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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How we can select suitable Variance Influence Factor (VIF) critical value to detect collinearity?

In Variance Influence Factor(VIF) we should use a critical value. A rule of for this value is 10. Is this a good value for detecting collinear based one ...
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22 views

Matrix singularity catch-22 in regression analysis

I'm trying to run a multiple regression, but I'm getting a singular matrix exception. I assume this means that there is high collinearity among my independent variables, so for each X I produce its ...
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29 views

Multicollinearity to be assessed item wise or construct wise

In order to check multicollinearity, do I have to combine items for the same construct and assess the correlation table for the constructs, OR do I have to check the item wise correlation table?
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22 views

Logistic Regression - VIF produces NaN

In my logistic regression model, I tried to explain the Decision in the function of some variables such as: Gender, Occupation, Age, BMI, ... When I do it with the sample of 1500 individuals, it works ...
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27 views

VIF around 3.5 in two covariates, how shall I deal the problem?

in my multinomial logistic regression model (sample size n=290) I am adjusting the results for a group of covariates (n=8). I tested them for multicollinearity and if most of them have a VIF lower ...
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Use of VIF to eliminate redundant explanatory variables used in separate models

I'm currently conducting a study examining the effect of early reproductive success (ERS) in female mountain goats on longevity. I have several definitions of ERS that will be used in separate ...
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90 views

What is the difference between VIF and stepwise regression?

What is the difference between the variance inflation factor (VIF) and stepwise regression as both help in detecting multicollinearity? What variables are different while running both techniques?
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39 views

VIFs and condition indexes give different answers about multicollinearity

For a multiple regression model, all the variables have p-values below 0.05. The p value for the whole model is below 0.05 as well. When I checked for multicollinearity, I got VIFs below 5 for all the ...
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35 views

Variance Inflation Factor and Condition Indeces

My data is cross-sectional macroeconomics data. I have six independent variables (x1,x2,x3,x4,x5,x6) plus 2 dummies (d1,d2) plus 2 interactions terms (d1*x1,d2*x1).I am testing my data for ...
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52 views

Multicollinearity Using VIF and Condition Indeces

I am testing my dataset for multicollinearity using VIF and condition indices(CI).My dataset is cross-sectional macroeconomics data. I have 6 independent variables (x1,x2,x3,x4,x5,x6) plus 2 dummies ...
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34 views

VIF & CI in Regression with Dummies and Interaction Terms

I am checking my dataset for multicollinearity using VIF and condition indices(CI).My dataset is cross-sectional macroeconomics data (n=75). I have six independent variables (x1,x2,x3,x4,x5,x6) plus ...
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59 views

VIF Values for Multicollinearity

I have cross-sectional data which I obtained from panel data. All the variables are on macroeconomics with n=75. I want to check my variables for multicollinearity using VIF. I got the following ...
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92 views

How to interpret VIF?

I am using the vif function in the R package car to test for multicollinearity. I am a little confused at the output given. For example, I have 5 variables (x1, x2, ...
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80 views

Multicollinearity? Non-significant multiple-linear regression, highly correlated regressors but low variance inflation factor

I have a small sample (30 obs) and 4 independent variables, 3 of which are significantly correlated to each other. I have tried to run the simple linear regression on each of them separately and it ...
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204 views

VIF for generalized linear model

Is the variance inflation factor useful for GLM models. Below example shows OLS is showing VIF>5, but GLM lower. GLM shows instability in the coefficients between train and test set. ...
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64 views

high correlation between IVs, but VIF and tolerance normal?

In a multiple regression analysis: If two variables are correlated by more than .8, but the VIF for each of them are 1.005 and 1.006 and the tolerance numbers .994 and .995, what exactly does this ...
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60 views

Variance Inflation Factor less than 1 in ridge regression?

I was trying to determine the biasing constant in ridge regression when I came across a phenonomenon that seems quite puzzling, to me at least. I let the GCV criterion choose a constant for me and ...
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134 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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70 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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51 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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796 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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213 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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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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239 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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113 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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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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152 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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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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264 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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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 ...
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145 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 ...
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782 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 ...
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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 ...
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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 ...
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1answer
2k 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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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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246 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
403 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 ...
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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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398 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 ...
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10k 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 ...