Re: [R] Unable to install lme4
I am baffled by this as well. I'm having the same issue. Using suse linux, with 64 bit R2.8.1. Thanks, james Zege, Andrew wrote: I am unable to install package lme4, after several attempts to do so using various repository URLs. Just to make sure everything works fine with proxy, connection, etc, I installed ggplot2 and it worked fine. I am using command install.packages(lme4, lib=/myRlibs), optionally using contrib argument with different URLs. Error message the I get is Warning message; In install.packages(lme4, lib=/myRlibs) package 'lme4' is not available Some other details, not sure how relevant are: getOption(repos) returns http://lib.stat.cmu.edu/R/CRAN; I tried setting contrib to various other URL, such as http://cran.mtu.edu/src/contrib; or Berkeley URL, but with no success. Actually, when I did available.packages() on this repos, I didn't see lme4 in the package indices. My machine has x86_64bit RedHat Linux. Would appreciate any tips or directions, Thanks Andre __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- View this message in context: http://www.nabble.com/Unable-to-install--lme4-tp25514856p25697423.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] Unable to install lme4
This is the first time I've encountered R having difficulty with package and R version compatibility. I cant believe no one has fixed generally so that your version of R can get the latest package appropriate to that version. How nice would that be? :) Anyway, I figured it out for my version (2.8.1). I needed to install Matrix package first, which was also outdated. R CMD INSTALL -l lib Matrix_0.999375-22.tar.gz R CMD INSTALL -l lib lme4_0.999375-28.tar.gz it loads now within R. I haven used it much yet. jamesmcc wrote: I am baffled by this as well. I'm having the same issue. Using suse linux, with 64 bit R2.8.1. Thanks, james Zege, Andrew wrote: I am unable to install package lme4, after several attempts to do so using various repository URLs. Just to make sure everything works fine with proxy, connection, etc, I installed ggplot2 and it worked fine. I am using command install.packages(lme4, lib=/myRlibs), optionally using contrib argument with different URLs. Error message the I get is Warning message; In install.packages(lme4, lib=/myRlibs) package 'lme4' is not available Some other details, not sure how relevant are: getOption(repos) returns http://lib.stat.cmu.edu/R/CRAN; I tried setting contrib to various other URL, such as http://cran.mtu.edu/src/contrib; or Berkeley URL, but with no success. Actually, when I did available.packages() on this repos, I didn't see lme4 in the package indices. My machine has x86_64bit RedHat Linux. Would appreciate any tips or directions, Thanks Andre __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- View this message in context: http://www.nabble.com/Unable-to-install--lme4-tp25514856p25703018.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] comparing random forests and classification trees
Greetings tree and forest coders- I'm interested in comparing randomforests and regression tree/ bagging tree models. I'd like to propose a basis for doing this, get feedback, and document this here. I kept it in this thread since that makes sense. In this case I think it's appropriate to compare the R^2 values as one basic measure. I'm actually going to compare mean error (ME), mean absolute error (MAE), root mean squared error (RMSE) as well. This means that I need estimates from each approach so that I can form residuals. **As I see it, the important details are in how to set up the models so that I have comparable estimates, particularly in how the trees/forests are trained and evaluated.** For regression/bagging trees, the typical approach for my application is 100 runs of 10-fold CV. In each run all the values are estimated in an out-of-the-bag sense; each fold is estimated while it is withheld from fitting, thus fit is not inflated. The estimates are then averaged over the 100 runs at each point to get an average simulation and this is used to calculate residuals and the measures mentioned above. Somewhat more specifically, the steps are: I fit a model, I prune it via inspection, I loop 100 times on xpred.rpart(model,xval=10,cp=cp at bottom of cptable from pruned fit) to generate the 100 runs (bagging is thus performed while holding the cp criteria fixed?), I average these pointwise, I calculate the desired stats/quantities for comparison to other models. For randomForests, I would want to fit the model in a similar way, ie 100 runs of 10-fold CV. I think the 10-fold part is clear, the 100 runs, maybe less so. To get 10-fold OOB estimates, I set replace=FALSE, sampsize=.9*nrow(x). Then I get a randomForest with $predicted being the average OOB estimates over all trees for which each point was OOB. I would assume that each tree is constructed with a different 10-fold partitioning of the data set. Thus the number of runs is really more like the number of trees constructed. If i wanted to be really thorough, I could fit 100 random forests and get the $predicted for each and then average these pointwise. But that seems like over kill; isnt that the lesson of plot.randomForest that as the # of trees goes up the error converges to some limit. (from what i've seen). Thus, my primary concern is in the amount of data used for training and cross validating the model in an out-of-bag sense; can i meaningfully compare 10-fold oob estimates sing xpred.rpart to a random forest fit using 90% of the data as sampsize? Of secondary concern is the number of bagging trees versus then number of trees in the random forest. As long as the average estimate error is nearing some limit with the number of bagging trees I'm using, I think this is all that matters. So this is more of methodological difference to be retained, similar to differences in pruning under bagging and random forests, though I should probably specify the node sizes to be similar for each. Am I overlooking anything of grave consequence? Any and all thoughts are welcome. If you are aware of any comparisons of rpart and randomForests in the literature for any field (for regression) of which I am ignorant, I would appreciate the tip. I have read over Newer Classification and Regression Tree Techniques: Bagging and Random Forests for Ecological Prediction by Prasad, Iverson, and Liaw. I may have missed it, but I did not see discussion of maintaining consistency in the way the models were trained, though it is a very nice paper overall and contained many interesting approaches and points. Thanks in advance, James -- View this message in context: http://www.nabble.com/-R--comparing-random-forests-and-classification-trees-tp8682315p25491934.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
[R] ipred bagging segfault on 64 bit linux build
I wanted to report this issue here so others may not find themselves alone and as the author is apparently active on the list. I havent done an exhaustive test by any means, cause I dont have time. But here's a small example. Apparently the ns argument is the one that is killing it. I've gotten several different segfault messages, the only other one I remember said out of memory. This one is probably most common from the about 10 segfaults I've had. *** caught segfault *** address(nil), cause 'unknown' I'm working on a 64bit build of R 2.8.1 on a linux machine. If you want more details, I can surely get them. It happens on the last line for the following for all different valies of ns: library(rpart) library(ipred) data(Forbes2000, package=HSAUR) Forbes2000 - subset(Forbes2000, !is.na(profits)) datasize=length(Forbes2000$profits) f - rpart(profits ~ assets + marketvalue + sales, data=Forbes2000) fb - bagging(profits ~ assets + marketvalue + sales, data=Forbes2000) fb - bagging(profits ~ assets + marketvalue + sales, data=Forbes2000, nbagg=100,coob=TRUE) fb - bagging(profits ~ assets + marketvalue + sales, data=Forbes2000, nbagg=100,coob=TRUE, ns=round(.9*datasize)) -- View this message in context: http://www.nabble.com/ipred-bagging-segfault-on-64-bit-linux-build-tp25407509p25407509.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] rpart - the xval argument in rpart.control and in xpred.rpart
I have this *exact* same confusion. Adding to this is the fact that Everitt and Hothorn in their book, HSAUR, say that setting xval=100 gives 100 runs of 10-fold cross-validation (1st ed., page 136). Is this actually 1 run of 100-fold cross-validation? For large xval, doing multiple cross-validations is not super important. But I would want to perform multiple cross-validataion with different partitions of the data when xval is moderate or small wrt the size of the data set. In that case do we need to do as Paolo suggests? Paolo Radaelli wrote: Usually 10-fold cross validation is performed more than once to get an estimate of the misclassification rate thus I thought number of cross-validations was different from the number of cross-validation groups. So, if I want to perform 10-fold cross-validation more than once (say 5) in order to estimate the miscalssification rate I have to run xpred.rpart 5 times ? Thanks Paolo 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. It is the same thing. If xval=10 then the data is divided into 10 disjoint groups. A model is fit with group 1 left out and that model is used to predict the observations in group 1; then a model is fit with group 2 left out; then group 3, ... So 10 groups = 10 fits of the model. Actually I thought that in rpart.control Terry Therneau __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. -- View this message in context: http://www.nabble.com/Re%3A-rpart---the-xval-argument-in-rpart.control-and-in-xpred.rpart-tp23942907p25408496.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
Re: [R] goodness of prediction using a model (lm, glm, gam, brt, regression tree .... )
I think it's important to say why you're unhappy with your current measures? Are they not capturing aspects of the data you understand? I typically use several residual measures in conjunction, each has it's benefits/drawbacks. I just throw them all in a table. -- View this message in context: http://www.nabble.com/goodness-of-%22prediction%22-using-a-model-%28lm%2C-glm%2C-gam%2C-brt%2C-regression-tree--%29-tp25270261p25408808.html Sent from the R help mailing list archive at Nabble.com. __ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
