search for: multicollinearity

Dear all, I would like to perform a regression tree analysis on a dataset with multicollinear variables (as climate variables often are). The questions that I am asking are: 1- Is there any particular statistical problem in using multicollinear variables in a regression tree? 2- Multicollinear variables should appear as alternate splits. Would it be more accurate to present these alternate

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 recodi...

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 of linear models one can use VIF but what about count data. Another problem is as the multicollinearity is visible from the plot one solution is poisson ridge regression. But how to do the programmi...

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,...

...2*ds2+at*trout)+asymporig(da.p,th1,th2)+ asymporigb(vol,th1b,th2b), fixed=ah+ads+ads2+at+th1+th2+th1b+th2b~1, random=pdBlocked(list(th1~1,th2~1)), start=c(ah=.5524,ads=.8,ads2=-.1,at=-1,th1=2.542,th2=-7.117,th1b=2,th2b=-7), data=pca1.grouped,verbose=T) I am looking at potential multicollinearity among the fixed effects, in particular I am concerned about multicollinearity between da.p (drainage area) and vol (volume). How do I interpret the correlation reported in the summary command for th1 and th1b, which are the asymptotes for fa20~da.p and fa20~vol. It is -.50, but how is the correla...

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 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 Group of Economic A...

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]]

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. http://pj.freefaculty.org/guides/stat/Regression/Multicollinearity/Multicollinearity-1-lecture.pdf I used persp mainly because I can understand it, and it can be made to work like plot in R, with additional tools like lines and po...

...build regression model to check whether those variables can predict the final score. I'm attaching output of several steps, but I tried to following procedure: - build model with only those two variables - summary shows that non of them is significant predictor of the final outcome. - test for multicollinearity revealed tolerance below 0.2 (potential problem) - build two new models having as a predictor only one of those values - both models show that variable used for the model is significant predictor. Separately they are significant, together not. Probably multicollinearity problem, but... - as I keep...

Dear R users, I'm analysing some data, and I'm using an lme function. I have a problem with choosing the right order for three of my explanatory variables, which shows collinearity. Is there any rules to make the decision?(r.squared?) Or it's better if I choose the order, that I think gives me more information about the data? Say x1 is the variable with the highest r.squared, x3

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)

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]]

...model because they are country level data). If IV4 and IV5 are included in the model, it is possible that the model will not be able to be defined because we only have six countries and it is very likely that the levels of counties (IV3) may be confounding with IV4 and IV5. This also calls for multicollinearity issues, right? I would like to suggest to the analyst to use lmer using the IV3 as a random variable and  IV4 and IV5 as IV at the second level of the two-level model. The questions are: (a) Is it true that IV4 and IV5 cannot be entered in a one-level regression if we also have IV3?, (b) can...

...in the coefficients estimated, I get a negative sign for temperature and a positive sign for precipitation, which is unreasonable, the greater the temperature I would expect more fire, on the contrary, the greater the precipitation I would expect less fires. So far I have deal with overdispersion, multicollinearity and the amount of zeroes through passing from Poisson to Negative Binomial and Hurdle. I believe I have used all my options and still have the wrong signs on my coefficients. Do I have more options? What does it mean that I keep getting those signs? If anyone could help me I would really apprec...

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 interactions. How do i take into account interactions...

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 interactions. How do i take into account interactions...

...analyze the effect of x1 and x2 on y but also of their interaction x1*x2. Evidently this interaction term has a substantial correlation with both x1 and x2: x3 <- x1*x2 cor(x1,x3) cor(x2,x3) I therefore expect that a simple regression of y on x1, x2 and x1*x2 will lead to biased results due to multicollinearity. For example, even when y is completely random and unrelated to x1 and x2, I obtain a substantial R2 for a simple linear model which includes all three variables. This evidently does not make sense: y <- rnorm(9) model <- lm (y ~ x1 + x2 + x1*x2) summary(model) Is there some function within...

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 frame a,containg variables x,y,z. my model is : model<-lm(y~x+z,...

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 followi...