Estimate a shrinkage factor for shrinkageafterestimation techniques, with application to linear regression models.
1  ols.shrink(b, dat, sdm)

b 
1 x 
dat 
a 
sdm 
the shrinkage design matrix. This determines the regression coefficients that will be involved in the shrinkage process. 
This is an accessory function that works together with bootval
, splitval
,
kcrossval
and loocval
to estimate a shrinkage factor. For further details,
see References. This function should not be used directly, and instead should
be called via one of the aforementioned shrinkageafterestimation functions.
the function returns a shrinkage factor.
Currently, this function can only derive a single shrinkage factor for a given model, and is unable to estimate (weighted) predictorspecific shrinkage factors.
Harrell, F. E. "Regression modeling strategies: with applications to linear models, logistic regression, and survival analysis." Springer, (2001).
Steyerberg, E. W. "Clinical Prediction Models", Springer (2009)
1 2 3 4 5 6 7 8 9  ## Shrinkage design matrix examples for a model with an
## intercept and 4 predictors:
## 1. Uniform shrinkage (default design within apricomp).
sdm1 < matrix(c(0, rep(1, 4)), nrow = 1)
print(sdm1)
## 2. Nonuniform shrinkage; 1 shrinkage factor applied only to the
## first two predictors
sdm2 < matrix(c(0, 1, 1, 0, 0), nrow = 1)
print(sdm2)

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