means squared errors for hold out data in cross...
In extlasso: Maximum Penalized Likelihood Estimation with Extended Lasso Penalty
msefun.normal R Documentation
Prediction means squared errors for hold out data in cross validation
Description
The function computes Prediction means squared errors for hold out data of ith fold
Usage
msefun.normal(lambda1,f1,xi,yi)
Arguments
lambda1
value of lambda
f1
A fitted ‘extlasso’ object
xi
Hold out data of predictor variables
yi
Hold out data of response variables
Details
This function is internal and used by cross validation routines.
Value
A value of prediction mean squared error
Author(s)
B N Mandal and Jun Ma
extlasso documentation built on May 13, 2022, 9:08 a.m.
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| msefun.normal | R Documentation |
Prediction means squared errors for hold out data in cross validation
Description
The function computes Prediction means squared errors for hold out data of ith fold
Usage
msefun.normal(lambda1,f1,xi,yi)
Arguments
lambda1 |
value of lambda |
f1 |
A fitted ‘extlasso’ object |
xi |
Hold out data of predictor variables |
yi |
Hold out data of response variables |
Details
This function is internal and used by cross validation routines.
Value
A value of prediction mean squared error
Author(s)
B N Mandal and Jun Ma
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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