similar to: Error while using rfImpute

I am having trouble with the rfImpute function in the randomForest package. Here is a sample... clunk.roughfix<-na.roughfix(clunk) > > clunk.impute<-rfImpute(CONVERT~.,data=clunk) ntree OOB 1 2 300: 26.80% 3.83% 85.37% ntree OOB 1 2 300: 18.56% 5.74% 51.22% Error in randomForest.default(xf, y, ntree = ntree, ..., do.trace = ntree, : NA not

In trying to execute this line in R (Version 1.7.1 (2003-06-16), under windows XP pro), with the randomForest library (about two weeks old) loaded, the program crashed: bost4rf <- rfImpute(TargetDensity~.,data=bost4rf0) Specifically, an XP dialog box popped up, saying ?R for windows GUI front-end has encountered a problem and needs to close.? That was the dialog saying asking whether I

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I tried rfImpute (randomForest) and as far as I understood should it replace NA`s using a proximity matrix: > set.seed(100000) > Subset5Imputed<-rfImpute(Sex~., data=Subset5) ntree OOB 1 2 300: 11.78% 12.36% 11.21% ntree OOB 1 2 300: 12.07% 12.64%

Hello everybody, this is my first request about R so I am sorry if I send it to a bad mail or if I am not very clear. So my problem is about the use of rfImpute from randomForest package. I am interested in imputations of missing values and I read that randomForest can make it. So i write the following code : set.seed(100); library(mlbench) library(randomForest) data(BreastCancer)

Hello, I'm running into a very strange problem: > xrange <- c(-2.5,2.5) > xdim <- 100 > mobility <- 0.1 > slope <- 1.16 > urange <- slope*xrange > udim <- max(slope*xdim,5) > du <- (urange[2]-urange[1])/udim > uvec <- urange[1]+(1:udim-0.5)*du > # type dependent weight function > ckern <-

Dear all; Working with the following code extracted from the document S Poetry by Patrick Burns (from CRAN), I haven't been able to load the resulting dll into R. The code is basically the calculation of the quadratic form x'Qx: static double quad_form(double *Q, double *x, long n) { long i, j, ij; double ans = 0.0; for(i=0; i < n; i++) { for(j=0, ij = i * n; j < n; j++,

I can do this by writing (and reading) the file according to some format of my own devising, but I'm wondering if there is a built-in way to write and then restore a matrix with not only the dimnames (which write.table/read.table can preserve) but also the names(dimnames)? Example: > M <- matrix(1:4, 2, 2) > dimnames(M) <- list(xdim=c("a", "b"),

Hello, I have a problem writing a variable to an existing file. Below is a part of my script and how it fails. I can't find "create.var.ncdf" in help Thanks for any help. Mark nc <- open.ncdf(ncname, readunlim=FALSE, write=TRUE ) missing <- 1.e+30 xdim <- nc$dim[["west_east"]] ydim <- nc$dim[["south_north"]] tdim <- nc$dim[["Time"]]

Hi all, I originally posted this to the bioconductor group, but maybe it's better suited to the r-help... I'm using som() to partition samples of gene expression data into clusters. The point is to classify control vs. experimental cases (sample clustering). The original matrix was 22283 x 8. The 8 samples have 4 controls and 4 experimentals. I transposed the matrix so that its dim

Does anyone know a reason why, in principle, a call to randomForest cannot accept a data frame with missing predictor values? If each individual tree is built using CART, then it seems like this should be possible. (I understand that one may impute missing values using rfImpute or some other method, but I would like to avoid doing that.) If this functionality were available, then when the trees

Dear all I'm a newbie regarding netcdf data. Today I realized that I maybe do not understand some basics of the netcdf. I want to create a *.nc file containing three variables for Switzerland. All data outside of the country are NAs. The third variable is calculated from the first two variables. Basically there is no problem to do that. I copy the file with the data of the first variable,

I have a question regarding NA in randomForest (in R). I have a dataset which include both numerical and non-numerical variables, and the data includes some NA. I tried to use na.roughfix but then i get an error message "na.roughfix only works for numeric or factor". I also tried rfImpute but this does not work either because I have some NA in my response variable. Does anyone have som

Hi all, I am using the package "random forest" for random forest predictions. I like the package. However, I have fairly large data sets, and it can often take *hours* just to go through the "na.roughfix" call, which simply goes through and cleans up any NA values to either the median (numerical data) or the most frequent occurrence (factors). I am going to start

Dear All, Would you please tell me how to display the sample No. on the map ? ---Below commands don't display the sample No.(from 1 to 150).--- library(som) library(klaR) iris.som3 <- som(iris[,1:4], xdim = 14,ydim = 6) library(klaR); opar<- par(xpd = NA) shardsplot(iris.som3, data.or = iris,label = TRUE) legend(3.5,14.3, col = rainbow(3), xjust =0.5, yjust = 0,legend =

Hello R users, I'm using SOM() to cluster a gene expression data set the syntax i used was dataGrid <- c(somgrid(xdim = 3, ydim = 3, topo = c("rectangular","hexagonal"))) dataClusters <- SOM(dataMatrix, grid = dataGrid) plot(dataClusters) it seems that this works just fine but the thing i can't figure out is how to determine where each data point has been

I have a set of timestamp data that I have in a text file that I would like to import into R for analysis. The timestamps are formated as follows: DT_1,DT_2 [2006/08/10 21:12:14 ],[2006/08/10 21:54:00 ] [2006/08/10 20:42:00 ],[2006/08/10 22:48:00 ] [2006/08/10 20:58:00 ],[2006/08/10 21:39:00 ] [2006/08/04 12:15:24 ],[2006/08/04 12:20:00 ] [2006/08/04 12:02:00 ],[2006/08/04 14:20:00 ] I can get

On Mon, Aug 14, 2017 at 5:29 AM, <raphael.felber at agroscope.admin.ch> wrote: Dear all > > I'm a newbie regarding netcdf data. Today I realized that I maybe do not > understand some basics of the netcdf. I want to create a *.nc file > containing three variables for Switzerland. All data outside of the country > are NAs. The third variable is calculated from the first two

Hello Rprofessionals, The SOM-Obj works very well, when i normalize my data and the plot-function, too ! But i miss or didn't find the possibility , extract the information from the SOMplot "clusterSize" and "mean" for every cluster as quantitative information ( i.e. the DataFrame with an additional column which define the calculate clusters from SOM)? My intention -

Hi List, I am trying to plot a grid with an overlayed height. I have a dataframe with four variables: x,y,gridvalue,height. The dataframe has 2.5mio observations (ie grid points), I assign colors through the gridvalue using map_color_gradient thus producing: x,y,gridvalue,height,gridcol as variables of the dataframe. The grid dimensions are 1253 x 2001 (=2507253 data points). My attempts with

Hi guys of the R-Help-Team!<br /><br />First of all - you do a great job!<br />I've found a lot of your mails in the internet. So I thought it would be a good idea to ask you a question about R.<br /><br />R is new to me, so sorry, if the question is too simple :)<br /><br />I have a matrix.<br />I can make a 2D Heatmap of this matrix.<br