similar to: party for prediction

Apologies for re-posting, my original message seems to have been overlooked by the moderators. ---------- Forwarded message ---------- From: Ed <icelus2k5 at gmail.com> Date: 11 October 2012 19:03 Subject: party for prediction To: R-help at r-project.org Hi there I'm experiencing some problems using the party package (specifically mob) for prediction. I have a real scalar y I want to

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

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

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

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.

I'm working across the statistical literature to find methods for calculating prediction intervals for GLM, BRT (boosted regression tree) models and MARS (multivariate adaptive regression spline) models, but unfortunately my statistical background is too weak to understand most of the stuff I read. I would by satisfied by knowing how to code this in R (and accept the methods as black

Hi there fellow R-users, Can anyone tell me how to build a prediction interval for a gaussian log-link model for the reponse variable?? I can find the standard error of the predictions but I cant seem to find the prediction interval. Is there a way I can calculate the prediction interval from the standard errors?? Here's the example: logX<-rnorm(100)

marked by the genius, the courage, the originality, and the written. Not only is Tradition silent, but even Fable herself does not attempt to tell the story of the origin of her population. Here stand In accordance with these principles, we observe that, while the most tear her away, and then, maddened by her resistance and the sight of the sea and of the land by evaporation, falls again, under

marked by the genius, the courage, the originality, and the written. Not only is Tradition silent, but even Fable herself does not attempt to tell the story of the origin of her population. Here stand In accordance with these principles, we observe that, while the most tear her away, and then, maddened by her resistance and the sight of the sea and of the land by evaporation, falls again, under

Dear list and Martin, I'm testing different approaches to fit an electricity demand time series and come upon the fracdiff package (v 1.3-1) for fitting fractional ARIMA models. The following questions are motivated by this package. 1. Despite having a help page, the residuals and fitted functions don't seem to have implementation, or did i miss something obvious? Alternatively, having a

Hi In order to add several phones to a single extension number, I have replaced entries like: exten => 100,1,Dial(SIP/sipphone100,20,Tr) into: exten => 100,1,Dial(Local/100 at phones,20,Tr) [phones] exten => XXX,1,Dial(SIP.sipphone${EXTEN}) etc This works great. However in the CDR, than seeing one entry for each call, I see several entries in the CDR Worse, if I do something like:

Hi List, Puppet Version: v2.7.13 Question - If I correctly setup multiple manifestdir''s in the puppet.conf file how can I import a node pp file located in a different directory tree to site.pp? My configuration information is below. In my puppet.conf file I have setup an environment called "stable", within this environment I have setup the manifestdir, modulepath and

Good morning, we recently observed a problem with importing S3 generics from a foreign package (without namespace), defining a S3 method in a package _with_ namespace and the `cleanEx()' function which is automatically generated and executed before examples are run by R CMD check. To be more precise. Package `strucchange' defines a S3 generic sctest <- function(x, ...)

I am trying to explore the vpim gem.. Using that i am trying to generate Vcard..Now I am able to generate vcard also i am able to send that vcard through mail. If i try to open that vcard through thunderbird, all the fields getting stored in address book except mobile number, Here ia m send the code for adding mobile number into vcard.. if @bizcard_label.mobile_number!=""

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

I am using the quantreg package to build a quantile regression model and wish to generate confidence intervals for the fitted values. After fitting the model, I have tried running predict() and predict.rq(), but in each case I obtain a vector of the fitted values only. For example: library(quantreg) y<-rnorm(50,10,2) x<-seq(1,50,1)

When I do: > apc <- glm( D ~ ns( Ax, knots=seq(50,80,10), Bo=c(40,90) ) + + ns( Cx, knots=seq(1880,1940,20), Bo=c(1840,1960) ) + + ns( Px, knots=seq(1960,1980,10), Bo=c(1940,2000) ) + + offset( log( Y ) ), + family=poisson ) > pterm <- predict( apc, type="terms" ) > plink <- predict( apc,

Dear R-help, I am trying to estimate a Cox model with nested effects basing on the minimization of the overall AIC; I have two frailties terms, both gamma distributed. There is a error message (theta2 argument misses) and I don?t understand why. I would like to know what I have wrong. Thank you very much for your time. fitM7 <- coxph(Surv(lifespan,censured) ~ south + frailty(id,

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