similar to: Partial dependence plot in randomForest package (all flat responses)

Dear R list, I have an event history data set that is structured like this: Legislative act Discussion Agreement Time Event Act1 2006-05-30 2006-06-19 20 1 Act2 2004-03-01 2004-06-14 105 1 . . . I have information on the meetings in the legislature between adoption periods in a separate variable (the

Hi, I need to sample randomly my dataset for 1000 times. The sample need to be the 80%. I know how to do that, my problem is that not only I need the 80%, but I also need the corresponding 20% each time. Is there any way to do that? Alternatively, I was thinking to something like setdiff () function to compare my 80% sample to the original dataset and obtain the corresponding 20%, unfortunately

Hi, I am reading a large space-delimited text file into R (41 columns and many rows) and need to do run each row's values through another R object and then write to another text file. So, far using readLines and writeLines seems to be the best bet. I've gotten the data exchange working except each row is read in as one 'chunk', meaning the row has all values between two quotes

Hi, I have a data.frame that contains a variable act which records the duration (in seconds) of two states (wet-dry) for several individuals (identified by Ring) over a period of time. Since I want to work with daytime (i.e. from sunrise till sunset) and night time (i.e. from sunset till next sunrise), I have to split act from time[i] till sunset and from sunset until time[i+1], and from time[k]

Hi all, Had struggled in getting "Strata" in randomForest to work on this. Can I get randomForest for each of its TREE, to get ALL sample from some strata to build tree, while leaving some strata TOTALLY untouched as oob? e.g. in below, how I can tell RF to, - for tree 1 in the forest, to use only Site A and B to build the tree, while using the WHOLE Site C data for the oob error

Hi All, > act_2 Date Dtime Hour Min Second Rep 51 2006-02-22 14:52:18 14 52 18 useractivity_act 52 2006-02-22 14:52:18 14 52 18 4 55 2006-02-22 14:52:49 14 52 49 4 57 2006-02-22 14:52:51 14 52 51 4 58 2006-02-22 14:52:52 14 52 52 3 60 2006-02-22 14:54:42 14 54

My data frame shows changes on the variable act which records the consecutive duration (in seconds) of two states (wet-dry) over a few days for several?individuals (identified by Ring). Since I want to work with daytime (i.e. from dawn till dusk) and night time (i.e. from dusk till next dawn), I have to split act in two: from time[i] till dusk and from dusk until time[i+1], and from time[k] till

I'd like to announce the availability of version 0.1-4 of the "doMPI" package, a parallel backend for the "foreach" package, which acts as an adaptor to the "Rmpi" package. The package has been uploaded to CRAN and is now available under the GPL-2 license. In addition to fixing a few problems in version 0.1-3, the main new feature of this release is the

I'd like to announce the availability of version 0.1-4 of the "doMPI" package, a parallel backend for the "foreach" package, which acts as an adaptor to the "Rmpi" package. The package has been uploaded to CRAN and is now available under the GPL-2 license. In addition to fixing a few problems in version 0.1-3, the main new feature of this release is the

Hi, i try several possibilities adn looking in the archive, but didn't getting success to install j.lindsey's usefuel "library repeated" on my linux (suse9.0 with kernel 2.6.7,R.1.9.1) P.S. Windows, works fine Many thanks for help Christian chris at linux:/space/downs> R CMD INSTALL - l /usr/lib/R/library repeated WARNING: invalid package '-' WARNING:

Hello, I am struggling to produce an MDS plot using the randomForest package with a moderately large data set. My data set has one categorical response variables, 7 predictor variables and just under 19000 observations. That means my proximity matrix is approximately 133000 by 133000 which is quite large. To train a random forest on this large a dataset I have to use my institutions high

I am using randomForest to classify (relapse vs non-relapse) patients. I have built a forest using a training data and now want to predict classes in a test dataset. Instead of using the resulting randomForest object. I was wondering if there is a way to use the MDSplot. From looking at the MDS plot it seems like I could draw some lines through the plot to define 'high risk',

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

Greg, Two thoughts: 1. It might be possible that 'vars' is a reserved word of sorts and if you change the name of your vector RF might be happier 2. A way that works for me is to call importance as follows: sel.imp <- importance(sel.rf, class=NULL, scale=TRUE, type=NULL) and then use the 'names' of the imp data frame to be absolutely clear to RF you are talking about the

Dear R Users, I'm using the randomForest package and would like to generate partial dependence plots, one after another, for a variety of variables: m <- randomForest( s, ... ) varnames <- c( "var1", "var2", "var3", "var4" ) # var1..4 are all in data frame s for( v in varnames ) { partialPlot( x=m, pred.data=s, x.var=v ) } ...but this

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

All, I'm trying to set up a function which calls the partialPlot function but am getting an error that I can't seem to solve. Here's a simplified version of the function and error... > pplot <- function(rf,pred.var){partialPlot(x=rf,pred.data=acoust,x.var=pred.var)} > > attach(acoust) > acoust.rf <-

I'm having trouble calling randomForest::partialPlot programmatically. It tries to use name of the (R) variable as the data column name. Example: library(randomForest) iris.rf <- randomForest(Species ~ ., data=iris, importance=TRUE, proximity=TRUE) partialPlot(iris.rf, iris, Sepal.Width) # works partialPlot(iris.rf, iris, "Sepal.Width") # works (function(var.name)

> > 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

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