similar to: party for prediction [REPOST]

Hi there I'm experiencing some problems using the party package (specifically mob) for prediction. I have a real scalar y I want to predict from a real valued vector x and an integral vector z. mob seemed the ideal choice from the documentation. The first problem I had was at some nodes in a partitioning tree, the components of x may be extremely highly correlated or effectively constant

Dear useRs: I successfully model-based partitioned several datasets through the use of mob from the party package (thanks Achim et al. once again !!!). At times, however, the partitioning leads to terminal nodes in which the parameter estimates of the model are not significant (although the split points and in general the proposed segmentation both seem reasonable). As I do not seem to be able

I try tu use mob() with my data.frame ('data.frame': 288 obs. of 81 variables; factors, numerics and ordered factors) My response is a binary variable and I should use for modelling a logistic regression (family=binomial). I read in the "MOB" Vignette that I could use a formula like this if I would like to have only partitioning variables apart from the response.

Dear useRs: I try to use mob from the party package (thanks Achim and Co.!) to model based recursive partition a data set. The model is a logistic regression specified with model=glinearModel and family=binomial(). Running mob results in a few warnings of the type: In glm.fit ... algorithm did not converge. As I speculate that this may be due to an insufficient number of iterations I am

Hello: Has anyone tried to model-based recursive partition (using mob from package party; thanks Achim and colleagues) a data set based on a multinomial logit model (using mlogit from package mlogit; thanks Yves)? I attempted to do so, but there are at least two reasons why I could not. First, in mob I am not quite sure that a model of class StatModel exists for mlogit models. Second, as

DeaR all, I am using mob() for model based partitioning, with a dichotomous variable (participant's correct/incorrect response to a test item) regressed onto a continuous predictor related to a given property of the test item. Although this variable is continuous, the value of this variable for many items in this particular analysis is 0. The partitioning criterion is self-reported ability in

Hello, With this program I try to repeat analysis for different years. The results of the analysis are not printed when in the loop, except for the year sequence. What is wrong? Thanks a lot. for (i in 92:99){ cat("\n", "=============================================================\n", "YEAR =",i,"\n",

Dear List, Consider the following example x=c(1,2,3,4,5) y=c(2,4,6,8,10) linearmodel=lm(y~x) To predict a y-value if you know the corresponding x value is very easy with the command predict. predict(linearmodel, newdata=(x=1.5)) The other way around, to predict an x-value with a corresponding y-value, doesn't work unfortunately. Is there another function that can do that, or do I need to

DeaR list, I happily use eigen() to compute the eigenvalues and eigenvectors of a fairly large matrix (200x200, say), but it seems over-killed as its rank is limited to typically 2 or 3. I sort of remember being taught that numerical techniques can find iteratively decreasing eigenvalues and corresponding orthogonal eigenvectors, which would provide a nice alternative (once I have the

Hi R users, I am using the optim funciton to maximize a log likelihood function. My code is as follows: p<-optim(c(-0.2392925,0.4653128,-0.8332286, 0.0657, -0.0031, -0.00245, 3.366, 0.5885, -0.00008, 0.0786,-0.00292,-0.00081, 3.266, -0.3632, -0.000049, 0.1856, 0.00394, -0.00193, -0.889, 0.5379, -0.000063, 0.213, 0.00338, -0.00026, -0.8912, -0.3023, -0.000056), f,

I'm posting this again in the hope that someone can shed some light on the error - I've included logs and as much detail as I can! Chris -------- Original Message -------- I'm using Samba 2.2.8a on Red Hat 8. I was able to set up Samba running as a PDC and have my Windows 2000 client join the domain and authenticate users, map home drives, etc, without any problems. I've now

Dear useRs, Version 0.8-1 of the `party' package will appear on CRAN and its mirrors in due course. This version implements two new tools: o `mob', an object-oriented implementation of a recently suggested algorithm for model-based recursive partitioning (Zeileis, Hothorn, Hornik, 2005) has been added. It works out of the box for partitioning (generalized) linear

Why are my coefficients getting appended with a 1? It borks a match I do later against the original list that doesn't have the random 1 added to the end. > linearModel[[1]] Call: lm(formula = modelSource ~ +UNITBUILD + UNITDB + ITBUILD + ITDB + UATBUILD + UATDB + HOGANCODE + RCF + ReleaseST1 + ReleaseST2 + ReleaseBLA + Small.Bank.Acquisitions + HLY.NewYear + HLY.MLK + HLY.PRES +

I am trying to vectorize the function void bar(float *c, float *a, float *b) { const int width = 256; for (int i = 0 ; i < 256 ; ++i ) { c[ i ] = a[ i ] + b[ i ]; c[ width + i ] = a[ width + i ] + b[ width + i ]; } } using the following commands clang -emit-llvm -S loop.c opt loop.ll -O3 -debug-only=loop-vectorize -S -o - LV: Checking a loop in

Hi, I am writing a piece of code to open a raw image file of a virtual machine which has ubuntu installed in it. The virtual disk is formatted using MBR partitioning method and has 3 primary and 1 extended partition. I want to open up that file and read the block group descriptor table and inode table for each partition. I have written some lines of code and successfully able to read the

Bingo! That works (when coming from C source) Now, I have a serious problem. I am not coming from C but I build the function with the builder. I am also forced to change the signature and load the pointers a,b,c afterwards: define void @bar([8 x i8]* nocapture readonly %arg_ptr) #0 { entrypoint: %0 = bitcast [8 x i8]* %arg_ptr to i32* %1 = load i32* %0, align 4 %2 = getelementptr [8 x

Hello everyone, I am trying to do within subjects repeated measures anova followed by the test of sphericity (sample dataset below). I am able to get either mixed model or linear model anova and TukeyHSD, but have no luck with Repeated-Measures Assuming Sphericity or Separate Sphericity Tests. I am trying to follow example from "car" package, but it seems that I am not getting something

Hi, I am trying to estimate a quantile regression using panel data. I am trying to use the model that is described in Dr. Koenker's article. So I use the code the that is posted in the following link: http://www.econ.uiuc.edu/~roger/research/panel/rq.fit.panel.R How to estimate the panel data quantile regression if the regression contains no constant term? I tried to change the code of

Hi Nadav, Arnold, I managed to find some time to work on the pointer reduction, and I got a patch that can make "canVectorize()" pass. Basically what I do is to teach AddReductionVar() about pointers, saying they don't really have an exit instructions, and that (maybe) the final store is a good candidate (is it?). This makes it recognize the writes and reads, but then

----- Original Message ----- > I am trying to vectorize the function > > void bar(float *c, float *a, float *b) > { > const int width = 256; > for (int i = 0 ; i < 256 ; ++i ) { > c[ i ] = a[ i ] + b[ i ]; > c[ width + i ] = a[ width + i ] + b[ width + i ]; > } > } > > using the following commands > > clang