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Questions tagged [multicollinearity]

62 questions
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Remove linear dependent variables while using the bife package

Some pre-programmed models automatically remove linear dependent variables in their regression output (e.g. lm()) in R. With the bife package, this does not seem to be possible. As stated in the package description in CRAN on page 5: If bife does…
John Doe
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Ordinal Logistic Regression in Python with rpy2 (Python interface for R): issue with collinear predictors

I am trying to perform an Ordinal Logistic Regression in Python calling R's mass.polr function with rpy2 (Python interface for the R language). However, I run into trouble when there are some collinear or almost collinear columns in my predictors:…
Elena Fortina
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Multicollinearity on multiple dependent variables

Consider the following: library(tidyverse) library(car) a <- c( 2, 3, 4, 5, 6, 7, 8, 9, 100, 11) b <- c(5, 6, 7, 80, 9, 10, 11, 12, 13, 14) c <- c(15, 16, 175, 18, 19, 20, 21, 22, 23, 24) x <- c(17,18,50,15,64,15,3,5,6,9) y <-…
Kgosi
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How to overcome infinite value to perform multicollinearity test in environmental variables?

Here's the dataset which consists of X = 45 columns collected the data from bioclimate database. The multicollinearity test model - from statsmodels.stats.outliers_influence import variance_inflation_factor vif_data =…
perth
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Multicollinearity test with car::vif

I am trying to run a car::vif() test in R, to test for multicollinearity. However, when I run the code reg.model1 <- log(Price2) ~ Detached.house + Semi.detached.house + Attached.houses + Apartment + Stock.apartment +…
whoknows
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Convergence issue with multi linear regression problems

I'm try to solve a multi-linear-regression problem with a very simple linear network. The network only consists of a single dense layer as its output layer and the activation function is set to linear. I synthesize the output data Y by multiplying…
Kami
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1 answer

Calculating VIF for ordinal logistic regression & multicollinearity in R

I am running an ordinal regression model. I have 8 explanatory variables, 4 of them categorical ('0' or '1') , 4 of them continuous. Beforehand I want to be sure there's no multicollinearity, so I use the variance inflation factor (vif function…
Hugo Perrichon
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Feature selection based on (automatic) multicollinearity detection

as part of an automatic feature selection pipeline, I am wanting to automatically remove features if they are highly correlated, based on its Variable Inflation Factor (VIF) result. Below is an example of df (although the data I use often have >200…
Danny Smith
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How can I solve multicollinearity?

I constructed a linear model and tried to calculate the VIF of the variables but I get the following error: vif(lm_model3101) Error in vif.default(lm_model3101) : there are aliased coefficients in the model To check which numeric variables are…
Lucasjansens
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How to interpret multicollinearity in a correlation plot?

I created a correlation plot for my dataset in R but I am not sure how to choose which of the following pairs of variables express multicollinearity? An explanation with examples would be really helpful!
Feverish123
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Checking for multicollinearity using fixed effects model in R

Guten Tag from Germany community :) I'm working with panel data and fixed effects (= FE) for both, time and firm. I wanted to check my model for multicollinearity by using the variance inflation factor (= VIF), but R is giving me a warning message…
wrangjangler
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How to simulate multi-collinearity using Sklearn?

I want to see what effect multi-collinearity has on a linear regression model but I need to be able to generate multi collinear data where I can vary the number of features and the collinearity between these features. I've had a look at Sklearn's…
Dwo
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Splitting a data into 70:30 but all the outliers only in the training samples using R

I am simulating data in R to check which models perform better when outliers and multicollinearity present simultaneously. For this, I split the data into 70:30 random split, but I need to introduce outliers and multicollinearity only in the 70…
jeza
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VIF Test for Multiple Multivariate Regression

I would like to test for the Multicollinearity by using the VIF function. I have a model with 13 measures and 4 predictors, created like so: M <- lm(cbind(S1, S2, S3, S4, S5, S6, S7, S8, S9, S10, S11, S12, S13) ~ x1 + x2 + x3 + x4, data =…
Christina
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How to use VIF in r?

I am new in R and learning ml using caret. I was working on UCI bank marketing response data but used iris data here for reproducibility. Issue is that I am getting error on running vif from car package on classification…
ViSa
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