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Questions tagged [vif]

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 collinearity.

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342 views

Variance Inflation Factor for Meta-Analyses

I've conducted a Meta-Regression Analysis for my Masters Thesis. However, my supervisor told me that I should check if the proposed moderating variables highly correlate. So I thought, that I can ...
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1answer
2k views

High correlation among two variables but VIFs do not indicate collinearity

What would you go for in assessing collinearity - correlation or VIFs? Say you run pairplots and calculate Pearson correlation coefficients between pairs of explanatory variables. Two of them have a ...
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1answer
284 views

Variance Inflation Factors are incredibly high for t1 - can I use this model

I am building a model to explain performance in one time period to another. I have my variables and then I have an interaction effect for those variables in the second time period. Using this data,...
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263 views

Variance Inflation Factors (VIFs) on model vs covariates themselves

I am confused on which type of "object" do the VIF functions operate. Let me give two examples, which are confusing me. The VIFs from the car and ...
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0answers
153 views

VIF and coefficient variance-covariance matrix for OLS

After reading this answer, I am not very clear on how to get to the formula of VIF ($1/1-R^2$) as part of the variances on the diagonals of the variance-covariance matrix of the coefficients, if auto ...
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250 views

Why VIF is calculated on every value of a predictor in R?

I'm currently working on a logistic regression model and I wanted to pinpoint the VIF for every predictor inside the model. I've found that packages 'car' and 'HH' have the same vif function, so I ...
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182 views

Variance inflation factor

I am trying out VIF in order to reduce the number of variables, by removing highly collinear variables. I will be using VIF in R, but before applying it, does my time series need to be stationary?I ...
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0answers
328 views

feature seletion and multi-collinearity & vif

I want to use R to select a variable. In particular, variables are selected considering the correlation, and variables with large multicollinearity, which are correlations between variables, should be ...
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1answer
1k views

What to do when ANOVA is significant, but regressors and VIF are not?

My question is a bit long, with 2 major parts. Here are the variables: Number of cells (C): main dependent variable Disease severity 1 (D1): continuous Disease severity 2 (D2): continuous but only ...
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1answer
1k views

Equation for the variance inflation factors

Following a question asked earlier, the variance inflation factors (VIFs) can be expressed as $$ \textrm{VIF}_j = \frac{\textrm{Var}(\hat{b}_j)}{\sigma^2} = [\mathbf{w}_j^{\prime} \mathbf{w}_j - \...
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1answer
160 views

Exploratory data analysis. What features are important?

I am trying to fit the number of crimes in a city with some enviromental variables (aka my features). I'm using a Poisson/Negative Binomial model since I have count data. The problems are: selecting ...
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1k views

Multicollinearity: How to convert GVIF^(1/(2df)) values to VIF

I am using the GVIF^(1/(2df)) method in my analyses to check for multicollinearity of my (mainly) categorical variables. However, I am struggling with the cut-off values. For the 'regular' VIF several ...
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1answer
299 views

Multicollinearity with highly safe t-statistics but VIF of 13

If all of my coefficients in my logsitic model have really perfect t-statistics that all show sufficiently high significance but have two coefficients that have high VIF like 13-14 with sample size of ...
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1answer
284 views

Is multicolinearity problem ignorable under this situation?

When I run the logistic regression, two independent variables have VIF values greater than 10 like 13 or so. Logistic regression is the one I will use to measure the overall change in the dependent ...
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2answers
275 views

Multicolinearity Test for Multiple Multivariate Regression

I have multiple independent variables and multiple dependent variables, some categorical and some quantitative. I have created a data sheet with dummy columns appropriate to each categorical variable. ...
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0answers
278 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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130 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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348 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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0answers
43 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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1answer
1k 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
190 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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0answers
29 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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1answer
4k views

How can we calculate the variance inflation factor for a categorical predictor variable when examining multicollinearity in a linear regression model?

For example, if we have the linear regression model: $$E(y) = \beta_0 + \beta_1 x_1 + \beta_2 x_2 + \beta_3 x_3 $$ where $ x_1 =\begin{cases} 1 & \mbox{if level 2} \\ 0 & \mbox{otherwise} \...
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1answer
1k views

VIF to find multicollinearity

I tried VIF on the Longley dataset to look for multicollinearity. (I have used a custom function returned in https://beckmw.wordpress.com/2013/02/05/collinearity-and-stepwise-vif-selection/comment-...
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56 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
1k 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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1k 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
817 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
42k 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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6answers
21k views

Why is multicollinearity not checked in modern statistics/machine learning

In traditional statistics, while building a model, we check for multicollinearity using methods such as estimates of the variance inflation factor (VIF), but in machine learning, we instead use ...
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1answer
560 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
1k 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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0answers
59 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
100 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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274 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
3k 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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398 views

Multicolinearity in logistic regression using R

Upon performing binary logistic regression, I have found VIF, using R programming, as follows: ...
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1answer
331 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
159 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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2answers
555 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 ($x_1$,$x_2$,$x_3$,$x_4$,$x_5$,$x_6$)...
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1answer
697 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
13k views

How to interpret R VIF function in CAR package?

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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0answers
255 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 ...
6
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1answer
11k 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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2answers
564 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 phenomenon that seems quite puzzling, to me at least. I let the GCV criterion choose a constant for me and then ...
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1answer
443 views

variable reduction before doing random forest in R

I have a dataset featuring around 50 predictors, some of which are correlated. Now I am trying to fit a random forest model in R for prediction purpose with this dataset. Because there are too many ...
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0answers
737 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 1....
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0answers
418 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 multi-...
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0answers
11k 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 ...
5
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1answer
4k 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 ...