Bootstrap-derived Shrinkage After Estimation

Description

Shrink regression coefficients using a bootstrap-derived shrinkage factor.

Usage

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bootval(dataset, model, N, sdm, int = TRUE, int.adj)

Arguments

dataset

a dataset for regression analysis. Data should be in the form of a matrix, with the outcome variable as the final column. Application of the datashape function beforehand is recommended, especially if categorical predictors are present. For regression with an intercept included a column vector of 1s should be included before the dataset (see examples).

model

type of regression model. Either "linear" or "logistic".

N

the number of times to replicate the bootstrapping process

sdm

a shrinkage design matrix. For examples, see ols.shrink

int

logical. If TRUE the model will include a regression intercept.

int.adj

logical. If TRUE the regression intercept will be re-estimated after shrinkage of the regression coefficients.

Details

This function applies bootstrapping to a dataset in order to derive a shrinkage factor and apply it to the regression coefficients. Regression coefficients are estimated in a bootstrap sample, and then a shrinkage factor is estimated using the input data. The mean of N shrinkage factors is then applied to the original regression coeffients, and the regression intercept may be re-estimated.

This process can currently be applied to linear or logistic regression models.

Value

bootval returns a list containing the following:

raw.coeff

the raw regression model coefficients, pre-shrinkage.

shrunk.coeff

the shrunken regression model coefficients

lambda

the mean shrinkage factor over N bootstrap replicates

N

the number of bootstrap replicates

sdm

the shrinkage design matrix used to apply the shrinkage factor(s) to the regression coefficients

Examples

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## Example 1: Linear regression using the iris dataset
data(iris)
iris.data <- as.matrix(iris[, 1:4])
iris.data <- cbind(1, iris.data)
sdm1 <- matrix(c(0, 1, 1, 1), nrow = 1)
set.seed(777)
bootval(dataset = iris.data, model = "linear", N = 200, sdm = sdm1,
int = TRUE, int.adj = TRUE)

## Example 2: logistic regression using a subset of the mtcars data
data(mtcars)
mtc.data <- cbind(1,datashape(mtcars, y = 8, x = c(1, 6, 9)))
head(mtc.data)
set.seed(777)
bootval(dataset = mtc.data, model = "logistic", N = 500)