similar to: interpret the importance output?

Hi, I am using the integrate function in some simulations in R (tried ver 2.12 and 2.15). The problem I have is that the last few rows do not integrate correctly. I have pasted the code I used. The column named "integral" shows the output from the integrate function. The last few rows have no integration results. I tried increasing the doses, number of subjects, etc.... this error occurs

Dear R users, I am running a loop with the integrate function. I have pasted the code below. I am integrating a function from time=0 to the time value in every row. I have to perform this integration over thousands of rows with different parameters in each row. Could someone please suggest if there is an efficient/faster/easier way to do this by avoiding the loops ? Thank you so much for your

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

Hi I am trying to explore the use of random forests for regression to identify the important 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

I have a question about predictor importances in randomForest. Once I've run randomForest and got my object, I get their 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

Hi everyone, I have another "Random Forest" package question: - my (presumably incorrect) understanding of the varImpPlot is that it should plot the "% increase in MSE" and "IncNodePurity" exactly as can be found from the "importance" section of the model results. - However, the plot does not, in fact, match the "importance"

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

Hello, I am running randomForest models on a number of species. 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

I'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

I have data that looks like this: > df1 group id 1 red A 2 red B 3 red C 4 blue D 5 blue E 6 blue F I want a list of the groups containing vectors with the ids. I am avoiding subset(), as it is only recommended for interactive use. Here's what I have so far: df1 <- data.frame(group=c("red", "red", "red", "blue",

Dear R Helpers, I need help with a slightly unusual situation in which I am trying to select some columns from a data frame. I know how to use the subset statement with column names as in: x=as.data.frame(matrix(c(1,2,3, 1,2,3, 1,2,2, 1,2,2, 1,1,1),ncol=3,byrow=T)) all.cols<-colnames(x) to.keep<-all.cols[1:2] Kept<-subset(x,select=to.keep) Kept

Hello, all. I wanted to use the "survey" package to analyze data from the National Ambulatory Medical Care Survey, and am having some difficulty translating the analysis keywords from one package (Stata) to the other (R). The data were collected using a multistage probability sampling, and there are variables included to identify the sampling units and weights. Documentation from the

Hi - When using the randomForest function for regression, I get different results for mean-squared error of the predictions depending on whether or not I specify to calculate variable importance. There is an example below. I looked briefly at the source code, but couldn't find anything that would indicate why calculating variable importance would (or should) change predictions. I'm

i am new to R and using the random forest package. is there a way to specify the x-axis scale range for the variable importance plot? many thanks. -alison -- View this message in context: http://www.nabble.com/specifying-x-axis-scale-on-random-forest-variable-importance-plot-tp19780560p19780560.html Sent from the R help mailing list archive at Nabble.com.

I have installed the latest relaimpo library ( form their website with the 8 functions) When running pvmd as a type in c=type("pmvd") in calc.relimp I get the error message ...could not find function "pmvdcalc" (this is in R version 2.12.1) Can anyone help? Paul Prof P Rheeder School of Health Systems and Public Health Faculty of Health Sciences University of Pretoria Room

Hello, I have an object of Random Forest : iris.rf (importance = TRUE). What is the difference between "iris.rf$importance" and "importance(iris.rf)"? Thank you in advance, Best, Li GUO [[alternative HTML version deleted]]

Dear R people, I would like to know if it is possible to compute the relative importance of the inputs to a neurol network computed with the function nnet in the nnet package. Thank you so much!!! Victor Robles [[alternative HTML version deleted]]

Hello, I'm trying to use the party package function varimp() to get conditional variable importance measures, as I'm aware that some of my variables are correlated. However I keep getting error messages (such as the example below). I get similar errors with three separate datasets that I'm using. At a guess it might be something to do with the very large number of variables (e.g.

I have set up a directory (/var/local/virtualdir/) as the maildir with a special user/group (virtualmail) and in it I create directories for each domain (example1.tld, example2.tld etc) and sub dir's for each mailbox). Only at the moment, apart from the first one, they are all created as with user root as the owner. How important is to have all the directory tree owned by the same user group

Item 1: I store the indexes and control in their own directory. I noticed in 1.1: 1192 ./private/indexes/.INBOX 32 ./private/indexes/.Trash 1228 ./private/indexes 4 ./private/control/.INBOX/.INBOX 84 ./private/control/.INBOX 4 ./private/control/.Trash/.Trash 12 ./private/control/.Trash 100 ./private/control 1332 ./private .INBOX/.INBOX and .Trash/.Trash