similar to: varimp in party (or randomForest)

Hi! I am trying to install the RMySQL package. The installation stops with the following error message. path to mysql is set. setenv PKG_CPPFLAGS /home/arabidopsis/software/R1.9.1/linux/mysql/include setenv PKG_LIBS /home/arabidopsis/software/R1.9.1/linux/mysql/lib R CMD INSTALL RMySQL_0.5-5.tar.gz #....cut cut. creating src/Makevars ** libs gcc

> > I would like to build a forest of regression trees to see how well some > covariates predict a response variable and to examine the importance of > the > covariates. I have a small number of covariates (8) and large number of > records (27368). The response and all of the covariates are continuous > variables. > > A cursory examination of the covariates does not

Friends, I have noticed that many publications that use RF report variable importance as a function of mean decrease in accuracy rather than mean decrease in gini. Am I correct that the mean decrease in accuracy is just the mean decrease in gini divided by 100? Thanks, Helen Mills Poulos Yale School of Forestry

I would like to build a forest of regression trees to see how well some covariates predict a response variable and to examine the importance of the covariates. I have a small number of covariates (8) and large number of records (27368). The response and all of the covariates are continuous variables. A cursory examination of the covariates does not suggest they are correlated in a simple fashion

Hello, I am beginner user of R. I am trying to use GeneNet package. I started by the example on this link: http://strimmerlab.org/software/genenet/download/arabidopsis-net.R However I am getting an error at this line:system("fdp -T svg -o arthdyn.svg arthdyn.dot") # SVG format sh: fdp: command not found I tried to search how to intall the fdp command but could not find explanation. I am

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

Hi, It's unfortunate that with recent revisions of R 2.13 (this appeared in revision 54640, March 2), 'R CMD build' now removes empty dirs in the package. People might have good reasons for having empty dirs in their packages. For example, in Bioconductor, we have some tools to automatically generate annotation packages and those tools are implemented in software packages that use

Dear R-devel, Can anyone give me some hints on how to go about debugging a strange segfault in my randomForest package? Here's the scoop: A user reported segfault when running predict() in the randomForest package. I asked for the data and code. The combination runs fine under WinXPPro, but does give segfault on one of our Linux boxes running R (1.7.0 through R-devel_2004-01-08) on

Gene names being misinterpreted by spreadsheet software (read.csv is no different) is a classic issue in bioinformatics. It seems like every practitioner ends up encountering this issue in due time. E.g. https://pubmed.ncbi.nlm.nih.gov/15214961/ https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1044-7 https://www.nature.com/articles/d41586-021-02211-4

Dear list members, I'm running some tests looking at differences between means for various levels of a factor, using Tukey's HSD method. I would like to plot the data as boxplots or dotplots, with horizontal significance lines indicating which groups are statistically significantly different, according to Tukey HSD. Here's a nice image showing an example of such a graphical

-----Original Message----- From: Robert Gentleman [mailto:rgentlem@fhcrc.org] Sent: Wed 10/12/2008 18:47 To: Legaie, Roxane Subject: Re: Kegg.db with GOstats Hi Roxanne, Can you redirect your question to the mailing list. And, you can find the answer in the mailing list archives... best wishes Robert Legaie, Roxane wrote: > Dear Robert Gentleman, > I am currently working on

Hello, I'm an MD working in an eye clinic. I'm learning by myself to use R for use in my research works and for implementation in a software project. There are some authors who recomends the use of Cox regression as a substitute for Logistic regression (<a href="http://www.biomedcentral.com/1471-2288/3/21.pdf"> Barros AJD, Hirakata VN. BMCMedical Research Methodology, 2003;

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

Dear R-users, we are happy to announce the release of our R package parcor. The package contains tools to estimate the matrix of partial correlations based on different regularized regression methods: Lasso, adaptive Lasso, PLS, and Ridge Regression. In addition, parcor provides cross-validation based model selection for Lasso, adaptive Lasso and Ridge Regression. More details can be found

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Dear R-users, I build already many contour graphs using the contour-procedure in R (akima library), after interpollating my data z on x,y (see e.g. graphs in this paper: http://www.biomedcentral.com/content/pdf/1471-2148-9-16.pdf). Now, however, i experience unexpected and completely unsensible problems: after building the syntax, i can create the graphs and use the interp-command without any

I was trying to produced bagged importance estimates of attributes in earth using the caret package with the following commands:   fit2 <- bagEarth(loyalty ~ ., data=model1, B = 10)   bagImpGCV <- varImp(fit2,value="gcv") My bootstrap estimates are produced however the second command "varImp" produces the following error:    Error in UseMethod("varImp") : no

Hi, I'm using package "caret" to rank predictors using random forest model and draw predictors importance plot. I used below commands: rf.fit<-randomForest(x,y,ntree=500,importance=TRUE) ## "x" is matrix whose columns are predictors, "y" is a binary resonse vector ## Then I got the ranked predictors by ranking

Hi, This is my code (my data is attached): library(languageR) library(rms) library(party) OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv") OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))? OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))? attach(OLDDATA) defaults <- cbind(YD, ND) set.seed(47) data.controls

Hola, Marta, Por lo que he podido ver tus datos ya tienen calculados las tasas de verdaderos y falsos positivos (TPR y FPR). También parece que los tienes ordenados por la variable FishSpeed y supongo que también por las que parecen marcas de tiempo. No necesitas ROCR porque con un simple plot te sale algo parecido a una curva. Eso sí, veo que son medidas repetidas en el tiempo para cada valor de