similar to: Using relaimpo or relimp with PLM and GLS

Hello! I am trying test my observed data against the null-hypothesis that different items from a psychological questionnaire contribute equally to the metric dependent variable that measures problems (sum score of a questionnaire). That is, I am interested in relative strength of the predictors. Predictor items of the questionnaire are on a scale from 0-3, and technically ordinal, although most

I am currently using the relaimpo package to estimate the relative importance of regressors (N= 4000): > m1 <- lm(y ~ x1+x2+x3+x4+x5+, data=data) > calc.relimp(m1, rela=TRUE) > m2=boot.relimp(m1, boot = 500, rela=TRUE, type="lmg") > booteval.relimp(m2) > plot(booteval.relimp(m2)) In a new dataset with 3 measurement points (0,6,12 weeks), I want to perform a similar

Hi, I have a question related to the function "boot.relimp" (package relaimpo). I have a model with numerical predictors and one dummy (class) variable. I would like to calculate the relative importance of the predictors but I have some problems with the dummy variable. The name of the linear model is "lmbroadst.lc" and the dummy variable is "datacon.landcover_gb"

I'm having trouble getting the relaimpo package to use a covariance matrix as input. I'm getting an error message that reads as follows: Error in eval(m$weights, data, parent.frame()) : numeric 'envir' arg not of length one I'm guessing there is something wrong with the structure of my covariance matrix, but it looks fine to me. Pardon my R ignorance if this is an easy

Dear developeRs, with R 2.4.1 (and also 2.4.0), the function str() fails on objects of class relimplmbooteval, if there are unused slots, which is very often the case. I am not sure whether this is a bug in str() or a correct behavior of str() that unmasks some sloppiness in my usage of S4 classes (that I am not aware of)? Reproducible example (package relaimpo needed): The program

Dear All: I am calculating the relative importance of a regressor in a linear model. Does anyone know how I can obtain/install the 'pmvd' computation type? I am a US user. Regards, Y -- View this message in context: http://r.789695.n4.nabble.com/calc-relimp-pmvd-for-US-R-user-tp3808752p3808752.html Sent from the R help mailing list archive at Nabble.com.

Hello, I have estimated the coefficients for my model using the 'pggls' function from the 'plm' package. Now I want to see the relative influence of those X's. How can some please tell me how to standardize those my results in R? Thank you! -- View this message in context: http://r.789695.n4.nabble.com/Standardize-GLS-coefficients-in-R-tp4671371.html Sent from the R help

When using the PLM package (version 1.2-8), I encounter the probem that calling the FGLS estimator evokes strange behavior, when choosing the "random" effects model. After calling the PGGLS function to estimate FGLS, PLM gives me a warning, stating that the "random" model has been replaced with the "pooling" model. I would, however, really like to estimate the random

Hi all, I a semi-beginner with R and I am working with the plm package to examine a longitudinal dataset. Each individual in this dataset has a longitudinal weight for the probability that he or she remains in the sample. Unfortunately, I have not found an argument to use weights in the plm function? I tried ?weights=? like in standard lm or in nlme or lm4 but it does not work. I asked the

I am trying to use Rcmdr 1.5_4 with R-2.11.1 (order to run to run the new version of misdist-0.5.3 which is built under R-2.11.1). however relimp is required for Rcmdr and the version of relimp_1.0.1 downloaded from CRAN will not work with the latest version of Rcmdr (I get error message telling me to reload it). Is there any way round this problem or will there be a new version relimp that is

Hello, Does anyone know if relaimpo only applies to "pure" multiple linear regression models, i.e. - linear in the variables AND linear in the coefficients or is it safe to use it in models that are: - non-linear in the variables BUT linear in the coefficients? Thanks Jyotin [[alternative HTML version deleted]]

Dears, I am specifying a panel model with GLS function and corCAR1 and cor AR1 parameters to correct for serial autocorrelation. But I have one seemingly trivial question: those models fitted that way are random effects models or pooling models? Thanks again, Tomas Notes [[alternative HTML version deleted]]

Hello, I am trying to use the plm package for panel econometrics. I am just trying to get started and load my data. It seems from most of the sample documentation that I need to use the pdata.frame function to get my data loaded. However, even after installing the "plm" package, my R installation cannot find the function. I am trying to follow the example in plmEN.pdf (

Dear All, I'm looking to perform an ANOVA between two nested panel fixed effects models. I tried with anova, as well as with waldtest. anova tells me there is no method available for plm objects, while waldtest tells me my models are not nested. I think they are instead. The difference between the two models is that in the second I let the regression coefficients of a given variable variate

I have installed the latest relaimpo library ( form their website with the 8 functions) When running pvmd as a type in c=type("pmvd") in calc.relimp I get the error message ...could not find function "pmvdcalc" (this is in R version 2.12.1) Can anyone help? Paul Prof P Rheeder School of Health Systems and Public Health Faculty of Health Sciences University of Pretoria Room

Hello, I am switching to R from Stata and I am having particular trouble with the transition from Stata's 'xtabond' and 'ivreg' commands to the "plm" package. I am trying to replicate some of the dynamic panel data work using the UK Employment data in Arellano and Bond (1991) and available as 'EmplUK' under the 'plm' package. I have been

Hello. Just a quick follow-up, for other 'plm' users on the list: - the problem turned out to be logs of zero values hidden in the big dataset - trying log(xx) would not reveal the problem, because log(0)=-Inf is a valid result in log() while it is an invalid input to plm() --> it is always advisable to try lm(yourformula, yourdata) as a first diagnostic check when plm(yourformula,

Dear plm Package users, I use the plm package a lot but I have not updated it for some times. Now I realized the following difficulty with the summary.plm function (demonstrated with the example from the ?plm documentation). library(plm) data("Produc", package="Ecdat") estimation_method<-"within" estimation_effect<-"individual" zz

Dear Sir, Thank you for accepting my request for registration on this site. I am trying to solve panel data problems using plm package , but while suing random effect model i am getting following messege saying " Warning message:In sqrt(sigma2) : NaNs produced " In some other cases i am getting message saying where TSS = NA , that I am not understanding I am sending you my code along

Dear Kailas Gokhale, The negative individual variance is not a problem with your code or plm. It a property of your data. Please check the posts of Giovanni Millo on this topic: [R] R: plm random effect: the estimated variance of the individual effect is negative Millo Giovanni Giovanni_Millo at Generali.com Sat Jan 5 10:10:01 CET 2013 You can find the posts in the archive by rseek.org.