similar to: Multivariate regression tree: problems with surrogate splits

R helpers, I would like to know if it is possible that the last version of R is not giving the surrogate splits when you perform a Multivariate regression tree analysis? I installed the programm in different computers and i run the some matrix and it didn't gave me this information. With a previus version R 2.1.1. I do get the information for the surrogates. Please let me know how to get the

Sub: using glmer to fit a mixed-effects model with gamma-distributed response variable Hello, I'm currently trying to fit a mixed effects model , i.e.: > burnedmodel1.2<-glmer(gpost.f.crwn.length~lg.shigo.av+dbh+leaf.area+ bark.thick.bh+ht.any+ht.alive+(1|site/transect/plot), family=gaussian, na.action=na.omit, data=rws30.BL) If I run this code, I get the error below: Error:

Dear R users, I try to use xyplot() to display two different response variables from the same dataframe per panel, but don't succeed: xyplot(ptot.seaslog ~ vmcwit | seas, data=reeks, as.table=TRUE, panel = function(x,y){ panel.xyplot(x, y, ylim=c(0,1)) panel.superpose(x=reeks$vmcwit, y=reeks$ptotbin, panel.groups = "panel.xyplot",

Dear R, Short verse of the question: Is there R code which will calculate surrogate splits and/or delta impurity for decision trees at each node? Long Version: I have local, legacy code which I use to calculate my decision trees. I would like to switch to R, but as I understand it surrogate splits are not implemented. Surrogate splits and feature ranking are described in Breiman et al

Dear R-help, I would like to compute the variance for the proportion of treatment effect by a surrogate in a survival model (Lin, Fleming, and De Gruttola 1997 in Statistics in Medicine). The paper mentioned that the covariance matrix matches that of the covariance matrix estimator for the marginal hazard modelling of multiple events data (Wei, Lin, and Weissfeld 1989 JASA), and is implemented

Dear R-help, I'm applying the surrogate Poisson glm, by following Venables & Ripley (7.3 pp238-42). >overall_cbind(expand.grid(treatment=c("Pema","control"),age=c("young","adult","old"),repair=c("excellent","good","poor")),Fr=c(8,0,7,1,2,0,2,7,1,4,7,1, 0,3,2,5,1,9))

For ordered factor the natural contrast coding would be to parametrize by the succsessive differences between levels, which does not assume equal spacing of factor levels as does the polynomial contrasts (implicitly at least). This requires the contr.cum, which could be: contr.cum <- function (n, contrasts = TRUE) { if (is.numeric(n) && length(n) == 1) levs <- 1:n

Apologies for continuing to ask about this but . . in my quest to fit a gamma GLMM model to my data (see partial copy of thread below), I'm exploring using hglm today. The question of the day has to do with the errors I'm currently getting from the hglm package. Can hglm handle a model with nested random effects? I don't see an example of one of those in the package documentation. If

Addendum: I tried a gamma fit in glmmPQL and got the same errors. *Ben Caldwell* PhD Candidate University of California, Berkeley On Tue, May 17, 2011 at 3:51 PM, Benjamin Caldwell <btcaldwell@berkeley.edu>wrote: > Hello > After seeing this ( > https://stat.ethz.ch/pipermail/r-sig-mixed-models/2011q1/005213.html) email > I thought I would check the issue with a gamma family

Hello, I am trying to draw the biplot of a PCA, but I would like to use another variable of my initial data frame than my "row.names" to assign labels to the points. A part of my R file follows ; "m" is my initial data frame, which has the variable "code" as row.names, and another variable "name" that I'd like to use as labels for the points. >

I have a question on generating multivariate time series surrogates using the "surrogates" function in the RTisean library. The surrogate data matrices are always much shorter than the input matrices. FYI, I'm using R version 2.12.2 on Windows XP RTisean library v 3.0.14 Tisean algorithms v 3.0.13 Creating a surrogate univariate time series returns a time series with the

I've gone over the data and do not see my error; the dput() output of the data frame and the pdf output of cenboxplot() are attached. The command used: cenboxplot(sb.t$quant, sb.t$ceneq1, range=1.5, main='Total Recoverable Antimony', xlab='Pre-Mining Era', ylab='Concentration (log mg/L)') (on a single line in emacs). The RL on the plot is drawn at 0.01 rather

I've searched the Web with Google and do not find what might cause this particular error from an invocation of cenboxplot: cenboxplot(cu.t$quant, cu.t$ceneq1, cu.t$era, range=1.5, main='Total Recoverable Copper', ylab='Concentration (mg/L)', xlab='Time Period') Error in if (n > 0) (1L:n - a)/(n + 1 - 2 * a) else numeric() : argument is of length zero I do