Shrink regression coefficients using a splitsamplederived shrinkage factor.
1 
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". 
nrounds 
the number of times to replicate the sample splitting process. 
fract 
the fraction of observations designated to the training set 
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 reestimated after shrinkage of the regression coefficients. 
This function applies samplesplitting to a dataset in order to derive a shrinkage factor and apply it to the regression coefficients. Data are randomly split into two sets, a training set and a test set. Regression coefficients are estimated using the training sample, and then a shrinkage factor is estimated using the test set. The mean of N shrinkage factors is then applied to the original regression coeffients, and the regression intercept may be reestimated.
This process can currently be applied to linear or logistic regression models.
splitval
returns a list containing the following:
raw.coeff 
the raw regression model coefficients, preshrinkage. 
shrunk.coeff 
the shrunken regression model coefficients 
lambda 
the mean shrinkage factor over Nrounds splitsample replicates 
Nrounds 
the number of rounds of sample splitting 
sdm 
the shrinkage design matrix used to apply the shrinkage factor(s) to the regression coefficients 
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  ## Example 1: Linear regression using the iris dataset
## Splitsamplederived shrinkage with 100 rounds of samplesplitting
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(321)
splitval(dataset = iris.data, model = "linear", nrounds = 100,
fract = 0.75, sdm = sdm1, int = TRUE, int.adj = TRUE)
## Example 2: logistic regression using a subset of the mtcars data
## Splitsamplederived shrinkage
data(mtcars)
mtc.data < cbind(1,datashape(mtcars, y = 8, x = c(1, 6, 9)))
head(mtc.data)
set.seed(123)
splitval(dataset = mtc.data, model = "logistic",
nrounds = 100, fract = 0.5)

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