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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213 views

Error when using vif() on glmmTMB obejct

I have run a zero-inflated model as follows: number of birds ~ treatment * date + minutes after sunrise + snow cover + (1|site) This was the code: ...
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In what form should my features be in when running a model trained on features transformed through Weights of Evidence?

I have trained an anomaly detection model on features transformed/selected by means of the Weights of Evidence and Variance Inflation Factor approaches. My question is how should I go about running ...
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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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287 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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228 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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1answer
161 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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170 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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19 views

How to show that VIF's are the main diagonal elements of $\mathbf{T\Lambda^{-1}T'}$?

I'm stucked in the Exercise 9.29 of Introduction to Linear Regression Analysis (5th edition), by Montgomery: 9.29) Show that if $\mathbf{X'X}$ is in correlation form, $\mathbf{\Lambda}$ is the ...
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1answer
213 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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58 views

VIF on binary data

I am performing data analysis on the categorical variables. I have performed one hot encoding. The only thing that I want to ask is how can we compare multiple categorical variables. Is it correct to ...
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13 views

Usage of VIF in unsupervised model

I'm working on building an unsupervised model for real time anomaly detection based on the concept of Randomized Matrix Sketching (http://www.vldb.org/pvldb/vol9/p192-huang.pdf) which involves ...
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82 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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34 views

How is GVIF calculated for categorical variables?Also is there any other way to detect multi co-linearity of categorical variables?

I was tring to find a way to remove the redundant categorical variables as features. I believe GVIF would give high value for the redundant/multicollinear categorical variables. Please let me know if ...
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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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1answer
500 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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1answer
1k 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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23k 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
55 views

multicollinearity between confounders logistic regression

Im going to investigate if a disease have an negative impact on a binary response variable. The disease is the independent variable with additionally confounders. I want to do a manual stepwise ...
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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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How to estimate the VIF for geeglm models in r

I am very new in r and at analyzing gee models. I have a very high dimensional data (51 independent variables measure at multiple times with no missing values (secondary dataset)). I am pretty sure ...
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442 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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22 views

Model Deviance lower than Residual Degree of Freedom

I am trying to calculate the Variance inflation factor (VIF) for a Generalized Additive Model (GAM). The GAM model contains both constant terms and splines. The VIF is defined as Deviance of model ...
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46 views

Excellent model fit but high VIF

I want to use a predictive model for a time series variable M that is related to an other variable X. I can generate independent scenarios for X and I need to generate corresponding values for M. ...
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298 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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3answers
96 views

Why should I check for collinearity in a linear regression?

The Gauss-Markov Assumptions: MLR.1: Linearity in parameters. MLR.2: Random sampling. MLR.3: No perfect multicollinearity. MLR.4: Zero conditional mean Hence, why should I check for high (but not ...
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243 views

GVIF output in R

After fitting a logistic regression model m I've run vif() on it and been given the following output ...
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206 views

Understanding VIF procedure with different methods from r package rms and car - VIF by level of a factor

Consider the output from two different r packages, rms::vif and car::vif: ...
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104 views

Variance inflation factors for time series

R code: I got an error by using vif function. ...
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4k 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
113 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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1answer
42 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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488 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
5k 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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1answer
61 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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23 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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1answer
154 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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73 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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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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1answer
14k 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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34 views

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
603 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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2answers
582 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
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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2answers
577 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
74 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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128 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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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 ...
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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 ...
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210 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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53 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 ...