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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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Multicollinearity in spatial data

I have a spatial polygon data set with a couple of thousand data entries. I am estimating a spatial durbin error model (SDEM) and I wonder if the output of the vif(OLS) command that I run on the OLS ...
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14 views

feature selection and classification - train and test on the sample?

I have a dataset of 93 records and 45 radiomics variables from various CT scans. I wanted to check if age and sex could be classified by the variables so I made a new variable with both sex and age. I ...
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1answer
31 views

Checking for multicollinearity in selection of variables for regression model

For the selection of variables for a regression model, I did a pairwise correlation matrix between the different predictors and the response variable. From the pairwise correlation matrix, I realise ...
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18 views

Why the df in GVIF test is zero?

I am developing a GAM model,and when I test the vif of the gam model, the result is strange. Is there any problem with my model? The picture is the results. PC1 and PC2 are two independent variables ...
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16 views

Disparate results between functions estimating Variance Inflation Factors

I have been trying to implement the function VIF() from package 'fmsb', and I find that it returns different results compared with the function ...
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28 views

Is there a need to check for Multicollinearity? [duplicate]

I am working on a project for anomaly detection, using two algorithms: Histogram-based outlier score (which just estimates the density of a single feature separately, assign a score based on that and ...
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34 views

Multicollinearity in the data with categorical variables

I want to calculate the vif to check for multicollinearity in my data set. I read that a values of >10 tells me that I could have a problem with multicollinearity in my data set. I run an ols ...
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39 views

GVIF and RIDGE, how to combine them?

Using R function car::vif(model) I understood that I have a data with high levels of multicollinearity. For example, one of the variable has ...
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38 views

How to test for multicollinearity over matrices?

I plan to test whether there is multicollinearity not between predictors in an individual linear regression model (columns in a matrix) but between the models (over matrices). The matrices are all ...
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36 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 ...
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82 views

Using VIF in lasso does it make sense?

I tried to shrink all features that create multicollinearity problem in my model. For this I use VIF for understanding what level of $\alpha$ coefficient for lasso will be enough (calculate VIF for ...
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30 views

GVIFs increase after removing variables

I have a qualitative dependent variable (Y), a dicothomic cathegorical variable (X1), a cathegorical variable (around 60 non-homogeneous groups, X2), two cathegorical variables (10-20 groups each, X3 ...
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173 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 ...
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45 views

When is multicollinearity a good thing or acceptable?

I was looking through a few scenarios where it might be okay to use variables that have high VIF here. But most of such discussions and remedies I have seen use it in the context of linear regression ...
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68 views

lm in R: What's better? “ugly” model with high $R^2$ or a “beautiful” one with worse $R$? How much VIF is too much?

I have been trying to model with lm in R. I have several variables: Initial cell concentration $(X1)$, macroscopic appearance $(X2, X3, X4, X5)$, microscopic appearance $(X6,X7,X8)$, % of cells moving ...
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51 views

How to deal with high VIF and multicollinearity between numerical and categorical values?

I'm performing a multiple linear regression about the price of alcohol. Two of the predictors are the categorical variable 'type of alcohol', the levels being 'beer', 'wine', 'spirits' and the ...
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35 views

Multicollinearity between predictors used to simulate data

I have simulated a dataset containing individual level variables that results from two processes. In the first process there is a selection of individuals according to one variable, say "indQual". ...
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76 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: ...
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502 views

What is the definition of the generalized variance inflation factors (GVIF)?

I am currently working on a statistical project at my school, in short, it is about finding the "best" linear regression model to explain the price of houses in a giving community. The model we have ...
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20 views

Does the estimation process in a regression effect multicollinearity tests

Does the applicability of VIF vary based on the parameter estimation process? For example, can I use VIF to check for multicollinearity if the parameters in my logistic regression are estimated using ...
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709 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 ...
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113 views

VIF in panel data

If I have too many variables can I do a linear model first and then eliminating variable based on VIF and then put the remaining variables in the random or fixed effect model?
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200 views

Which method of calculating the condition number of a group of variables should I consider?

I have 10 variables, basically Likert scales (ordinal variables assuming between 5 and 7 values) and a binary variable. I wanted to assess whether they can be considered as all informative or some of ...
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58 views

Questions about Feature selection

Lets say we have a dataset with hundreds of features. Since I'm not really sure whether all these features are good identifiers or not, I think we might need use one or all of the following ...
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222 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 ...
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325 views

Should we run vif function right after glm() in R programming?

If not then where are we supposed to use that? If yes, then my vif values are all below 3, but statistically they are insignificant variables then what should I do next? How do I make myself ...
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1answer
567 views

Why is Variance Inflation Factors(VIF) in Gretl and Statmodels different?

I have 3 variables R&D Spend, Administration and Marketing spends. I wanted to calculate VIF and eliminate a variable for better fit to the model. I tried to use the solution at ...
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1answer
91 views

Including correlated variables in multiple regression model.

While doing multiple regression, some of my predictors are correlated. But it's not collinear. The results are given below. y - dependent variable a,b,c - independent variables Correlation <...
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29 views

How to interpret different VIF values and coefficients before/after introducing interaction terms? [duplicate]

So as you can see, the VIF changed a lot with/without interaction. Curiously, the explanatory variables "risk" and "scopeinprofit" are both not significant without interaction, but becomes ...
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1answer
2k views

VIF doesn't show up values for categorical variables

My data set contains few varibales which I converted to factor as I wanted them to be in that format. To start with, I wanted to check the importance of each variable and check for multicollinearity. ...
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1answer
230 views

Beginner question about VIF and interactions

I am giving a "blind idiot's" try at VIF (do not understand it, it is just quoted in a hurry by my course) and I notice that adding interactions seems to change the results considerably. This at my ...
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558 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 ...
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960 views

Variation Inflation Factor Gives inf

I have a dataset that contains 292 attributes. I want to calculate VIF to address multicollinearity in dataset. Here is a snippet which I have used to calculate VIF ...
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1answer
145 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: ...
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1answer
258 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 ...
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1answer
2k views

VIF(collinearity) vs Correlation?

I am trying to understand the basic difference between both . As per what i have read through various links, previously asked questions and videos - Correlation means - two variables vary together, ...
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270 views

How to use VIF in fmsb package in R with categorical variables?

I want to see if there's multicolinearity between variables, but some are categorical. I have been using trying to use fmsb's VIF, which gives the example of usage to be: the target multiple ...
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690 views

VIF (Variance Inflation Factor) and correlation in linear regression

Linear regression: $Y = X_1 + X_2$ Is that possible that $X_1$ could have a low $VIF (1.25)$ and the same time, have a $0.35$ correlation with $X_2$? If $X_1$ has almost 1 of correlation with $X_2$, ...
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88 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 ...
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246 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
726 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
118 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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189 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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115 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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183 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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156 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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225 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
653 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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484 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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129 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 ...