Bootstrap-derived Shrinkage After Estimation
Shrink regression coefficients using a bootstrap-derived shrinkage factor.
bootval(dataset, model, N, sdm, int = TRUE, int.adj)
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 |
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 |
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. |
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.
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 |
## 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)
Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.