Code cleanup for R CMD CHECK

NAMESPACE

@@ -1,16 +1,17 @@
 # Generated by roxygen2: do not edit by hand
 
-export(coef.l2boost)
+S3method(coef,l2boost)
+S3method(fitted,l2boost)
+S3method(l2boost,default)
+S3method(l2boost,formula)
+S3method(plot,l2boost)
+S3method(predict,l2boost)
+S3method(print,l2boost)
+S3method(residuals,l2boost)
 export(cv.l2boost)
 export(elasticNetSim)
-export(fitted.l2boost)
 export(l2boost)
-export(l2boost.default)
-export(l2boost.formula)
 export(mvnorm.l2boost)
-export(plot.l2boost)
-export(predict.l2boost)
-export(print.l2boost)
-export(residuals.l2boost)
 importFrom(MASS,mvrnorm)
+importFrom(parallel,detectCores)
 importFrom(parallel,mclapply)

R/coef.l2boost.R

@@ -1,6 +1,6 @@
-#' @title Extract model coefficients from an l2boost model object at any point along the solution path indexed by step m.
+#' Extract model coefficients from an l2boost model object at any point along the solution path indexed by step m.
 #'  
-#' @details \code{\link{coef}} is a generic function which extracts model coefficients from objects returned by modeling functions. 
+#' \code{\link{coef}} is a generic function which extracts model coefficients from objects returned by modeling functions. 
 #' 
 #' By default, \code{\link{coef.l2boost}} returns the model (beta) coefficients from the last step, 
 #' M of the \code{\link{l2boost}} model. For a \code{\link{cv.l2boost}} object, the default returns the coefficients from 
@@ -32,8 +32,7 @@
 #' # At the m=500 step
 #' coef(object, m=500)
 #'
-#' @export coef.l2boost
-#' 
+#' @export
 coef.l2boost <- function(object, m=NULL, ...){
   if(inherits(object, "cv")){
     if(!is.null(m)) warning ("Cannot select step number for cv.l2boost objects.")

R/cv.l2boost.R

@@ -111,8 +111,10 @@
 #' plot(coef(cv.eBoost$fit, m=cv.obj$opt.step), cex=.5, 
 #'   ylab=expression(beta[i]), xlab="Column Index", ylim=c(0,140), col=sig)
 #' }
+#' 
 #' @export cv.l2boost
-#' @importFrom parallel mclapply
+#' @importFrom parallel mclapply detectCores
+#' 
 cv.l2boost <- function(x, y, K = 10, M = NULL, nu = 1e-4, lambda = NULL, trace = FALSE, 
                        type = c( "discrete", "hybrid", "friedman","lars"), cores=NULL,
                        ...) {

R/fitted.l2boost.R

@@ -1,6 +1,6 @@
-#' @title Extract the fitted model estimates along the solution path for an l2boost model.
+#' Extract the fitted model estimates along the solution path for an l2boost model.
 #' 
-#' @details \code{\link{fitted}} is a generic function which extracts fitted values from objects 
+#' \code{\link{fitted}} is a generic function which extracts fitted values from objects 
 #' returned by modeling functions. 
 #' 
 #' \code{\link{fitted.l2boost}} returns the function estimates obtained 
@@ -54,8 +54,7 @@
 #'   type="l", lty=2, col="red", lwd=2)
 #' abline(h=0, lty=2, col = 'gray')
 #' 
-#' @export fitted.l2boost
-#' 
+#' @export
 fitted.l2boost <- function(object, m=NULL, ...){
   if(inherits(object, "cv")){
     if(is.null(m)){

R/l2boost.R

@@ -7,21 +7,15 @@
 # y is centered --- > mean is returned as part of the object
 # !!! remove x-columns with NA's !!! CAUTION
 #---------------------------------------------------------------------
-#' @export l2boost
-#' @name l2boost
-#' @aliases l2boost l2boost.default l2boost.formula
-#' 
-#' 
-l2boost <- function(x, ...)UseMethod("l2boost")
 #
-#' @title Generic gradient descent boosting method for linear regression.
+#' Generic gradient descent boosting method for linear regression.
 #' 
-#' @description Efficient implementation of Friedman's boosting algorithm  [Friedman (2001)] with L2-loss function and coordinate
+#' Efficient implementation of Friedman's boosting algorithm  [Friedman (2001)] with L2-loss function and coordinate
 #'  direction (design matrix columns) basis functions. This includes the elasticNet data augmentation of Ehrlinger and Ishwaran (2012), 
 #'  which adds an L2-penalization (lambda) similar to the elastic net [Zou and Hastie (2005)].
 #' 
-#' @details
-#'  The \code{\link{l2boost}} function is an efficient implementation of a generic boosting method [Friedman (2001)] for
+#' 
+#' The \code{\link{l2boost}} function is an efficient implementation of a generic boosting method [Friedman (2001)] for
 #' linear regression using an L2-loss function. The basis functions are the column vectors of the design matrix. 
 #' \code{\link{l2boost}} scales the design matrix such that the coordinate columns of the design correspond to the
 #' gradient directions for each covariate. The boosting coefficients are equivalent to the gradient-correlation of each 
@@ -76,7 +70,6 @@ l2boost <- function(x, ...)UseMethod("l2boost")
 #' @references Zou H. and Hastie T (2005) "Regularization and variable selection via the elastic net"  \emph{J. R. Statist. Soc. B}, 67, Part 2, pp. 301-320
 #' @references Efron B., Hastie T., Johnstone I., and Tibshirani R. (2004). "Least Angle Regression" \emph{Ann. Statist.} 32:407-499
 #'
-#' @usage \method{l2boost}{default}(x, y, M, nu, lambda, trace, type, qr.tolerance, eps.tolerance, ...)
 #'
 #' @param x design matrix of dimension n x p
 #' @param y response variable of length n
@@ -184,9 +177,12 @@ l2boost <- function(x, ...)UseMethod("l2boost")
 #'   main="elasticBoost nu=1.e-3, lambda=.1")
 #' }
 #' 
-#' @rdname l2boost
-#' @name l2boost
-#' @export l2boost.default
+#' @aliases l2boost l2boost.default l2boost.formula
+#' 
+#' @export
+l2boost <- function(x, ...)UseMethod("l2boost")
+
+#' @export 
 l2boost.default <- function(x, y,
                             M = NULL, nu = 1e-4, lambda = NULL, trace = FALSE, 
                             type = c("discrete", "hybrid", "friedman","lars"),
@@ -473,7 +469,7 @@ l2boost.default <- function(x, y,
 #' 
 #' @aliases l2boost.formula l2boost.default
 #' 
-#' @export l2boost.formula
+#' @export
 l2boost.formula <- function(formula, data, ...){  
     mf <- model.frame(formula=formula, data=data)
     x<- model.matrix(attr(mf, "terms"), data=mf)

R/plot.l2boost.R

@@ -108,8 +108,7 @@
 #' # red points are selected in boosting algorithm.
 #' plot(coef(cv.l2$fit, m=cv.l2$opt.step), col=clr, ylab=expression(beta))
 #' }
-#' @export plot.l2boost
-#' 
+#' @export 
 plot.l2boost <- function(x, 
                          type = c("rho", "coef"),
                          standardize = TRUE, active.set=NULL,

R/predict.l2boost.R

@@ -60,7 +60,7 @@
 #' # Or used to predict new data, in this case a subset of training data
 #' cbind(diabetes$y[1:5], predict(object, xnew=diabetes$x[1:5,])$yhat)
 #' 
-#' @export predict.l2boost
+#' @export
 predict.l2boost <- function(object, xnew = NULL, type = c("fit", "coef"), newdata=xnew, ...) {
   if(inherits(object,"cv")) object <- object$fit
 

R/print.l2boost.R

@@ -32,7 +32,7 @@
 #' # Similar at m=100
 #' print(object, m=100)
 #' 
-#' @export print.l2boost
+#' @export
 print.l2boost <- function(x, m = NULL, ...){
   call<-match.call()
 

R/residuals.l2boost.R

@@ -49,7 +49,7 @@
 #'   lty=2, col="red", lwd=2)
 #' abline(h=0, lty=2, col = 'gray')
 #' 
-#' @export residuals.l2boost
+#' @export 
 residuals.l2boost <- function(object, m=NULL, ...){
   if(inherits(object, "cv")) object<- object$fit