cv.poisson | R Documentation |
The function does k-fold cross validation for selecting best value of regularization parameter.
cv.poisson(x,y,k=5,nlambda=50,tau=1,plot=TRUE,errorbars=TRUE)
x |
x is matrix of order n x p where n is number of observations and p is number of predictor variables. Rows should represent observations and columns should represent predictor variables. |
y |
y is a vector of response variable of order n x 1. |
k |
Number of folds for cross validation. Default is k=5. |
nlambda |
Number of lambda values to be used for cross validation. Default is nlambda=50. |
tau |
Elastic net parameter, 0 ≤ τ ≤ 1 in elastic net penalty λ\{τ\|β\|_1+(1-τ)\|beta\|_2^2\}. Default tau=1 corresponds to LASSO penalty. |
plot |
if TRUE, produces a plot of cross validated prediction mean squared errors against lambda. Default is TRUE. |
errorbars |
If TRUE, error bars are drawn in the plot. Default is TRUE. |
Produces a plot and returns a list with following components:
lambda |
Value of lambda for which average cross validation error is minimum |
pmse |
A vector of average cross validation errors for various lambda values |
lambdas |
A vector of lambda values used in cross validation |
se |
A vector containing standard errors of cross validation errors |
This function need not be called by user. The function is internally called by cv.extlasso function.
B N Mandal and Jun Ma
Mandal, B.N. and Jun Ma, (2014). A Jacobi-Armijo Algorithm for LASSO and its Extensions.
x=matrix(rnorm(100*30),100,30) y=sample(c(1:5),100,replace=TRUE) cv.poisson(x,y,k=5)
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