Fits a linear ridge regression model. Optionally, the ridge regression parameter is chosen automatically using the method proposed by Cule et al (2012).
linearRidge(formula, data, lambda = "automatic", nPCs = NULL,
scaling = c("corrForm", "scale", "none"), ...)# S3 method for ridgeLinear
coef(object, all.coef = FALSE, ...)
# S3 method for ridgeLinear
plot(x, y = NULL, ...)
# S3 method for ridgeLinear
predict(object, newdata, na.action = na.pass, all.coef = FALSE, ...)
# S3 method for ridgeLinear
print(x, all.coef = FALSE, ...)
# S3 method for ridgeLinear
summary(object, all.coef = FALSE, ...)
# S3 method for summary.ridgeLinear
print(x, digits = max(3,
getOption("digits") - 3),
signif.stars = getOption("show.signif.stars"), ...)
a formula expression as for regression models, of the form response ~ predictors. See the
documentation of formula for other details.
an optional data frame in which to interpret the variables occuring in formula.
A ridge regression parameter. May be a vector. If lambda is "automatic" (the default), then the ridge parameter
is chosen automatically using the method of Cule et al (2012).
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.
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.
A ridgeLinear object, typically generated by a call to linearRidge.
An optional data frame in which to look for variables with which to predict. If omitted, the fitted values are used.
function determining what should be done with missing values
in newdata. The default is to predict NA.
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?
An object of class ridgeLinear (for the
print.ridgeLinear and plot.ridgeLinear functions) or an object of class
summary.ridgeLinear (for the print.summary.ridgeLinear
function)
Dummy argument for compatibility with the default plot
method. Ignored.
minimum number of significant digits to be used for most numbers
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.
An object of class "ridgeLinear", with components:
Logical. Was lambda chosen automatically?
The matched call.
A named vector of fitted coefficients.
A vector of degrees of freedom of the model fit, degrees of freedom for variance, and residual degrees of freedom of the fitted model.
Was an intercept included?
Were the predictors scaled before the model was fitted?
The ridge regression parameter(s).
The scales used to standardize the predictors.
The terms object used.
The scaled predictor matrix.
A vector of means of the predictors.
The response.
The mean of the response.
And optionally the components
The maximum number of principal components for which a ridge regression parameter was computed.
The number of principal components used to compute the ridge parameter.
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.
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]
# NOT RUN {
data(GenCont)
mod <- linearRidge(Phenotypes ~ ., data = as.data.frame(GenCont))
summary(mod)
# }
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