similar to: Ordered probit model -marginal effects and relative importance of each predictor-

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

Hi, I would like to know, what's the importance of sid and rid, for: 1) a win2k to samba migration, with roaming profiles 2) brand new domain on samba, with roaming profiles If rid and sid need to be migrated to a samba+ldap how do I do it? Another thing is, I've read in ldap-smb-HEAD-howto.html that the builtin accounts are implemented internally in the samba server. Does this mean I

Hi, All. I have a logistic regression model that I have run. The question came up: which of these regressors is more important than another? (I'm using Design) Logistic Regression Model lrm(formula = iconicgesture ~ ST + SSP + magnitude + Condition + Expertise, data = d) Coef S.E. Wald Z P Intercept -3.2688 0.2854 -11.45 0.0000 ST 2.0871 0.2730 7.64

Hi, I've a question about the RandomForest package. The package allows the extraction of a variable importance measure. As far as I could see from the documentation, the computation is based on the Gini index. Do you know if this extraction can be also based on other criteria? In particular, I'm interested in the info gain criterion. Best regards, Chris --

For anyone who is knowledgeable about the randomForest package in R, I have a question: When I look at the variable importance for data, I see that my response variable is included along with my predictor variables. That is, I am getting a MeanDecreaseGini for my response variable, and therefore it seems as though it is being treated as a predictor variable. my code (just in case it helps) :

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

I have a dataset of multiple variables and a response. For example, > str(x) 'data.frame': 3557238 obs. of 44 variables: $ response : Factor w/ 2 levels $ var2: Factor w/5000 levels If var2 for example is a factor with 5000 levels, what is the best approach to determine which of these levels is the most important to include in building the model, and which ones to discard.

> 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]]

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

Dear All, I am using randomForest to predict the final selling price of some items. As it often happens, I have a lot of (noisy) historical data, but the question is not so much about data cleaning. The dataset for which I need to carry out some predictions are fairly recent sales or even some sales that will took place in the near future. As a consequence, historical data should be somehow

Hello! I think I am relatively clear on how predictor importance (the first one) is calculated by Random Forests for a Classification tree: Importance of predictor P1 when the response variable is categorical: 1. For out-of-bag (oob) cases, randomly permute their values on predictor P1 and then put them down the tree 2. For a given tree, subtract the number of votes for the correct class in the

Hei Group, I want to compare the relative importance of predictors in a multiple linear regression y~a+bx1+cx2... However, bptest indicates heteroskedasticity of my model. I therefore perform a robust regression (rlm), in combination with bootstrapping (as outlined in J. Fox, Bootstrapping Regression Models). Now I want to compare the relative importance of my predictors. Can I rely on the

Dear R user! I have a small question! I have calculated the relative importance of the variables. However I would like to compare the relative importance of two different groups of variables (i.e Strategy and industry) For example let me say that strategy has 2 sub varialbes and industry has four different variables! Can I simply add the importance of those four industry variables importance