similar to: Force printing of excluded axis annotations

Howdy, I have read that if you put a URL in the text of a plot being saved into pdf, the result is a functional hyperlink. I am interested in having text in a plot that is linked to a URL, but I would like the text to be something other than the URL. Is this possible? Thank you. - Fincher

Howdy, I have written a small function to generate a simple plot and my colleague is having an error when attempting to run it. Essentially I loop through categories in a data frame and take the average value for each category The categories are in $V1, subset first then mean taken and concatenated to previous values using rbind(c("label",mean(data$V6)). The result is a two-column

Howdy, I am trying to make a simple monochrome heatmap from 1 row of data. Essentially, I just want a long bar where black represents the max value in the data, white is the minimum, and all values in between are interpolated appropriately. I have tried using heatmap and heatmap.2, but both have issues. heatmap seems to be restricted to only drawing a square plot, and this data requires a

Howdy, I have done many searches and can't seem to find a way around this. I am reading in a .csv file where each row is a dataset and each column represents a position. The values are sparse (there are 2003 positions but usually only 100-200 values) and the idea is to be able to plot each dataset (row) and overlay them in different combinations. What I would like to do is have a plot

I have two datasets that I would like to plot in a single figure. The first plot is generated by a function that then takes a subset of the data. (It is biological data so it is usually by chromosome e.g. function(data1,subset="chr8") ) Since not only are the chromosomes different sizes, but across different datasets there may be different numbers of points for a single chromosome, I

hello, I want to use randomForest to classify a matrix which is 331030?42,the last column is class signal.I use ? Memebers.rf<-randomForest(class~.,data=Memebers,proximity=TRUE,mtry=6,ntree=200) which told me" the error is matrix(0,n,n) set too elements" then I use: Memebers.rf<-randomForest(class~.,data=Memebers,importance=TRUE,proximity=TRUE) which told me"the error is

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

Hi all, Can some one tell me the difference between the following two formulas? 1. epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) 2.epiG.rf <-randomForest(gamma~.,data=data, na.action = na.fail,ntree = 300,xtest = NULL, ytest = NULL,replace = T, proximity =F) [[alternative HTML version deleted]]

Greetings R Users! I am posting to inquire about the proximity matrix in the randomForest R-package. I am having difficulty pushing very large data through the algorithm and it appears to hang on the building of the prox matrix. I have read on Dr. Breiman's website that in the original code a choice can be made between using an N x N matrix OR to increase the ability to compute large

Hi, I was reading an article recently about how google ranks results (among many other things of course) based on the proximity of the search terms in the source documents. In addition, the position of the search terms in the search query string itself is also taken into consideration when determining how important each term is. Does Xapian do something similar - at least for the first part?

Hello all, I've been pouring through the various spatial packages, but haven't come across the right thing yet. Given a set of points in 2-d space X, i'm trying to find the subset of points in Y proximate to each point in X. Furthermore, the proximity threshold of each point in X differs (X$threshold). I've constructed this myself already, but it's horrificly slow with a

<posted & mailed> Dear all, I'm trying to solve the problem, of how to find clusters of values in a vector that are closer than a given value. Illustrated this might look as follows: vector <- c(0,0.45,1,2,3,3.25,3.33,3.75,4.1,5,6,6.45,7,7.1,8) When using '0.5' as the proximity requirement, the following groups would result: 0,0.45 3,3.25,3.33,3.75,4.1 6,6.45 7,7.1

Dear all, I am trying to calculate the proximity matrix for a data set with 16 variables and 6804 observations using random forests. I have a Pentium 4, 3.00GHz processor with 1 GB of RAM. When I use the command randomForest(data.scale,proximity=T) I get the warning message Error: cannot allocate vector of size 361675 kb Is this because I have reached the limit of what my computer is

Hi I am trying to use randomForest for classification. I am using this code: > set.seed(71) > rf.model <- randomForest(similarity ~ ., data=set1[1:100,], importance=TRUE, proximity=TRUE) Warning message: The response has five or fewer unique values. Are you sure you want to do regression? in: randomForest.default(m, y, ...) > rf.model Call: randomForest(x = similarity ~ .,

Apologies for any obtuseness in the following. We have been working on Version 2.0 of the randomSurvivalForest CRAN package and we're encountering a perplexing 'memory not mapped' segfault that we believe is "influenced" by GC. We essentially have two R functions, rsf.default(..), and predict.rsf(..) and two corresponding entry points, rsfGrow(...), and rsfPredict(...),

Hello Experts I have the following codes and data for 2 interpolation plots. http://www.nabble.com/file/7206/3d_plot_data.txt 3d_plot_data.txt data<-read.table("3d_plot_data.txt", header=T) attach(data) par(mfrow=c(1,2)) library(akima) interpolation<-interp(rr,veg_r,predict) persp(interpolation,theta = -45, phi = 30, ticktype = "detailed", nticks=4, cex=0.8,

I getting "Error in matrix(0, n, n) : too many elements specified" while building randomForest model, which looks like memory allocation error. Software versions are: randomForest 4.5-25, R version 2.7.1 Dataset is big (~90K rows, ~200 columns), but this is on a big machine ( ~120G RAM) and I call randomForest like this: randomForest(x,y) i.e. in supervised mode and not requesting

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

Hi, I am currently writing code to input a few thousand files, run them through the Random Forests package, and then output corresponding results. When I use the code below: zz<-textConnection("ex.lm.out", "w") sink(zz)

Are there any options to forming queries with Omega? For instance can you specify a proximity search like "file near 5 manager"? If so, where might I find this documented? I don't see it in the docs directory. Thanks Jim.