Questions tagged [variance-inflation-factor]
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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Fixed Effects causes Multicollinearity
I have a question regarding my regression model and would be grateful for any help.
I have a dataset that contains every tranche of a Deal (a Deal may contain multiple Tranches) and its Variables like ...
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High vif after squaring the regressors
If I have 2 regressors in regression, with low vif (less than 5 for each of them), after squaring them, the vif increased significantly (more than 10), in the course, we haven’t covered how to fix ...
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Is using VIF to Select Lambda in Ridge Regression a valid approach?
I recently came across an article that suggests selecting the lambda parameter in ridge regression based on Variance Inflation Factor (VIF) values. The method aims to choose a lambda that ensures all ...
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Unusually High Variance in a Nested Model
I have a dataset containing lizard home range estimates and several environmental variables. My end goal is to create a model that uses the best combination of environmental variables to predict home ...
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What's the difference between a beta coef. estimate from linear regression and a VIF value?
I'm having trouble deciding what to do when two important variables (important to my research questions) are seemingly too related, but both need to be included in a full model (this is a longitudinal ...
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Design Effect in Cluster Randomised Trials
I want to understand why and how variance inflation factor is taken in account with the formula [1+(m-1)*ICC], where M= Size of clusters and ICC is the intracluster correlation.
As I have deduced till ...
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Using step() and car::vif(): order matters?
When fitting linear models and coming up with a plausible one, AIC and VIF are often used. However, I notice that the order in which the methods are used makes a difference on the final model.
Should ...
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Introducing ln variables causes VIF to increase above 10
I am running a regression like this on Stata to do model diagnostics: xtreg lnTobinsQ l.ESGScore l.lnTotalAssets l.Leverage l.CurrentRatio l.PricetoBookValueperShare, fe
When I run the command vif, ...
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Variance Inflation Factor for 2 categorical variables is coming to be greater than 5, can I drop one of them?
I am practicing Linear Regression on the Airbnb dataset. The VIF for 2 dummy variables 'room_type_Entire home/apt' and 'room_type_Private room' is coming as '9.546159' and '9.116464'. These dummy ...
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How to proceed when correlation plot doesn't show as much multicorrelation as is seen by statsmodels variance_inflation_factor in a regression task?
I am working with the kaggle Blueberry Yield prediction dataset. There are 17 columns including the target variable. Below is the correlation heat map:
It can be seen that multiple features are ...
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If VIF value is greater than 5,
I am running a binomial logistic regression with a dependent variable is 0/1 and the explanatory variables are categorical - binary and multiple categories. When I add an interaction term, and run a ...
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GVIF in binomial logistic regression
I am running vif(model) to identify multicollinearity between predictor variables in the model. Is there a rule of thumb on the gvif value that indicates presence of multicollinear variables?
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VIF, SMC and the inverse of the correlation matrix
The Variance Inflation Factor (VIF) is defined to be:
$$VIF_p = \frac{1}{1-R_p^2}$$
where $R_p^2$ is the $R^2$ calculated when $X_p$ is the dependent variable, and all the other variables are ...
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If we have a binary variable in our linear regression, the VIF for its coefficient estimate uses the $R^2$ of a linear probability model. What gives?
The variance inflation factor (VIF) in an ordinary least squares linear regression coefficient is calculated using the $R^2$ of a linear model that uses the other features to predict the feature to ...
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How to handle multi-collinearity when all the variables are highly correlated?
In my dataset, all the variables are highly correlated (correlation coefficient > 0.95). However, the correlation with the dependent variables is very low (<0.35). I checked the variation ...
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Multicollinearity and Interaction Effects
I know similar questions were asked before. However, none of the existing answers help me with my problem:
I have a gls model with y=B0+B1X1+B2X2+B3X1X2+e. The VIF value for X1 and the interaction ...
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Using VIF (variance inflation factor) to reduce the data set and account for multicollinearity decreases the r2 and the performance of my model
Currently I am working on three models to predict the personality factors of subjects according to three data sets. All three data sets are exported features from video interviews, i.e. audio features,...
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Having an issue with using the vif() function - 'there are aliased coefficients in the model'
I have the following code. I am trying to run a linear model and then calculate the variance inflation factor for this model using the vif() function.
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Find Variance Inflation Coefficient in linear regression model with country fixed effects - R
I have the following model in R:
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How can Variance Inflation Factor be used to measure multicollinearity in panel data / fixed effects model
I would like to check my data for multicollinearity. The dataset I have consists of panel data and I'm not sure how to go about it.
If I were to calculate the variance inflation factor for the data as ...
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High Adj. $R^2$ (by economic data standards) but insignificant p-values
I'd like to start by saying I'm not a statistician - I have stats education at the Masters level, but no specialization or advanced work experience.
I'm currently trying to regress financial return ...
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Intercept variance inflation factor?
In the comments to the question here, John Madden remarks about the variance inflation factor (VIF) of the intercept of a linear model. Does such a notion exist?
My argument against such a notion is ...
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Regression model has very high const VIF value where as the other features have value between less than 4. Should I drop const?
I am a beginner in modelling. I have created a linear regression model using statsmodel and I see the const has VIF value around 124 where as the other features have value around 4. I already referred ...
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Multicollinearity for ordered logistic regression
How is multicollinearity calcuated for an ordered logistic regression if R^2 is not determined for this type of regression?
Because the formula for vif says: 1/(1-R^2)
In R the looking up ...
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How does variance-inflation factor creep into a chunk test?
In this question, the OP runs a “chunk test” and has a linear relationship between a variable in the restricted model and the variables in the chunk.
If this were a chunk of just one variable, that ...
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Logistic regression: How to improve the pseudo-$R^2?$
Firstly, I would like to ask you opinion about my model: I have a logistic regression model where the dependent variable is late payments (1=late/0=not late) and diesel prices and interest rates as ...
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Variance inflation factors not equal to $C_{jj}$
I am following a book which states that the diagonal elements of $C = (X'X)^{-1}$ are called the variance inflation factors:
$$
VIF_j = C_{jj} = \frac{1}{1-R^2_j}
$$
where $R^2_j$ is the coefficient ...
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VIF after LASSO?
In have been working on a project along with other colleagues in which the dataset has almost 100 variables. The earlier approach was to use LASSO for subset selection and then use VIF to remove the &...
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Does multicollinearity among control variables matter?
I am conducting a regression analysis between $X$ and $Y$, where $X$ is the main independent variable. However, I want to control for several variables that are related to $Y$. For example, my ...
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Is it possible to create a 0 intercept ridge regression model?
I am working on implementing ridge regression for market mix modeling where I wish to use my own create base(UCM) instead of intercept, I had been using linear regression for this purpose but now my ...
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Checking for significant associations between a mixed set of predictors prior to running a conditional random forest model
I am running a conditional random forest model using the party package in R, with the goal of quantifying variable importance (permutation importance) for 29 predictor variables. My response variable ...
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What to do when every VIF value is infinity
I know that VIF values have no upper limit, and that anything over 10 is usually bad news if you are trying to avoid multicollinearity especially for regression models such as multiple logistic ...
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VIF - but with a categorical response variable?
I've seen a tonne of tutorials online for managing categorical variables in a VIF test by making binary dummy variables, but with a variable with 3 or more categories this means creating two or more ...
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Batchwise Variance Inflation Factor Calculation for Feature Selection
I am currently working on a research project that involves interpretation of coefficients from a fitted Cox Proportional Hazards model. Unfortunately, the features within my model exhibit high ...
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Multiple Regression - VIF
could anyone help me with my problem - I’m a beginner.
When doing multiple regression and checking for multicollinearity, I have found 2 VIF’s of 17, however, I’ve done some reading and few say it is ...
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Average VIF after dummy variables [duplicate]
I have classified (label encoding) independent variables. To calculate VIF, I converted them to dummy variables. When I calculate VIF for dummy variables, there are VIF values for each class of an ...
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How to Assess Multicollinearity for Independent Count and Rate Variables
How do I test an independent variable for multicollinearity if it comes from a Poisson or Negative Binomial distribution?
A common approach for testing the multicollinearity of a model's independent ...
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Theoretical backing for VIF
I'm giving a lecture in machine learning class covering linear regression, and wanted to briefly touch up on the idea of multicollinearity since I come from a more traditional stats background. I was ...
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PCA can make VIF worse? Why is it so often sold as a multicollinearity fix?
It's the conventional wisdom that a PCA transformation can cure multicollinearity. Putting this into practice on example data, I find myself confused. In the following case, applying PCA seems to have ...
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VIFs in Fixed-Effects-model
I know that R is providing some Warnings messages when you try to get your VIFs to test for multicollinearity.
But why is that?
Does someone know a good paper or the answer itself?
CODE:
...
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Which threshold should I use for GVIF1/(2⋅df)? (Variance Inflation Factor)
I'm using the mtcars dataset in R, I used the car packages to estimate the VIF, but since I have factor variables I got the vif table with GVIF and GVIF1/(2⋅df) values, in another question Which ...
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Is there any way to determine VIF of some variable that is included in the dataset that has so many variables?
I am new in statistics and need some help to determine the VIF value on all my variables/features in the dataset with a lot of variables. I have 98 variables with 76 observations and need to find VIF ...
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Can I use variance inflation factor (VIF) for logistic regression? [duplicate]
I have generated a glm model with 20 or so predictors. I have carried out stepwise regression(forward and backwards selection) to identify the important predictor variables. My final model has 7 ...
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Is multicollinearity acceptable where one variable is the anomaly between, or sum of, other variables?
I'm using the variance inflation factor (VIF) to calculate multi-collinearity between the variables I'd like to include within a regression model. I was reading this article: https://...
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Running Variance Inflation Factor(VIF) iteratively for a large number of features taking a long time
I am trying to do logistic regression on a dataset with 1,500 features that are very multicolinear. I care about interpretability of coefficients so I am running a VIF calculation on all columns and ...
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Appropriate test for independence of predictor variables in logistic regression
I want to test for independence between 7 categorical predictor variables in a logistic regression model. I believe the appropriate test would be a chi-square test, but I have 7 variables - is there a ...
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Is that possible to rank the importance of independent variables with simple linear regression with standardized IVs
I run a multiple linear regression model with six independent variables using lm function in R (n=25). However, I found severe multicollinearity between my
IVs (based on VIF values). The presence of ...
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How to show VIF
The variance of the $j$th element of the OLS estimator is given by
$$\operatorname{Var}\left(\hat{\beta}_{j}\right)=\sigma^{2}\left(X_{j}^{T} M_{-j} X_{j}\right)^{-1}$$
where $X_j$ is the column of ...
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When using variance inflation factor (VIF) should I do the removal recursively?
This is a pretty straightforward question and I guess I will get a negative score here - I was so happy improving my points in here lol -, but I couldn't find anywhere and even though I believe I know ...
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OLSR model: high negative correlation between 2 predictors but low vif - which one decides if there is multicollinearity?
Date, age, mrt and shops are all predictors in a dataset of 414 observations. Pearson's product-moment correlation shows a sizeable negative correlation between mrt and shops (-0.6 so definitely ...
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