R/zzz.R

In shrink: Global, Parameterwise and Joint Shrinkage Factor Estimation

#' Global, Parameterwise and Joint Shrinkage Factor Estimation
#'
#' The predictive value of a statistical model can often be improved by applying
#' shrinkage methods. This can be achieved, e.g., by regularized regression or
#' empirical Bayes approaches. Various types of shrinkage factors can also be
#' estimated after a maximum likelihood. While global shrinkage modifies all
#' regression coefficients by the same factor, parameterwise shrinkage factors
#' differ between regression coefficients. With variables which are either highly
#' correlated or associated with regard to contents, such as several columns of
#' a design matrix describing a nonlinear effect or two main effects and their
#' pairwise interaction term, parameterwise shrinkage factors are not interpretable
#' and a compromise between global and parameterwise shrinkage, termed 'joint
#' shrinkage', is a useful extension.
#' A computational shortcut to resampling-based shrinkage factor estimation based
#' on DFBETA residuals can be applied.
#' Global, parameterwise and joint shrinkage for models fitted by \code{lm},
#' \code{glm}, \code{coxph}, and \code{mfp} is available.
#'
#' Functions included in the \code{shrink}-package:
#' \tabular{ll}{
#'      \code{ shrink } \tab a function to compute global, parameterwise and joint post-estimation \cr
#'      \code{ } \tab shrinkage factors of fit objects of class \code{lm}, \code{glm}, \code{coxph}, or \code{mfp}. \cr
#'      \code{ } \tab   \cr
#'      \code{ coef.shrink } \tab returns shrunken regression coefficients from objects of class \code{shrink}. \cr
#'      \code{ predict.shrink } \tab obtains predictions from shrunken regression coefficients from objects \cr
#'      \code{ } \tab  of class \code{shrink}. \cr
#'      \code{ vcov.shrink } \tab returns the variance-covariance matrix of shrinkage factors. \cr
#'      \code{ print.shrink } \tab prints objects of class \code{shrink}. \cr
#'      \code{ summary.shrink } \tab summary of objects of class \code{shrink}. \cr
#'  }
#'
#'  Data set included in the \code{shrink}-package:
#'  \tabular{ll}{
#'        \code{ deepvein } \tab deep vein thrombosis study \cr
#'        \code{ GBSG } \tab German breast cancer study \cr
#'  }
#'
#' @section Notes: Sauerbrei (1999) suggested that before estimating parameterwise shrinkage 
#' factors, the data should be standardized to have a mean of 0 and unit variance. 
#' 
#' @references Dunkler D, Sauerbrei W, Heinze G (2016). Global, Parameterwise and Joint
#'     Shrinkage Factor Estimation. \emph{Journal of Statistical Software}. \bold{69}(8), 1-19.
#'     \doi{10.18637/jss.v069.i08} \cr
#'     Sauerbrei W (1999) The use of resampling methods to simplify regression
#'     models in medial statistics. \emph{Applied Statistics} \bold{48}(3): 313-329. \cr
#'     Verweij P, van Houwelingen J (1993) Cross-validation in survival analysis.
#'     \emph{Statistics in Medicine} \bold{12}(24): 2305-2314.
#'     
#' @seealso \code{\link{shrink}}, \code{\link{coef.shrink}}, \code{\link{predict.shrink}},
#'     \code{\link{print.shrink}}, \code{\link{summary.shrink}}, \code{\link{vcov.shrink}},
#'     \code{\link{deepvein}}
#'     
#' @examples
#' # with glm, family = binomial
#' set.seed(888)
#' intercept <- 1
#' beta <- c(0.5, 1.2)
#' n <- 200
#' x1 <- rnorm(n, mean = 1, sd = 1)
#' x2 <- rbinom(n, size = 1, prob = 0.3)
#' linpred <- intercept + x1 * beta[1] + x2 * beta[2]
#' prob <- exp(linpred) / (1 + exp(linpred))
#' runis <- runif(n, min = 0, max = 1)
#' ytest <- ifelse(test = runis < prob, yes = 1, no = 0)
#' simdat <- data.frame(cbind(y = ifelse(runis < prob, 1, 0), x1, x2))
#'
#' fit <- glm(y ~ x1 + x2, family = binomial, data = simdat, x = TRUE)
#' summary(fit)
#'
#' global <- shrink(fit, type = "global", method = "dfbeta")
#' print(global)
#' coef(global)
#'
#' shrink(fit, type = "parameterwise", method = "dfbeta")
#'
#' shrink(fit, type = "parameterwise", method = "dfbeta", join = list(c("x1", "x2")))
#'
#' #shrink(fit, type = "global", method = "jackknife")
#' #shrink(fit, type = "parameterwise", method = "jackknife")
#' #shrink(fit, type = "parameterwise", method = "jackknife",
#' #       join = list(c("x1", "x2")))
#'
#' # For more examples see shrink
#'
#' @docType package
#' @name shrink-package
#'
#' @import survival rms mfp MASS
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