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.

Filter by
Sorted by
Tagged with
1
vote
0answers
4 views

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 ...
0
votes
0answers
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 ...
0
votes
0answers
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 ...
0
votes
1answer
33 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 ...
1
vote
1answer
54 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 ...
0
votes
0answers
69 views

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 ...
0
votes
0answers
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 ...
1
vote
1answer
212 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: ...
0
votes
1answer
45 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. ...
0
votes
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 ...
0
votes
0answers
240 views

GVIF output in R

After fitting a logistic regression model m I've run vif() on it and been given the following output ...
1
vote
0answers
205 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: ...
0
votes
0answers
102 views

Variance inflation factors for time series

R code: I got an error by using vif function. ...
1
vote
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/...
-1
votes
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 ...
0
votes
0answers
485 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 $R_j^2$ ...
1
vote
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 ...
0
votes
0answers
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 ...
1
vote
0answers
72 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 = ...
2
votes
0answers
46 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 ...
1
vote
0answers
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 ...
4
votes
1answer
597 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 ...
0
votes
1answer
440 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 ...
0
votes
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 ...
0
votes
0answers
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 ...
0
votes
0answers
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 ...
2
votes
0answers
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 ...
1
vote
0answers
209 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 ...
0
votes
0answers
52 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 ...
1
vote
1answer
153 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 ...
2
votes
1answer
929 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 ...
1
vote
0answers
79 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 ...
0
votes
0answers
80 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 ...
1
vote
0answers
90 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 ...
1
vote
0answers
119 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". ...
2
votes
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: ...
1
vote
0answers
1k 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 ...
1
vote
0answers
27 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 ...
7
votes
2answers
882 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 ...
0
votes
1answer
70 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 ...
4
votes
3answers
549 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 ...
0
votes
0answers
593 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 ...
1
vote
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 ...
0
votes
1answer
131 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 <...
0
votes
1answer
6k 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. ...
0
votes
1answer
672 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 ...
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 ...
0
votes
0answers
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 ...
5
votes
2answers
308 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: ...
2
votes
1answer
542 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 ...