logisticRidge: Logistic ridge regression.

In ridge: Ridge Regression with Automatic Selection of the Penalty Parameter

Logistic ridge regression.

Description

Fits a logistic ridge regression model. Optionally, the ridge regression parameter is chosen automatically using the method proposed by Cule et al (2012).

Usage

logisticRidge(formula, data, lambda = "automatic", nPCs = NULL,
scaling = c("corrForm", "scale", "none"), ...)

## S3 method for class 'ridgeLogistic'
coef(object, all.coef = FALSE, ...)

## S3 method for class 'ridgeLogistic'
plot(x, y = NULL, ...)

## S3 method for class 'ridgeLogistic'
predict(object, newdata = NULL, type = c("link", "response"), 
    na.action = na.pass, all.coef = FALSE, ...)

## S3 method for class 'ridgeLogistic'
print(x, all.coef = FALSE, ...)

## S3 method for class 'ridgeLogistic'
summary(object, all.coef = FALSE, ...)

## S3 method for class 'summary.ridgeLogistic'
print(x, digits = max(3, getOption("digits") - 3),
signif.stars = getOption("show.signif.stars"), ...) 

Arguments

formula	a formula expression as for regression models, of the form response ~ predictors. See the documentation of formula for other details.
data	an optional data frame in which to interpret the variables occuring in formula.
lambda	A ridge regression parameter. If lambda is "automatic" (the default), then the ridge parameter is chosen automatically using the method of Cule et al (2012).
nPCs	The number of principal components to use to choose the ridge regression parameter, following the method of Cule et al (2012). It is not possible to specify both lambda and nPCs.
scaling	The method to be used to scale the predictors. One of "corrform"(the default) scales the predictors to correlation form, such that the correlation matrix has unit diagonal. "scale"Standardizes the predictors to have mean zero and unit variance. "none"No scaling.
object	A ridgeLogistic object, typically generated by a call to linearRidge.
newdata	An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
type	the type of prediction required. The default predictions are of log-odds (probabilities on logit scale) and type = "response" gives the predicted probabilities.
na.action	function determining what should be done with missing values in newdata. The default is to predict NA.
all.coef	Logical. Should results be returned for all ridge regression penalty parameters (all.coef = TRUE), or only for the ridge parameter chosen automatically using the method of Cule et al?
x	An object of class ridgeLogistic (for the print.ridgeLogistic and plot.ridgeLogistic functions) or an object of class summary.ridgeLogistic (for the print.summary.ridgeLogistic function)
y	Dummy argument for compatibility with the default plot method. Ignored.
digits	minimum number of significant digits to be used for most numbers
signif.stars	logical; if TRUE, P-values are additionally encoded visually as significance stars in order to help scanning of long coefficient tables. It defaults to the show.signif.stars slot of options.
...	Additional arguments to be passed to or from other methods.

Details

If an intercept is present in the model, its coefficient is not penalised. If you want to penalise an intercept, put in your own constant term and remove the intercept.

Value

An object of class "ridgeLogistic", with components:

automatic	Was lambda chosen automatically?
call	The matched call.
coef	A named vector of fitted coefficients.
df	A vector of degrees of freedom of the model fit and degrees of freedom for variance.
Inter	Was in antercept included?
isScaled	Were the predictors scaled before the model was fitted?
lambda	The ridge regression parameter.
scales	The scales used to standardize the predictors.
terms	The terms object used.
x	The scaled predictor matrix.
xm	A vector of means of the predictors.
y	The response.

And optionally the component

nPCs	The number of principal components used to compute the ridge regression parameter.

Author(s)

Erika Cule

References

A semi-automatic method to guide the choice of ridge parameter in ridge regression. Cule, E. and De Iorio, M. (2012) arXiv:1205.0686v1 [stat.AP]

See Also

linearRidge

Examples

data(GenBin)
mod <- logisticRidge(Phenotypes ~ ., data = as.data.frame(GenBin))
summary(mod)

ridge documentation built on April 11, 2022, 5:05 p.m.