| glmnet_measure | R Documentation |
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)
