similar to: how to compute condition index?

Dear R users, Usually the variance-inflation factor, which is based on R^2, is used as a measure for multicollinearity. But, in contrast to OLS regression there is no robust R^2 available for MM-regressions in R. Do you know if an equivalent or an alternative nmeasure of multicollinearity is available for MM-regression in R? With best regards, Carsten Colombier Dr. Carsten Colombier Economist

How about: x <- data.frame(matrix(rnorm(1550),c(50,31))) model <- step(lm(x[,1] ~ as.matrix(x[,2:31]))) --Matt -----Original Message----- From: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]On Behalf Of rongguiwong Sent: Monday, September 20, 2004 20:52 PM To: r-help at stat.math.ethz.ch Subject: [R] how to take this experiment with R? This message uses

Hi all, I've been struggling with trying to specify a diagnoal matrix for linear mixed effects model. I think I've got nearly everything correct, except the following message appears: In lme.formula(fixed = fwave ~ sex + sexXbulbar + visit + age + : Fewer observations than random effects in all level 1 groups Not sure if i've provided enough details, but I'm basically trying

I've posted a sample file for estimating loglinear models for square tables (mobility models) at http://www.xs4all.nl/~jhckx/mcl/R/ Comments and suggestions are welcome. John Hendrickx

A multinomial logit model can be specified as a conditional logit model after restructuring the data. Doing so gives flexibility in imposing restrictions on the dependent variable. One application is to specify a loglinear model for square tables, e.g. quasi-symmetry or quasi-independence, as a multinomial logit model with covariates. Further details on this technique and examples with several

the following the the lower.tri matrix in a file named luxry.car and i want to read it in R as a lower.tri matrix.how can i do? i have try to use help.search("read"),but no result what i want. 1.000 0.591 1.000 0.356 0.350 1.000

I've put two packages for R on my home page at http://www.xs4all.nl/~jhckx/R/. The "pcnt" package is for multiway percentage tables. I've posted a first effort called "ctab" on this group and a request for enhancing "ftable" with percentages on the wishlist. The "mcl" package is for estimating multinomial logistic models using conditional logistic

i want to study R programming by studying the existing code from R itself,but i don't know how to read the code,can any one give me some guide? my R is installed in /usr/lib/R/ [ronggui at mylinux ronggui]$ /usr/lib/R/ afm bin doc etc include library modules share > version _ platform i586-mandrake-linux-gnu arch i586 os linux-gnu system i586,

Dear R users, I have a problem with multicollinearity in mixed models and I am using lmer in package lme4. From previous mailing list, I learn of a reply "http://www.mail-archive.com/r-help at stat.math.ethz.ch/msg38537.html" which states that if not for interpretation but just for prediction, multicollinearity does not matter much. However, I am using mixed model to interpret something,

Dear Users, I try to calculate condition indexes and variance decomposition proportions in order to test for collinearity using colldiag() in perturb package, I got a large index and two variables with large variance decomposition proportions,but one of them is constant item.I also checked the VIF for that variable, the value is small.The result is as follows: Index intercept V1

I am working on a nlme model that has multiple fixed effects (linear and nonlinear) with a nonlinear (asymptotic) random effect. asymporig<-function(x,th1,th2)th1*(1-exp(-exp(th2)*x)) asymporigb<-function(x,th1b,th2b)th1b*(1-exp(-exp(th2b)*x)) mod.vol.nlme<-nlme(fa20~(ah*habdiv+ads*ds+ads2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b),

Hi, after starting Emacs/ESS/R environment I tried to launch "edit" or "fix". This normally should fire up the $editor, isn't it. Instead of this I regularily I run into an error that there something wrong with $editor. ----------------------------------------------- > op <- options(); str(op) Amongst many entries you'll find this: $ editor

I''m running brglm with binomial loguistic regression. The perhaps multicollinearity-related feature(s) are: (1) the k IVs are all binary categorical, coded as 0 or 1; (2) each row of the IVs contains exactly C (< k) 1''s; (3) k IVs, there are n * k unique rows; (4) when brglm is run, at least 1 IV is reported as involving a singularity. I''ve tried recoding the n

Dear everyone, I'm student of Masters in Statistics (Actuarial) from Central University of Rajasthan, India. I am doing a major project work as a part of the degree. My major project deals with fitting a glm model for the data of car insurance. I'm facing the problem of multicollinearity for this data which is visible by the plotting of data. But I'm not able to test it. In the case

I have been making simple functions to display regressions in a new package called "rockchalk". For 3d illustrations, my functions use persp, and I've grown to like working with it. As an example of the kind of things I like to do, you might consult my lecture on multicollinearity, which is by far the most detailed illustration I've prepared.

Hi, I'm trying to fit regression model, but there is something wrong with it. The dataset contains 85 observations for 85 students.Those observations are counts of several actions, and dependent variable is final score. More precisely, I have 5 IV and one DV. I'm trying to build regression model to check whether those variables can predict the final score. I'm attaching output of

Hi I need a bit of guidance on tests and methods to look for multicollinearity and Endogeniety while using plspm Pl help ------------------ T&R ... Deva [[alternative HTML version deleted]]

Dear all,  I hope this is the right place to ask this question. I am reviewing a research where the analyst(s) are using a linear regression model. The dependent variable (DV) is a continuous measure. The independent variables (IVs) are a mixture of linear and categorical variables. The author investigates whether performance (DV - continuous linear) is a function of age (continuous IV1 -

In searching the R help archives I find a number of postings in April of 2005, but nothing since then. If readers are aware of more recent contributions addressing the problems arising from multicollinearity (such as with the bootstrap, jackknife, or other techniques) I would appreciate a reference. Thank you, Ben Fairbank [[alternative HTML version deleted]]

Dear all,  I hope this is the right place to ask this question. I am reviewing a research where the analyst(s) are using a linear regression model. The dependent variable (DV) is a continuous measure. The independent variables (IVs) are a mixture of linear and categorical variables. The author investigates whether performance (DV - continuous linear) is a function of age (continuous IV1 -