search for: incmse

Hi I am having a problem using varImpPlot in randomForest. I get the error message "Error in plot.window(xlim = xlim, ylim = ylim, log = "") : need finite 'xlim' values" When print $importance, several variables have NaN under %IncMSE. There are no NaNs in the original data. Can someone help me figure out what is happening here? Thanks! [[alternative HTML version deleted]]

...ies. I would like to be able to automate the printing of dependence plots for the most important variables in each model, but I am unable to figure out how to enter the variable names into my code. I had originally thought to extract them from the $importance matrix after sorting by metric (e.g. %IncMSE), but the importance matrix is n by 2 - containing only the data for each metric (%IncMSE and IncNodePurity). It is clearly linked to the variable names, but I am unsure how to extract those names for use in scripting. Any assistance would be greatly appreciated as I am currently typing the varia...

...9;ve run the function randomForest with importance=T. All my variables (predictors and the dependent variable) are numeric. rf<-randomForest(formula, data=mydata, importance=T, etc.) my results object "rf" contains predictor importances: rf$importance I am seeing two columns: %IncMSE IncNodePurity V1 -0.01683558 58.10910 V2 0.04000299 71.27579 V3 0.01974636 67.22586 V4 0.25020393 113.69823 V5 0.03146358 67.11151 V6 0.01717313 66.57246 V7 -0.00500985 62.37103 V8 -0.02862065 66.15369 V9 -0.02431507 54.50013 They seem to be clearly...

I am trying to use the package RandomForest performing regression. The variable importance estimates are given as: "%IncMSE" and "IncNodePurity" Can anyone explain me what these refer to and how they are calculated? I found a lot of information on variable importance measures for classification problems, but nothing on regression. Thanks a lot. Mareike

> importance(rfor.pdp11_t25.comb1,type=1) %IncMSE v1 -0.28956401263 v2 1.92865561147 v3 -0.63443929130 v4 1.58949137047 v5 0.03190940065 I wasn't entirely confident with interpreting these results based on the documentation. Could you please interpret? [[alternative HTML version deleted]]

...m forest then I find myself stuck. The example below demonstrates the problem. library(randomForest) data(mtcars) mtcars.rf <- randomForest(mpg ~ ., data=mtcars,importance=TRUE) Importance<-data.frame(mtcars.rf$importance) Importance MSEImportance<-head(Importance[order(Importance$X.IncMSE, decreasing=TRUE),],3) MSEVars<-row.names(MSEImportance) MSEVars<-data.frame(MSEVars,stringsAsFactors = FALSE) colnames(MSEVars)<-"Vars" NodeImportance<-head(Importance[order(Importance$IncNodePurity,decreasing=TRUE),], 3) NodeVars<-row.names(NodeImportance) NodeVars<-d...

...rtant environmental/microclimate variables involved in predicting the abundance of a species in different habitats, there are approx 40 variable and between 200 and 500 data points depending on the dataset. I have successfully used the randomForest package to conduct the analysis and looked at the %IncMSE and IncNodeImpurity values given by calling and plotting these out and have looked at the partial dependence plots for the different variables effect of the response but I have been looking though the literature to see how people have previously used this type of analysis and I would like to be abl...

...eir importances: rfresult$importance I also get the "standard errors" of the permutation-based importance measure: rfresult$importanceSD I have 2 questions: 1. Because I am dealing with regressions, I am getting an importance object (rfresult$importance) with two columns, labeled "%IncMSE" (the first column) and "IncNodePurity" (the second column). I assume it's the first one that is the mean decrease in accuracy due to permutation. Am I correct or am I wrong? I am confused because ?randomForest says: "or Regression, the first column is the mean decrease in a...

...nd from the "importance" section of the model results. - However, the plot does not, in fact, match the "importance" section of the random forest model. E.g., if you use the example given in the ?randomForest, you will see the plot showing the highest few "%IncMSE" values around 17 or 18%. But if you look at the $importance, it is 9.7, 9.4, 7.7, and 7.3. Perhaps more importantly, for the plot, it will show "wt" is highest %MSE, then "disp", then "cyl", then "hp"; whereas the $importance will show "wt",...

...gt; subsequent assignments thereto (Jim Bouldin) > 68. Re: converting object elements to variable names and making > subsequent assignments thereto (R. Michael Weylandt) > 69. Re: Newbie question: Converting Table (David Winsemius) > 70. Re: randomForest - NaN in %IncMSE (Liaw, Andy) > 71. Re: Small Area Estimate Using Structural Equation Models > (Peter Maclean) > 72. Re: Kolmogorov-Smirnov test (Greg Snow) > 73. Re: Adding weights to optim (Rolf Turner) > 74. Re: Adding weights to optim (Ahnate Lim) > 75. ncdf - install err...