search for: vimp

Hi I use pixmapIndexed tmp.vimp <- array(0,c(x.dim,y.dim)) tmp.vimp <- pixmapIndexed(tmp.vimp, col=rainbow) to plot values of a 2D matrix. I 'fill' the pixmapIndexed like: for (x in 1:x.dim) { for (y in 1:y.dim) { tmp.vimp at index[x,y] <- my.matrix[x,y] }} how can I de...

...---------------------------------------------------------------------------------------------- Release 3.2.0 represents a significant upgrade in the functionality of the product. Key changes are as follows: o A second method of perturbing the data set in order to calculate variable importance (VIMP) has been implemented. In addition to permuting the values for a single variable, a random split approach has been taken in which a data point is randomly assigned to the left or right daughter node when a split occurs on the specified variable. o The joint VIMP among multiple variables of a (pot...

...---------------------------------------------------------------------------------------------- Release 3.2.0 represents a significant upgrade in the functionality of the product. Key changes are as follows: o A second method of perturbing the data set in order to calculate variable importance (VIMP) has been implemented. In addition to permuting the values for a single variable, a random split approach has been taken in which a data point is randomly assigned to the left or right daughter node when a split occurs on the specified variable. o The joint VIMP among multiple variables of a (pot...

...extract the ensembles for a single tree or subset of trees over the forest. The default nodesize for survival and competing risk has been changed to 15. We've added new splitrules "auc" and "entropy" for classification. A new variable importance methodology called Holdout VIMP has been implemented. Here, we exclude a variable from a subset of trees and compare the error rates between those trees in which the variables was included against those in which it was excluded. The key point here is that no permutation of a variable is conducted. See holdout.vimp.rfsrc() and t...

...extract the ensembles for a single tree or subset of trees over the forest. The default nodesize for survival and competing risk has been changed to 15. We've added new splitrules "auc" and "entropy" for classification. A new variable importance methodology called Holdout VIMP has been implemented. Here, we exclude a variable from a subset of trees and compare the error rates between those trees in which the variables was included against those in which it was excluded. The key point here is that no permutation of a variable is conducted. See holdout.vimp.rfsrc() and t...

...ity will migrate to the new CRAN package, Random Forests for Survival, Regression, and Classification, that will be released in the coming months. Key changes to the current release are as follows: o The ability to fully analyze competing risk data including ensemble ?estimation, error rates, and VIMP by event type. ?Can also predict ?on test data. ?Missing data imputation is also available. See rsf() ?and competing.risk() for details. o Automatic variable selection using minimal depth theory implemented ?in the new function varSel(). ?Also see the core function ?max.subtree(). o Pairwise inte...

...ity will migrate to the new CRAN package, Random Forests for Survival, Regression, and Classification, that will be released in the coming months. Key changes to the current release are as follows: o The ability to fully analyze competing risk data including ensemble ?estimation, error rates, and VIMP by event type. ?Can also predict ?on test data. ?Missing data imputation is also available. See rsf() ?and competing.risk() for details. o Automatic variable selection using minimal depth theory implemented ?in the new function varSel(). ?Also see the core function ?max.subtree(). o Pairwise inte...

...25:37 +0200 | From: Christoph Lehmann <christoph.lehmann at gmx.ch> | Subject: [R] pixmapIndexed color question | To: r-help at stat.math.ethz.ch | Message-ID: <40F25911.3070607 at gmx.ch> | Content-Type: text/plain; charset=us-ascii; format=flowed | | Hi | | I use pixmapIndexed | | tmp.vimp <- array(0,c(x.dim,y.dim)) | tmp.vimp <- pixmapIndexed(tmp.vimp, col=rainbow) | | to plot values of a 2D matrix. I 'fill' the pixmapIndexed like: | | for (x in 1:x.dim) { | for (y in 1:y.dim) { | tmp.vimp at index[x,y] <- my.matrix[x,y] | }}...