similar to: mutlicollinearity and MM-regression

Dear r-help team: Is a package implemented in R which includes a function that calculates polynomial distributed lag models (also: Almon models, pdl-model)? Provided a pdl function is available, can it be applied to robust statistics like MM-estimators? Thanks in advance! Best regards, Carsten Colombier Dr. Carsten Colombier Economist Group of Economic Advisers Swiss Federal Finance

Dear R-users, Do you know if there is any function in R available which calculates an R^2 for a robust MM-regression? With best regards, Carsten Colombier Dr. Carsten Colombier Economist Group of Economic Advisers Swiss Federal Finance Administration Bundesgasse 3 CH-3003 Bern phone +41 31 322 63 32 fax +41 31 323 08 33 email: carsten.colombier at efv.admin.ch www.efv.admin.ch

is there any existing function for computing condition index? " analysing multivariate data" say that we can use condition index to check multicollinearity.saying that we can get it via SVD. The elements of the diagnoal matrix are the standard deviations of the uncorrelated vectors. the condition index is the ratio of the largest of these numbers to the smallest. so if i have a data

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.

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,

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

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

Hi, could someone help me with dilemma on the simulation of logistic regressiondata with multicollinearity effect and high leverage point.. Thank you [[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 -

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]]

Hello to everyone. I´m writing you because I´m feeling a bit frustrated with my work. My work consists in finding the relation between the amount of fires and the weather, so, my response variable is the amount of fires in a fire season and the explanatory variables are the temperature, the amount of precipitation and the some others…. my problem is this; I keep getting the wrong sign in the

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

Dear all, I have one dependent variable y and two independent variables x1 and x2 which I would like to use to explain y. x1 and x2 are design factors in an experiment and are not correlated with each other. For example assume that: x1 <- rbind(1,1,1,2,2,2,3,3,3) x2 <- rbind(1,2,3,1,2,3,1,2,3) cor(x1,x2) The problem is that I do not only want to analyze the effect of x1 and x2 on y but

Hi I have a set of data with both quantitative and categorical predictors. After scaling of response variable, i looked for multicollinearity (VIF values) among the predictors and removed the predictors who were hinding some of the other significant predictors. I'm curious to know whether the predictors (who are not significant) while doing simple 'lm' will be involved in

I have a linear model y~x1+x2 of some data where the coefficient for x1 is higher than I would have expected from theory (0.7 vs 0.88) I wondered whether this would be an artifact due to x1 and x2 being correlated despite that the variance inflation factor is not too high (1.065): I used perturbation analysis to evaluate collinearity library(perturb)

Dear R community. I´m working with a generalized linear model which the response variable is a categorical one and the predictive variables are weather conditions. I have 250 different places where I need to fit the model. In some of these places I have strong correlations between some of the variables so I need to deal with this problem. I found a work similar than mine where they use tha

Hello to everyone. I'm fitting a glm.nb model to a count data. I'm using about 8 predictive variables. Once a run the script I do get a result but it tells me that the iteration limit has been reached. So, can i trust the results given by the model? Could it be a multicollinearity problem? Thank you for taking the time to help me. Greetings Lucas. -- View this message in context:

Dear All, I am contemplating centering the covariates in my Cox model to reduce multicollinearity between the predictors and the interaction term and to render a more meaningful interpretation of the regression coefficient. Suppose I have two indicator variables, x1 and x2 which represent age categories (x1 is patients less than 16 while x2 is for patients older than 65). If I use the following

Dear Sir, I am using the package pgmm you build in panel regression. However, I found that when T is 10, N=30, the error would show as following: system is computationally singular: reciprocal condition number But the similar code works well on Stata, so I wonder how I can optimize the algorithm, for example , the inverse matrix optimization ? And I have checked my data as well, no