[Mboost-commits] r735 - in pkg/mboostDevel: R man

[noreply at r-forge.r-project.org]

Author: hofner
Date: 2013-09-10 18:28:20 +0200 (Tue, 10 Sep 2013)
New Revision: 735

Modified:
   pkg/mboostDevel/R/methods.R
   pkg/mboostDevel/man/baselearners.Rd
Log:
- fixed a problem with extract() of single base-learners 
  (doesn't require execution of dpp() anymore)


Modified: pkg/mboostDevel/R/methods.R
===================================================================
--- pkg/mboostDevel/R/methods.R	2013-09-10 12:38:11 UTC (rev 734)
+++ pkg/mboostDevel/R/methods.R	2013-09-10 16:28:20 UTC (rev 735)
@@ -526,9 +526,16 @@
 extract.blg <- function(object, what = c("design", "penalty", "index"),
                         asmatrix = FALSE, expand = FALSE, ...){
     what <- match.arg(what)
-    object <- object$dpp(rep(1, NROW(object$model.frame())))
-    return(extract(object, what = what,
-                   asmatrix = asmatrix, expand = expand))
+    #object <- object$dpp(rep(1, NROW(object$model.frame())))
+    # return(extract(object, what = what,
+    #               asmatrix = asmatrix, expand = expand))
+    if (what == "design")
+        mat <- get("X", envir = environment(object$dpp))
+    if (what == "penalty")
+        mat <- get("K", envir = environment(object$dpp))
+    if (what == "index")
+        mat <- get("index", envir = environment(object$dpp))
+    return(mat)
 }
 
 extract.bl_lin <- function(object, what = c("design", "penalty", "lambda", "df",

Modified: pkg/mboostDevel/man/baselearners.Rd
===================================================================
--- pkg/mboostDevel/man/baselearners.Rd	2013-09-10 12:38:11 UTC (rev 734)
+++ pkg/mboostDevel/man/baselearners.Rd	2013-09-10 16:28:20 UTC (rev 735)
@@ -176,8 +176,10 @@
   \item{K}{ penalty matrix as it should be used in the penalized least
     squares estimation. If \code{NULL} (default), unpenalized estimation
     is used. }
-  \item{deriv}{an integer; the derivative of the spline of the given order 
-               at the data is computed, defaults to zero.}
+  \item{deriv}{an integer; the derivative of the spline of the given order
+               at the data is computed, defaults to zero. Note that this
+	       argument is only used to set up the design matrix and
+	       cannot be used in the fitting process.}
   \item{bl1}{a linear base-learner or a list of linear base-learners.}
   \item{bl2}{a linear base-learner or a list of linear base-learners.}
 }
@@ -357,9 +359,10 @@
   product of two matrices \code{X = kronecker(X2, X1)}, then \code{bl1
   \%O\% bl2} with design matrices X1 and X2, respectively, can be used
   to efficiently compute Ridge-estimates following Currie, Durban,
-  Eilers (2006). In cases the overall degrees of freedom of the combined
-  base-learner increase (additive or multiplicative, respectively).
-  These three features are experimental and for expert use only.
+  Eilers (2006). In all cases the overall degrees of freedom of the
+  combined base-learner increase (additive or multiplicative,
+  respectively). These three features are experimental and for expert
+  use only.
 
   \code{btree} fits a stump to one or more variables. Note that
   \code{\link{blackboost}} is more efficient for boosting stumps. For