similar to: predict for a model with a subset

I was under the impression that the tree method (e.g. as implemented in rpart) was insensitive to monotonic transformations of the dependent variable. e.g. Breiman Olshen et al. Classification and Regression Trees state "In a standard data structure [a tree] is invariant under all monotone transformations of individual ordered varaibles" (p. 57) However, I get very different results

I have got the change to set op an Asterisk PBX for one department in a large organisation. This means that in instead of connecting the Asterisk direct to the PSTN thought E1 interface (ISDN) I have to connect thought the big PBX central (IS3000).(http://pbcextra.net/commonnet/marcom/pdf/001_06221.pdf) Is there any body who has experience with this? The right size of connection between the

Hello all, I’m pretty new to LLVM. I'm writing a pass for loop optimization. I clone and rearrange loops, setting the cloned loop as the original loop’s parent. This can be done multiple times, until there is no more work to do. The trouble is, after the first time I do this, the cloned loop's SCEVs become unknown types when they should be AddRecExpr. If I re-run the whole pass on the

VorbisHi Any help is appreciate. I have two questions. First, I looked into the vorbis-?-specific and was confused by the floor1 algorithm. I think at last the aim is to derive the piecewise curve with the list X and Y,then when encodering ,why not use the selected point orderly to get the curve ? In other words, the specific use the list of [0,128,64,32,96,16,48,80] , why can not use

Hi there, I have just started using the MASS package in R to run M-estimator robust regressions. The final output appears to only give coefficients, degrees of freedom and t-stats. Does anyone know why R doesn't compute R or R-squared and why doesn't give you any other indices of goodness of fit? Does anyone know how to compute these in R? Sophie -- View this message in context:

Good morning to you all, Sorry for taking your time from your research and teaching schedules.   If you have a non-stationary univariate time Series data that has the transformation: Say; l.dat<-log (series) d.ldat<-diff (l.dat, differences=1) and you fit say arima model. predit.arima<-predict (fit.series, n.ahead=10, xregnew= (n+1) :( n+10)) How could I re-transform

hello, In my time series data, I was able to successfully fit its ARIMA model (Box-Jenkins) and its GARCH model and estimate their parameters. I was also able to forecast future values of the time series based on my fitted ARIMA model using the predict() function call. However, I'm not sure what is the correct function command to call in order to forecast future values of my time series

Hi all, for(pred_iterator PI=pred_begin(BB); PI!=pred_end(BB); PI++) { BasicBlock *Pres = *PI; pred.insert(Pres); } for(predit = pred.begin(); predit!=pred.end();predit++) (BB)->removePredecessor(*predit); Is this code enough to remove the predecessor for each basic block As I read in documentation that this removePredecessor() doesn't update the predecessor list. So,

Hi, I'm planning on a new mail infrastructure which constists of multiple 'frontends' running webmail & public access pop/imap, which would communicate over imap/pop to 'backend mail stores'. My original idea was to run dovecot on the backends, use a predition on the frontends to proxy imap/pop and also run imapproxy in front of webmail [squirrelmail]. I've since

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

New major versions of the caret packages (caret 3.37, caretLSF 1.23 and caretNWS 0.23) have been uploaded to CRAN. caret is a package for building and evaluating a wide variety of predictive models. There are functions for pre-processing, tuning models using resampling, visualizing the results, calculating performance and estimating variable importance. caretNWS and caretLSF are two parallel

Hello all, I am working at the FORTH institute in Crete and it's been a long now that I am trying to reproduce the results of the paper : "Gene expression profiling predits clinical outcome of breast cancer", by Van't Veer et al. It has been published in NATURE, vol 415, 31 January 2002. http://www.nature.com/nature/journal/v415/n6871/full/415530a.html I am facing some

Dear List! I am trying to calculate the distance between original data points and their position in the PLS model. In order to do this, I tried to predict the scores using the predict.mvr function and calculate the corresponding positions in variable space. The prediction of scores works perfectly: ------ data(trees) # build model t<-plsr(Volume~.,data=trees) # predict scores for training

Hello, I?ve got a statistical problem that I hope you can help me with. It doesn?t have to do directly with R, so if there?s another forum which would suit better, please tell me! Now here?s the problem: I want to derive confidence intervals for a variable X, which is - given the descriptive statistics - obviously negatively skewed and leptokurtic (i.e. peaked). My aim is to make a statement