similar to: How to deal with multicollinearity in mixed models (with lmer)?

Dear R users, I have a problem in plotting 3 dimensional graph using mixed models. My model is sur_prop ~ afr_c+I(afr_c^2)+I(afr_c^3)+byear_c+I(byear_c^2)+I(byear_c^3)+I(byear_c^4)+(1|Studyparish)+afr_c:byear_c +afr_c:I(byear_c^2)+afr_c:I(byear_c^3)+afr_c:I(byear_c^4)+I(afr_c^2):byear_c+I(afr_c^2):I(byear_c^2)+I(afr_c^2):I(byear_c^3)+I(afr_c^2):I(byear_c^4) This is a study on the effect 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 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]]

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),

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 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 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

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, 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 R-help members, Apologies - I am posting on behalf of a colleague, who is a little puzzled as STATA and R seem to be yielding different survival estimates for the same dataset when treating a variable as ordinal. Ordered() is used to represent an ordinal variable) I understand that R's coxph (by default) uses the Efron approximation, whereas STATA uses (by default) the Breslow. but we

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

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

Is it possible to get a register number to which the value is allocated to in MIPS in DAGToDAG class? More Specifically: SDValue Reg3 = Node->getOperand(3); if (RegisterSDNode *R = dyn_cast<RegisterSDNode>(Reg3)) { op3 = cast<RegisterSDNode>(Reg3)->getReg();

> On Feb 27, 2015, at 1:59 AM, Ambuj Agrawal <ambujbwt at gmail.com> wrote: > > Is it possible to get a register number to which the value is allocated to in MIPS in DAGToDAG class? > > More Specifically: > SDValue Reg3 = Node->getOperand(3); > if (RegisterSDNode *R = dyn_cast<RegisterSDNode>(Reg3)) >

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

Thanks for your reply Quentin. I do understand that the registers are allocated much later in the pipeline. I am assuming that the physical registers are allocated before MipsAsmPrinter class. I am doing something like if (MI->getOpcode() == Mips::OPCODE) { unsigned n = MI->getNumOperands(); for(unsigned i=0 ; i < n ; i++) { const MachineOperand &MO =

Dear R-users, I want to fit a linear model with Y as response variable and X a categorical variable (with 4 categories), with the aim of comparing the basal category of X (category=1) with category 4. Unfortunately, there is another categorical variable with 2 categories which interact with x and I have to include it, so my model is s "reg3: Y=x*x3". Using fit.contrast to make the

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