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 to calculate variance inflation factor (VIF) for a GLMER?

I've never worked with GLMER models before and I was wondering if calculating VIF is in any way different than in simpler regressions. In R, for instance, I usually use the ...
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18 views

Should the intercept be included when you check the condition index?

Many sources state that a condition index >30 constitutes a multicollinearity problem. When I've tried to implement this check in practice, I've realized that the condition index (and VIFs) change ...
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79 views

Why aren't we simply using $R_j^2$ instead the VIF?

After all we calculate the VIF by $1/(1-R_j^2)$. A VIF of $5$ corresponds to an $R_J^2$ of $0.8$. To me, the information given by $R_j^2$ just becomes more obscure when I apply the VIF formula. Why ...
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35 views

How to interpret differences in VIF and condition number?

In my present data, the Variance Inflation Factors suggest lack of substential multicolliniearity (<1,7). However, the condition number of 28 is almost at the critical value of 30. How do I ...
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19 views

Do I have multicollinearity? [duplicate]

I am examining the impact of 7 IVs on one DV using regression anaysis. Some of the IVs are significantly correlated with each other, which is consistent with theory. While the single OLS regression ...
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36 views

Vif and stepwise regression

I used the VIF to detect multicollinearity, I want to use forward selection and backward elimination procedures. My question is: Do I have to use all the variables in my dataset in the procedures or ...
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1answer
55 views

Is it appropriate to test for collinearity in a mixed model using VIF?

My study is examining predictors of skin lesions in pigs. I am looking at the effect of predictor variables (including weight at 4 weeks, 9 weeks and 20 weeks) and I have carried out a mixed model ...
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1answer
54 views

Regression — multicolinearity and VIFs

I understand that variance inflation factors can be used to detect multicollinearity. What is the intuition behind the VIF formulation? What aspect of this formula shows it detects multicollinearity ...
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21 views

Selecting Model Based On Data Set

I am doing a project for class in which we choose a data set and use SAS to analyze potential models to predict the response variable. I ran an preliminary proc reg in SAS to determine which variables ...
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57 views

Calculate VIF using standard deviation in binary logistic regression

I want to test my binary logistic regression model for multicollenearity. Hence, i want to calculate Variance Inflation Factor (VIF). But i'm confused with R-squared value which is needed to ...
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32 views

Not VIF but GVIF threshold criteria for factorial (2 to 4 levels) and continuous variables?

I would like to select between factorial and continuous variables that may be correlated for inclusion in a linear model. For the selection of variables I am using the "GVIF", however, how can I set ...
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24 views

How to show the equality of two Variance Inflation Factor(VIF) definitions

How to show the diagonal elements of the ${C = (X'X)^{-1}}$ are $C_{jj} = \frac{1}{1 - R_{j}^2}$ where $R_{j}^2$ is the coefficient of multiple determinations from the regression of $x_j$ on the ...
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1answer
82 views

Identical VIF/Tolerance scores - Multiple regression

To begin - I am extremely bad at stats. I am running a multiple regression analysis on a dataset to predict one variable from two others. There were three independent variables(IV) before, although I ...
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176 views

Principal Components Analysis Regression VIF Interpretation in Minitab?

I'm trying to verify my understanding of how to apply principal component analysis to a multiple regression. Here's my current process and understanding using Minitab: Part 1: I already have my data ...
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3answers
443 views

What are the merits of different approaches to detecting collinearity?

I want to detect whether collinearity is a problem in my OLS regression. I understand that variance inflation factors and the condition index are two commonly used measures, but am finding it ...
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2answers
94 views

How to interpret a VIF of 4?

I am doing a multiple regression, trying to test the extent to which personal income changes and Presidential popularity can predict election results. I have a small sample size, unfortunately, as ...
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1answer
110 views

Detect multicollinearity in maximum likelihood scenarios

I'm estimating a binary logit discrete choice model with BIOGEME and want to check for multicollinearity of my predictors. BIOGEME uses maximum likelihood estimation (MLE) and not ordinary least ...
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1answer
216 views

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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36 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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1answer
59 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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102 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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54 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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2answers
486 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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1answer
79 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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1answer
77 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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1answer
135 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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1answer
176 views

VIF Values for Multicollinearity

I have a cross-sectional dataset 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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1answer
327 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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131 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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2k 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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84 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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268 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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71 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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2k 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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1answer
312 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
3k 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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1answer
501 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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163 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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4k views

How to interpret the variance inflation factors? [duplicate]

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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1answer
213 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 ...
3
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103 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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432 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, ...
10
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7k 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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204 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
1k 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
3k 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 ...
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1answer
3k 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 ...
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1answer
3k 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
3k 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
279 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). ...