similar to: Methods of addressing multicollinearity in multiple linear regression with R

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),

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

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 R-experts, I want to use the jackknife function from the bootstrap package onto a linear model. I can't figure out how to do that. The manual says the following: # To jackknife functions of more complex data structures, # write theta so that its argument x # is the set of observation numbers # and simply pass as data to jackknife the vector 1,2,..n. # For example, to jackknife #

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]]

Does R provide a function analogous to LS() or str() that reports the storage space, on disk or in memory, required by objects? Ben Fairbank

Hello R users -- If I have a dataframe such as the following, named "frame" with the columns intended to be named col1 through col6, > frame col1 col2 cmlo3 col4 col5 col6 [1,] 3 10 2 6 5 7 [2,] 6 8 4 10 7 1 [3,] 7 5 1 3 1 8 [4,] 10 6 5 4 9 2 and I want to correct or otherwise change the

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

I've changed the heading because this really is another thread. I think it inevitable that there will, in the course of time, be other lists that are devoted, in some shape or form, to the concerns of practitioners (at all levels) who are using R. One development I'd not like to see is fracture along application area lines, allowing those who are comfortable in coteries whose

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

Hello, I am trying to obtain the partial r-square values (r^2 or R2) for individual predictors of an outcome variable in multiple linear regression. I am using the 'lm' function to calculate the beta coefficients, however, I would like to know the individual % contributions of several indepenent variables. I tried searching for this function in many R packages, but it has proven elusive

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 -

I bogged down about half way through reading the Green Book, in part because it became increasingly difficult to understand how some of the ideas related to R, as opposed to S (which I have not used). Does any reader know whether there is a document that points out differences between S and R that would be helpful in reading the Green Book? Ideally, perhaps, I need a "crib sheet" to

When preparing a series of histograms I found that hist was combining the two lowest categories or bins, 1 and 2. Specifying breaks, as illustrated below, resulted in the correct histogram: values <- sample(10,500,replace=TRUE) hist(values) hist(values,breaks = 0:10) Apparently, the number of values strictly less than 1 is shown in the first bin (and since none is less than 1,

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 -

I am finding complex analyses easier than some elementary operations in R. In particular I want to do some low level exploratory data analyses with data in a data frame but cannot find commands to easily insert, remove (delete), rename, and re-order (arbitrarily, not sort) columns. I see that the micEcon package has an insertCol command, but that is for matrices, not data frames. I have looked

Insightful are pleased to announce we are now taking bookings for our latest course on "Bootstrap methods and permutation tests" in the UK on 23rd - 24th October. This course will focus in particular on two resampling methods, bootstrapping and permutation tests, which have been applied successfully to areas of statistical modelling where "traditional" standard errors,

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

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