documentation and fix trace for cvrisk of noncyclical fitting

NAMESPACE

@@ -53,6 +53,7 @@ S3method(cvrisk, mboostLSS)
 S3method(cvrisk, nc_mboostLSS)
 S3method(print, cvriskLSS)
 S3method(plot, cvriskLSS)
+S3method(plot, nc_cvriskLSS)
 S3method(mstop, cvriskLSS)
 
 S3method(stabsel, mboostLSS)

R/cvrisk.nc_mboostLSS.R

@@ -20,15 +20,15 @@ cvrisk.nc_mboostLSS <- function(object, folds = cv(model.weights(object)),
   call <- deparse(attr(object, "call"))
   oobrisk <- matrix(0, nrow = ncol(folds), ncol = length(grid))
   if (trace)
-    cat("Starting cross-validation...\n",
-        "[fold]\t[current mstop]\n", sep = "")
+    cat("Starting cross-validation...")
   if (is.null(fun)) {
     dummyfct <- function(i, weights, oobweights) {
-      ## make model with new weights and minimal mstop
+      if (trace)
+        cat("\n[Fold: ", i, "]\n", sep = "")
+      ## make model with new weights and max mstop
       mod <- update(object, weights = weights, oobweights = oobweights,
-                    risk = "oobag", trace = FALSE,
-                    mstop = max(grid))
-
+                    risk = "oobag",
+                    mstop = max(grid), trace = trace)
 
 
       risks <- attr(mod, "combined_risk")()[grid]
@@ -45,13 +45,15 @@ cvrisk.nc_mboostLSS <- function(object, folds = cv(model.weights(object)),
   OOBweights[folds > 0] <- 0
   if (all.equal(papply, mclapply) == TRUE) {
     oobrisk <- papply(1:ncol(folds),
-                      function(i) dummyfct(weights = folds[, i],
+                      function(i) dummyfct(i = i,
+                                           weights = folds[, i],
                                            oobweights = OOBweights[, i]),
                       mc.preschedule = mc.preschedule,
                       ...)
   } else {
     oobrisk <- papply(1:ncol(folds),
-                      function(i) try(dummyfct(weights = folds[, i],
+                      function(i) try(dummyfct(i = i,
+                                               weights = folds[, i],
                                                oobweights = OOBweights[, i])),
                       ...)
   }
@@ -75,7 +77,7 @@ cvrisk.nc_mboostLSS <- function(object, folds = cv(model.weights(object)),
   attr(oobrisk, "mstop") <- grid
   attr(oobrisk, "type") <- ifelse(!is.null(attr(folds, "type")),
                                   attr(folds, "type"), "user-defined")
-  class(oobrisk) <- "cvrisk"
+  class(oobrisk) <- c("nc_cvrisk", "cvrisk")
   oobrisk
 }
 
@@ -89,3 +91,11 @@ risk.nc_mboostLSS <- function(object, merge = FALSE,
   }
 
 }
+
+
+plot.nc_cvriskLSS <- function(x, ylab = attr(x, "risk"),
+                                xlab = "Number of boosting iterations",
+                                ylim = range(x), main = attr(x, "type"), ...) {
+  mboost:::plot.cvrisk(x, ylab, xlab, ylim, main, ...)
+}
+

man/cvrisk.Rd

@@ -4,6 +4,7 @@
 \alias{cvrisk.nc_mboostLSS}
 \alias{make.grid}
 \alias{plot.cvriskLSS}
+\alias{plot.nc_cvriskLSS}
 
 \title{ Cross-Validation }
 \description{
@@ -35,10 +36,14 @@ make.grid(max, length.out = 10, min = NULL, log = TRUE,
     bootstrap samples.
   }
   \item{grid}{
-    a matrix of stopping parameters the empirical risk is to be
-    evaluated for. Each row represents a parameter combination. The
-    number of columns must be equal to the number of parameters of the
-    GAMLSS family. Per default, \code{make.grid(mstop(object))} is used.
+    a matrix of stopping parameters the empirical risk is to be evaluated for, 
+    if the model was fitted with \code{method = "cyclical"}. Each row represents a 
+    parameter combination. The number of columns must be equal to the number of 
+    parameters of the GAMLSS family. Per default, \code{make.grid(mstop(object))} 
+    is used.
+    Otherwise (i.e. \code{method = "inner"} or \code{method = "outer"}) one 
+    vector of steps to check. Per default all steps up to \code{mstop(object)} 
+    are used.
   }
   \item{papply}{
     (parallel) apply function, defaults to  \code{\link[parallel]{mclapply}}.
@@ -121,6 +126,9 @@ make.grid(max, length.out = 10, min = NULL, log = TRUE,
   additionally shows information on the variability of the risk from
   fold to fold. The heatmap shows only the average risk but in a nicer
   fashion.
+  For the noncyclical fitting methods (i.e. \code{method = "inner"} or 
+  \code{method = "outer"}) the plot function of \code{\link{cvrisk}} from the 
+  packge \code{mboost} is used.
 
   Hofner et al. (2015) provide a detailed description of
   cross-validation for \code{\link{gamboostLSS}} models and show a