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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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How to test the homoskedacity using GQ test, Interpret R2 with VIF, Calculate Adjusted R-squared [on hold]

Can you please show me with some explanations how to test the homoskedacity using GQ test, calculate and interpret R2 with VIF for a model, Calculate Adjusted R-squared from the pictures from this ...
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1answer
26 views

Is it normal for the intercept to have a 1600 Variance Inflation Factor (VIF)?

I'm using Python's module to calculate the VIF for my variables to be used in a binary logistic regression. I'm completely following this post to do this: https://etav.github.io/python/...
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interaction term and multicollinearity [duplicate]

I ran a logistic regression moderation analyses and I noticed that with the addition of the interaction term, one of the predictors flipped signs. The variable (X) was previously -.015 and became .006 ...
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1answer
33 views

multicollinarity issue

I ran a logistic regression moderation analyses and I noticed that with the addition of the interaction term, one of the predictors flipped signs. The variable (X) was previously -.015 and became .006 ...
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32 views

Why is the value of vif from $(X'X)^{-1}$ not matching the result?

The diagonal elements of matrix $C = (X'X)^{-1}$ are $C_{jj} = \frac{1}{1-R_{j}^{2}}$ (which is nothing but the vif) of $x_j$ where $j = 1, 2, 3, ..., n$ and $X$ is a $n\times p$ matrix and $R_j^2$ ...
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1answer
37 views

How can I use linear/logistic regression for inference with colinear variables and a smallish dataset?

I have a dataset of around 120 observations, with 30 calculated variables and I am trying to predict a continuous response (result of an experiment) using those 30 variables. Ideally the smallest ...
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16 views

Steps after calculating VIF to deal with multi collinearity

How do we eliminate variables after calculating VIF ? I have read about step wise removal of variables with high VIF till we reach VIF values below 5. On the other hand , my professor told me that ...
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23 views

VIF - Variance Inflation, when to remove the variable

I'm doing a regression analysis on cement mixtures. The goal is obviously to create the mixture with the most strength. Here are the following variables for me to work with: Variables: Strength = ...
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18 views

Colinearity and Variance Inflation Factor

I want to ask a question regarding colinearity and Variance Inflation Factor (VIF). I started with 7 variables, and have excluded one since there were not correlation between that predictor variable ...
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37 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 ...
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What regression diagnostics should I perform for an ordered probit?

Currently I have done the following diagnostics with the linktest multicollinearity with vif the parallel lines assumption with lr test of the oprobit and goprobit. I have seen that I may have to ...
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1answer
70 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 ...
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1answer
93 views

checking multicollinearity with generalized additive model in R

I am trying to check multicollinearity with GAM using VIF in R. Should I use vif from the package car ? or Is it right way to check vif using vif.gam from package mgcv::helper:: ? It gives quite ...
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21 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
41 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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28 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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17 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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29 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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123 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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79 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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49 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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127 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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37 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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1answer
58 views

Can VIF and backward elimination be used on a logistic regression model?

I'm conducting a study on mandatory reports in the healthcare sector. I've got a sample of 760 visits (690 individual patients ). I will use a binary logistic regression model to see if my independent ...
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1answer
392 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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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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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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75 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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84 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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106 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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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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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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766 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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1answer
67 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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3answers
331 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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428 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
839 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
100 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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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
3k 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
443 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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737 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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2k 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
199 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
366 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
3k 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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313 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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1answer
934 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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136 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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2answers
286 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 ...