similar to: variance?,...

Hi folks, I was wondering how to run a mixed models approach to analyze a linear regression with a user-defined covariance structure. I have my model y = xa +zb +e and b ~ N (0, C*sigma_square). (and a is a fixed effects) I would like to provide R the C (variance-covariance) matrix I can easily provide an example, but at this point I am first trying to know what is the best package the

Hi this is my first post. I am trying to run a simulation for a computer playing Von Neumann poker and adjusting it's expectation of an opponent's behavior according to how the opponent plays. This program involves random generation of "hands" and shifting of parameters. However, when I run the code, no errors come up, but the program doesn't do anything. Could someone

Depending on the setting of par()$usr, the 'at' setting in mtext(.., adj=0, outer=T) may cause the text to appear in an anomalous position (e. g. in the first instance below, at the left of the plot region rather than at 'at=0' in the figure region), or the text may not appear at all. If one does not set the 'at' parameter the text appears (with 'adj=0') on the

Dear R community, I've been writing simple functions for the past year and half and have come across a similar problem several times. The execution of a function within my own function produces NaN's or fails to execute as intended. My conundrum is that I can execute the function outside of my function without error, so it's difficult for me, as a novice functioneer, to figure out

Dear R-help and ROCR developers (Tobias Sing and Oliver Sander) - I think my first question is generic and could apply to many methods, which is why I'm directing this initially to R-help as well as Tobias and Oliver. Question 1. The plot function in ROCR will average your cross validation data if asked. I'd like to use that averaged data to find a "best" cutoff but I

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

I fitted a gaussian mixture to my financial data. The data can be found here: http://uploadeasy.net/upload/32xzq.rar I look at the density with plot(density(dat),col="red",lwd=2) this has a skew of library(e1071) skewness(dat) -0.1284311 Now, I fit a gaussian mixture according to: f(l)=πϕ(l;μ1,σ21)+(1−π)ϕ(l;μ2,σ22) with:

Dear R's I'm trying to use specific rather than random cross-validation groups in mvpart. The man page says: xval Number of cross-validations or vector defining cross-validation groups. And I found this reply to the list by Terry Therneau from 2006 The rpart function allows one to give the cross-validation groups explicitly. So if the number of observations was 10, you could use

Hello, I am in the process of writing a Perl program to carry out and analyze large numbers of regressions using R as the regression engine, and I am using Statistics::R to create the communication bridge between the two programs. Statistics::R creates a pipe between R and Perl and uses Rterm.exe (hidden during run-time) to carry out the R commands. This communication bridge works well for

All, I'm trying again with a slightly more generic version of my first question. I can extract the plotted values from hist(), boxplot(), and even plot.randomForest(). Observe: # get some data dat <- rnorm(100) # grab histogram data hdat <- hist(dat) hdat #provides details of the hist output #grab boxplot data bdat <- boxplot(dat) bdat #provides details of the boxplot

I use ROCR to plot multiple runs' performance. Using the sample code as example: # plot ROC curves for several cross-validation runs (dotted # in grey), overlaid by the vertical average curve and boxplots # showing the vertical spread around the average. data(ROCR.xval) pred <- prediction(ROCR.xval$predictions, ROCR.xval$labels) perf <- performance(pred,"tpr","fpr")

hi! this is a question about lda (MASS) in R on a particular dataset. I'm not a specialist about any of this but: First with the well-known "iris" dataset, I tried using lda to discriminate versicolor from the other to classes and I got approx. 70% of accuracy testing on train set. In iris, versicolor stands "between" the 2 other so one can expect lda not to perform well

Thanks for the tip using a smoothing technique before plotiing in order to get a curve instead of a line connecting the observations. But that's not the solution for my main problem with the "unclean" line plot. In order to show my problem let's take this simple example: > xval <- c(1, 2, 3, 4, 5, 6, 7, 8) > yval <- c(10, 30, 40, 50, 70, 90, 100, 110) > plot

Hi, I am applying the following code to map pca loadings onto a 3d grid, my problem is this - the output only plots the spheres in the requested color (in this case "red") for the first argument. The sphere from the second argument appear as flat dark circles. Also the text3d() command only seems to work for a couple of the positions, with no text added in most cases. Could anyone offer

Dear R-Gurus, I have ended up with a calculation problem where I need to use brobs. I have to work my way through a vector with a for loop to act on each element in a calculation (refering to the previous value in the new vector of results -- so as far as I know I can't use "apply") -- this produces a list of brobs. My problem is, how do I act on, plot this list, or do vector

Hi, I am trying to make a palaeoenvironmental transfer function using the R package rioja that predicts the water-table (measured as depth to the water table) of an area given the testate amoebae that are found there. I've carried out weighted averaging of the data and am trying to produce a graph that shows the observed water-table versues the model's predicted values. Following the

Hi, I am planning on using classification trees to build a predictive model for data which includes a random variable. I intend to use the R functions 'rpart' (and potentially also 'randomForest' and 'bagging'). I have a data set with 390 data points. The response variable is binary. There are a large number of variables (>20, both categorical and continuous). The

Hi Listers I need to calculate and then plot a frequency histogram of the best tree calculated using the 1-se rule. I have included some code that has worked well for me in the past but it was only for selecting the minimum cross-validation error. I include the code for my model, some relevant output and the code for selecting and plotting the frequency histogram of minimum xerror. Here is the

Dear R friends- I'm attempting to generate a regression tree with one gradient predictor and multiple responses, trying to test if change in size (turtle.data$Clength) acts as a single predictor of ten multiple diet taxa abundances (prey.data) Neither rpart or mvpart seem to allow me to do multiple responses. (Or if they can, I'm not using the functions properly.) > library(rpart)

Hi all, I am fairly new to R and I am trying to run mvpart and create a MRT using explanatory variables and covariables. I've been following the procedures in Numerical Ecoogy with R. The command (no covariables) which works fine - ABUNDTMRT <- mvpart(abundance ~ .,factors,margin=0.08,cp=0,xv="1se",xval=nrow(abundance),xvmult=100,which=4) where abundance is 4th root