similar to: Random Forest Variable Importance Interpretation

Hi all I have been trying to use the randomForest package to model insect species abundance in different habitats and identify the key variables (landscape/climate etc) in determining abundance, which has all worked fine and I get nice variable importance plots etc. Many thanks to everyone on this help forum who has given tips/advice along the way. But the percentage variance explained /pseudo r

Hi I am using R version 2.7.2. on a windows XP OS and have a question concerning an analysis of covariance with count data I am trying to do, I will give details of a scaled down version of the analysis (as I have more covariates and need to take account of over-dispersion etc etc) but as I am sure it is only a simple problem but I just can't see how to fix it. I have a data set with count

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 all I am hoping this is just a minor problem, I am trying to implement a best subsets regression procedure on some ecological datasets using the regsubsets function in the leaps package. The dataset contains 43 predictor variables plus the response (logcount) all in a dataframe called environment. I am implementing it as follows: library(leaps)

hello, i am new to R and want to use it for a small project to draw a wind data from a microclimate datasource, can someone give me an example of how i can represent this in a neat way? for example, i have: speed, direction 0.3,NNE 0.45,NNE 0.32,NE 0.28,N 0.30,NE how do i put this data to get a windrose graph? many thanks norman -- %>>> "".join( [

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

> 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 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

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 I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables

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"

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, Sometimes I have to create and use Navision Databases on samba shares - this works with samba 2.0.x but does not with samba 2.2.x (tried 2.2.3a and 2.2.4). The following tests were done with a acl enabeled samba and an acl aware kernel/filesystem, but I also have done these tests with acl not compiled into samba on an host without acl in the kernel - same result, does not work, same

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

Hello All, I have a list of dataframes, and I need to subset it by keeping only those dataframes in the list that meet a certain criterion. Specifically, I need to generate a second list which only includes those dataframes whose number of rows is > 1. Could someone suggest how to do this? I have come close to what I need with loops and such, but there must be a less clumsy way... Many

I am sorry to bug you, I am having this error whenever I want to run random effects regression in software R: Error in if (sigma2$id < 0) stop(paste("the estimated variance of the", : missing value where TRUE/FALSE needed. Please help me look into it. [[alternative HTML version deleted]]

Hello All, I have a data.frame with this structure: m <- matrix(sample(c(rep('yes', 10, replace = TRUE), rep('no', 10, replace = TRUE), NA), 500, replace = TRUE), nrow = 100, ncol = 5) colnames(m) <- colnames(m, do.NULL = FALSE, prefix = "col") m <- as.data.frame(m) I need to generate a Venn diagram from this data.frame, displaying the various intersections of

Hola a todos Me gustaría saber si me pueden ayudar con lo siguiente. Realicé un Boxplot usando ggplot2 para visualizar el comportamiento de dos variables. Visualmente no se notan las diferencias porque la gráfica de la derecha (parásitos en el abdomen) llega hasta 20 en el eje y. ¿Cómo puedo hacer para que las dos gráficas muestren la misma escala en el eje Y, es decir, que las dos lleguen a 60?

Dear List, I have a large (1600*1600) matrix generated with symnum, that I am using to eyeball the structure of a dataset. I have abbreviated the column names with the abbr.colnames option. One way to get an even more compact view of the matrix would be to display the column names rotated by 90 degrees. Any pointers on how to do this would be most useful. Any other tips for displaying the

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