Stack Exchange Network

Stack Exchange network consists of 175 Q&A communities including Stack Overflow, the largest, most trusted online community for developers to learn, share their knowledge, and build their careers.

Visit Stack Exchange

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.

40
votes
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 ...
26
votes
3answers
29k 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 ...
24
votes
2answers
7k 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 ...
14
votes
2answers
27k 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 ...
13
votes
6answers
8k 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. ...
11
votes
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 ...
10
votes
1answer
1k 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 + ...
8
votes
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 - \...
7
votes
2answers
859 views

Multicollinearity between ln(x) and ln(x)^2

I am running a negative binomial model and one of my predictor variables is a count variable. Since this variable was heavily skewed, I decided to log-transform it. However, the effect of this ...
7
votes
1answer
3k views

What is the fastest method for determining collinearity degree?

Given a large amount of data, how would I determine the degree of collinearity between all the variables? Preferably without relying on calculating linear regression between a variable and every other ...
7
votes
2answers
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 ...
6
votes
3answers
7k 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 ...
6
votes
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, ...
6
votes
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. ...
5
votes
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 ...
5
votes
1answer
670 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 ...
5
votes
1answer
10k views

How to test for multicollinearity among dummy explanatory variables?

I have a Box-Cox regression where the explanatory variables are almost all dummy variables. If I want to see if there is multicollinearity among them, what would be an appropriate test? Do variance ...
5
votes
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 ...
5
votes
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 ...
5
votes
1answer
437 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 ...
5
votes
1answer
256 views

Does the VIF make sense for a model with categorical variables?

I'm trying to detect multicollinearity in my model, it has count response variable and some proportional and one categorical explanatory variable called site. In R the model looks like this: ...
4
votes
1answer
624 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 ...
4
votes
1answer
438 views

VIF Drops Significantly When I Delete Some Dummy Variables

Is my model valid even with the high VIF? Does it matter which dummy variable I drop as the reference point? I have a a category variable (Fruit) that I converted ...
4
votes
1answer
10k 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 ...
4
votes
3answers
500 views

VIF understanding - does only >4 variables are multi-collinear and others are not?

I am trying to understand if there will be multicollinearity between few variables or not. I took a sample data and tried to see the Variance Influencing Factor results - in general vif > 4 indicates ...
4
votes
2answers
554 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 ($x_1$,$x_2$,$x_3$,$x_4$,$x_5$,$x_6$)...
4
votes
0answers
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 ...
3
votes
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 ...
3
votes
2answers
4k 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 ...
3
votes
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} \...
3
votes
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 ...
3
votes
0answers
417 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-...
3
votes
0answers
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? ...
3
votes
0answers
171 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 ...
2
votes
1answer
5k 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) ...
2
votes
1answer
5k 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 ...
2
votes
1answer
498 views

How to deal with interaction term's VIF score

I have a linear regression model that has no multicolinearity problem with low VIF scores. However, when I include the interaction term, this interaction term and its components get very high VIF ...
2
votes
2answers
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 ...
2
votes
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 ...
2
votes
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 ...
2
votes
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?
2
votes
1answer
794 views

How do I know what my VIF limits should be for collinearity should be when doing binary logistic regression?

Using SPSS, I have determined that for each of my IVs the VIF is between 1.2 to 5.1. How do I know what is a good cut off and what is a bad one? How is this determined? Is it relative to the number ...
2
votes
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 ...
2
votes
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 ...
2
votes
1answer
330 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 ...
2
votes
0answers
44 views

Is multicolinearity testing altogether useless if the p-value on each regressor is less than 0.01?

Isn't the effect of multicolinearity to artificially increase the standard errors and thus artificially decrease the t-statistics, and thus artificially increase the p-values? If so, if all ...
2
votes
0answers
71 views

Is it appropriate to use a pseudo R-squared when calculating a variance inflation factor for a binary variable?

When calculating the Variance Inflation Factor for a variable $X_j$ in a multiple regression: $$VIF_j= \frac{1}{1-R^2_{j}}$$ if the variable in question is a dummy variable with 0/1 values, is it ...
2
votes
1answer
156 views

How to interpret an intercept VIF

I ran a multiple regression with six independent variables (A-F) and an Intercept. None of the independent variables has a VIF to worry about but the Intercept VIF is way too large: ...
2
votes
0answers
1k views

Multicollinearity (or not) in exploratory factor analysis

I’m performing an exploratory factor analysis with 28 items, n = 300. I’m confused whether I have a multicollinearity problem or not, and if so whether/how I go about choosing items to remove from the ...
2
votes
0answers
217 views

Identifying variables contributing to near multicollinearty in linear regression using VIF's and multiple R squared's

When trying to detect collinear columns in $X$ a high proportion of cases give a $R_k^2$ close to 1 for independent columns (see figure). When near multicollinearity arise in a $n\times m$ data ...