glmnet_measure: Extract Measures of Model Fit from GLMNET Fit

In jmleach-bst/ssnet: Fit Spike-and-Slab Elastic Net GLM With Spatial Priors on Parameters

Extract Measures of Model Fit from GLMNET Fit

Description

Extracts measures of model fitness from a glmnet object.

Usage

glmnet_measure(
  cv.model,
  x = NULL,
  y = NULL,
  lambda.criteria = "lambda.min",
  type.measure = c("default", "mse", "deviance", "class", "auc", "mae", "C"),
  family = "gaussian",
  alpha = 1
)

Arguments

cv.model	A model fit by glmnet function cv.glmnet().
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). Requirement: nvars >1; in other words, x should have 2 or more columns.
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.
lambda.criteria	Determines the model selection criteria. When "lambda.min" the final model is selected based on the penalty that minimizes the measure given in type.measure. When "lambda.1se" the final model is selected based on the smallest value of lambda that is within one standard error of the minimal measure given in type.measure.
type.measure	loss to use for cross-validation. Currently five options, not all available for all models. The default is type.measure="deviance", which uses squared-error for gaussian models (a.k.a type.measure="mse" there), deviance for logistic and poisson regression, and partial-likelihood for the Cox model. type.measure="class" applies to binomial and multinomial logistic regression only, and gives misclassification error. type.measure="auc" is for two-class logistic regression only, and gives area under the ROC curve. type.measure="mse" or type.measure="mae" (mean absolute error) can be used by all models except the "cox"; they measure the deviation from the fitted mean to the response. type.measure="C" is Harrel's concordance measure, only available for cox models.
family	Either a character string representing one of the built-in families, or else a glm() family object. For more information, see Details section below or the documentation for response type (above).
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

A data frame containing measures of model fitness.

Note

This function is primarily used within compare_ssnet, but perhaps could be useful elsewhere.

Examples

## generate data (no intercept)
set.seed(4799623)
cn <- c()
for (i in 1:100) cn[i] <- paste0("x", i)
tb <- rbinom(100, 1, 0.05)
tx <- matrix(rnorm(10000), nrow = 100, ncol = 100,
             dimnames = list(1:100, cn))
ty <- tx %*% tb + rnorm(100)

## fit model and export model fit stats
cv1 <- cv.glmnet(x = tx, y = ty, family = "gaussian", alpha = 1)
glmnet_measure(cv1, x = tx, y = ty)

jmleach-bst/ssnet documentation built on March 4, 2024, 5:04 p.m.