Estimate linear models goodness of fit statistic

Estimate goodness of fit statistic of penalized linear regression models. Works with different goodness of fit statistic functions.

Usage

estimateStat(x, y, u, s, method = "cv", nfold = 10, statistic = rsq, alpha = 0)

Arguments

x

input matrix, of dimension nobs x nvars; each row is an observation vector. Can be in sparse matrix format (inherit from class "sparseMatrix" as in package Matrix)

y

response variable. Quantitative for family="gaussian", or family="poisson" (non-negative counts). For family="binomial" should be either a factor with two levels, or a two-column matrix of counts or proportions (the second column is treated as the target class; for a factor, the last level in alphabetical order is the target class). For family="multinomial", can be a nc>=2 level factor, or a matrix with nc columns of counts or proportions. For either "binomial" or "multinomial", if y is presented as a vector, it will be coerced into a factor. For family="cox", preferably a Surv object from the survival package: see Details section for more information. For family="mgaussian", y is a matrix of quantitative responses.

u

offset vector as in glmnet. "U" experiment in mae.

s

user supplied lambda.

method

currently only cross-validation is implemented.

nfold

number of fold to use in cross-validation.

statistic

function computing goodness of fit statistic. Should accept y, x, offset arguments and return a numeric vector of the same length. See rsq, mse for examples.

alpha

The elasticnet mixing parameter, with \(0\le\alpha\le 1\). The penalty is defined as $$(1-\alpha)/2||\beta||_2^2+\alpha||\beta||_1.$$ alpha=1 is the lasso penalty, and alpha=0 the ridge penalty.

Value

numeric vector of statistic estimates.