Variance Inflation Factor (VIF) quantifies the severity of multicollinearity in an ordinary least squares regression analysis. It provides an index that measures how much the variance (the square of the estimate's standard deviation) of an estimated regression coefficient is increased because of ...

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36 views

Very low VIF values, but extreme high condition index

In my multiple linear regression model, all of my explanatory variables have a VIF score, lower then 3, but the highest condition index is 709. The constant and one of the explanatory variables have ...
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19 views

Multicolinearity: conflicting Spearman rank values and VIF scores

I was wondering if you could provide some advice/feedback on a problem I have been having with detecting multi-colinearity. First, the variables which I am using are not normally distributed, hence I ...
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0answers
31 views

In plain language, why is there no VIF for binary outcome regression models?

As far as I know, the variance inflation factor is not computed with pseudo-$R^{2}$ or generalized $R^{2}$ in binary outcome models (e.g. logistic regression). Are there other measures of ...
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233 views

VIF in GLM model in R

Before running or building a model, ho can we check on the multicollinearity between different covariates in GLM model in R? I know that SAS Proc MIXED procedure gives a column for VIF which is very ...
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19 views

Questions about $R^2$, VIFs and very non normal input variables

I have been working with a small part of my dataset trying to eliminate variables and do some micro models. When analysing my micro set I initially found a few high correlations with inputs (0.95 ...
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1answer
106 views

VIF calculation in regression

I want to use VIF to check the multicollinearity between some ordinal variables and continuous variables. When I put one variable as dependent and the other as independent, the regression gives one ...
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3answers
437 views

Is centering a valid solution for multicollinearity?

Let's assume that $y = a + a_1x_1 + a_2x_2 + a_3x_3 + e$ where $x_1$ and $x_2$ both are indexes both range from $0-10$ where $0$ is the minimum and $10$ is the maximum. I found by applying VIF, CI and ...
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126 views

Variance inflation factor for generalized additive models

In the usual VIF calculation for a linear regression, each independent/explanatory variable $X_j$ is treated as the dependent variable in an ordinary least squares regression. i.e. $$ X_j = \beta_0 + ...
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70 views

Weeding out multi-collinear categorical variables

I have a huge amount of features that are categorical variables and I'm trying to find a system for weeding out categorical variables that are close to being multicollinear. Is vif a reasonable ...
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629 views

How to interpret the variance inflation factors?

I have run a Poisson glm and I want to test for multicollinearity in my data. I have used the vif in R and obtained the following result. How can I interpret this? ...
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71 views

calculate VIF for glmm

If we have colinearity among our axplanatory variables we can calculate a varianvce inflation factor to estimate the effects on our standard errors $$ VIF = 1/(1-R^2) $$ I am unsure how to calcuate ...
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1answer
104 views

Testing for multicollinearity in logistic regression

So far I have checked the tolerance value, VIF and condition indexes. But checking the variance of the regression coefficients I have to wonder: how little variance of the regression coefficient ...
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27 views

PCA, non positive definitive & potential cures?

I am trying to use PCA to remove the correlation in my dataset My correlation matrix is being reported as non positive definitive As I understand it a NPD report means that my transposed matrix does ...
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0answers
66 views

Question about variance inflation factors

I'm considering the regression model $y_i = \beta_0 + \beta_1x_{1i} + \beta_2x_{2i} + \varepsilon_i$ where the $\varepsilon_i$ are iid and $\mathcal N(0,\sigma^2)$ A study question asks to show the ...
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0answers
151 views

Adjust p-values for variance inflation factors

I'm fitting linear mixed models and I have slightly inflated p-values due to multicollinearity. I deleted the factors with the highest VIFs until none of them was larger than 3. The VIFs tell me, ...
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2answers
748 views

Which variance inflation factor should I be using: $\text{GVIF}$ or $\text{GVIF}^{1/(2\cdot\text{df})}$?

I'm trying to interpret variance inflation factors using the vif function in the R package car. The function prints both a ...
2
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0answers
96 views

What to do if a covariate is collinear in one model but not in another

I have 10 different species with presence/absences data, as well as 6 different covariates relating to the design of marinas, including 3 continuous (lengths of walls, pontoons and groynes) and 3 ...
3
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2answers
464 views

Is a log transformation of predictors a suitable way of dealing with multicollinearity in multiple regression?

Suppose two independent variables in the linear regression initially have very high correlation of 0.95. This introduces severe multicollinearity into the model (as indicated by very high variance ...
2
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1answer
1k views

Difference between Variance Inflation Factor (VIF) and kappa in R?

I am running a regression analyis in r: fit <- lm(Cost ~ Slope + YardDist, data = test) I want to test the two independent variables for multicollinearity. I ...
11
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1answer
2k views

Collinearity diagnostics problematic only when the interaction term is included

I've run a regression on U.S. counties, and am checking for collinearity in my 'independent' variables. Belsley, Kuh, and Welsch's Regression Diagnostics suggests looking at the Condition Index and ...
2
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1answer
973 views

How to solve collinearity problems in OLS regression?

Please see the model below (link to bigger image). The independent variables are properties of 2500 companies from 32 countries, trying to explain companies' CSR (corporate social responsibility) ...
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1answer
1k views

VIF values and interactions in multiple regression

I am running a multiple regression of the form y~a+b+c+ab+ac+bc I have checked the VIF values for the direct effects - should I check them for the interactions? I am assuming not as that would ...
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1answer
218 views

colinearity between variables

I am running a multiple regression of Y~a+b+c+d etc... I want to do a quick check to see whether my different explanatory variables are colinear (they're a mix of categorical and continuous). ...
4
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1answer
364 views

At what VIF level should you switch from OLS to ridge-regression?

Regarding multicollinearity, is it recommended to use ridge-regression if you have some covariates with VIF values around 10 in the OLS model? What would be the best VIF level to use to decide ...
11
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6answers
3k views

Multicollinearity when individual regressions are significant, but VIFs are low

I have 6 variables ($x_{1}...x_{6}$) that I am using to predict $y$. When performing my data analysis, I first tried a multiple linear regression. From this, only two variables were significant. ...
4
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1answer
361 views

VIF values in regression

Do high VIF values for a a particular variable $x$ just indicate that it is highly correlated with at least one of the other variables in the model? Does it specify which variables and how many ...
9
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2answers
7k views

VIF, condition Index and eigenvalues

I am currently assessing multicollinearity in my datasets. What threshold values of VIF and condition index below/above suggest a problem? VIF: I have heard that VIF $\geq 10$ is a problem. After ...