Multicollinearity means predictor variables are correlated with each other, making it harder to determine the role each of the correlated variables is playing. Mathematically, it means the standard errors are increased. Multicollinearity can have counter-intuitive effects.

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Combine two regression models when variables are highly correlated

I investigated the relation between an angle $\alpha$ and a sensor value $x$. So I have $\alpha = f(x)$ which can be modeled here as a simple polynom. When I want to use this relationship in a real ...
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15 views

Zero inflated GLM and singularities

So I am using a zero-inflated model to (1) model the presence/absence of an animal over certain habitat characteristics using a binomial distribution (2) model the count data over the same ...
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47 views

Ridge Regression Plot by Direct Calculation [closed]

I would like to emphasize that ridge regression coefficients is becoming close to zero as the penalty parameter $\lambda$ increases, but without using R package (glmnet, lm.ridge). My procedures are: ...
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12 views

Is dependency created when adding one variable which is the difference of two existing variables to a regression model?

I have two variables $x_1$ and $x_2$ in linear regression. I would like to see if the distance between $x_1-x_2$ is significant. So I want to add one more variable $x_3$, which is equal to $x_1-x_2$. ...
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1answer
83 views

Too many variables and multicollinearity in OLS regression

After reading material related to my topic, I understood that multicollinearity among predictors would result in singular matrix $X'X$, and that leads to noninvertible matrix. Thus, the solution will ...
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1answer
205 views

Can standardized $\beta$ coefficients in linear regression be used to estimate the $R^2$?

I am trying to interpret the results of an article, where they applied multiple regression to predict various outcomes. However the $\beta$'s (standardized B coefficients defined as $\beta_{x_1} = B_{...
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6 views

multicollinearity, why is my variable significant? [duplicate]

In the case of a linear regression where: X1 ~ X2 is highly correlated (r2>0.5) X1 ~ X3 is significantly associated (P=0.00001) X2 ~ X3 is not significantly associated (P=1) I'm wondering why I ...
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12 views

Is there a best practice for managing the effects of hierarchical predictors in a logistic regression?

Let us say I have feature vectors for each of my visitors to my website on which topics they visited. Each feature vector would be of the form: ...
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22 views

collinearity in conditional logistic regression: glm vs coxph

I am fitting some conditional logistic regression models to wildlife radio telemetry data using a 1:1 paired design, specifically where habitat features at a single telemetry point are compared to ...
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20 views

Multiple linear regression - significant ANOVA, but non-significant predictors. No multicollinearity. What's the problem? [duplicate]

I've conducted a multiple linear regression on a particular outcome variable with one covariate (block 1) and five related but independent predictors (block 2). The ANOVAs for both models are ...
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3answers
201 views

What is an example of perfect multicollinearity?

What is an example of perfect collinearity in terms of the design matrix $X$? I would like an example where $\hat \beta = (X'X)^{-1}X'Y$ can't be estimated because $(X'X)$ is not invertible.
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33 views

Multicollinearity: How to convert GVIF^(1/(2df)) values to VIF

I am using the GVIF^(1/(2df)) method in my analyses to check for multicollinearity of my (mainly) categorical variables. However, I am struggling with the cut-off values. For the 'regular' VIF several ...
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33 views

Feature Selection with Categorical Variables: Multicollinearity and Statistical Significance

Building a logistic regression model with three categorical features and one continuous. For simplicity, let's say I have the following features and variables: ...
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29 views

The proof of lasso regression solution? In which it shrinks some of coefficents to zero? [duplicate]

I would like to know how the lasso method shrinks some of coefficients exactly to zero, "for example could show me how that works mathematically if there are two parameter". For example, with ridge ...
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1answer
38 views

check multicollinearity before regression in R

I want to check multicollinearity to avoid any redundancy in my database before doing the multinomial logistic regression with categorical dependent variable using R, knowing that the majority of my ...
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1answer
12 views

lateral collinearity

Is there somebody who could, in a reasonably general way, explain "lateral collinearity" and its practical implication in logistic regression? Does lateral collinearity necessarily imply strong ...
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1answer
70 views

Multicollinearity with highly safe t-statistics but VIF of 13

If all of my coefficients in my logsitic model have really perfect t-statistics that all show sufficiently high significance but have two coefficients that have high VIF like 13-14 with sample size of ...
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33 views

Can you create a mixed effects General Additive Model?

I'm facing a challenge with how to apply a GAM to a dataset. I'm using network data, pretend for the sake of specificity, between individuals. I'm interesting in the relationship between team size and ...
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1answer
159 views

Is multicolinearity problem ignorable under this situation?

When I run the logistic regression, two independent variables have VIF values greater than 10 like 13 or so. Logistic regression is the one I will use to measure the overall change in the dependent ...
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3answers
546 views

Can I ignore multicolinearity problem if all the regression coefficients are highly significant? [duplicate]

Can I ignore multicolinearity problem if all the regression coefficients are highly significant? My data is large enough (i.e. I have several regression models where each of the data points for them ...
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1answer
25 views

multicollinearity in logistic regression

Which is the best way to check for multicollinearity between two binary explanatory variables in logistic regression..? I use SPSS, if anyone could answer with special regards to that programme it ...
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1answer
50 views

Does the order of entering predictors in multiple regression model change the standardised Beta coefficients?

I am reviewing a number of research papers regarding domestic space heating energy consumption which used multiple regression techniques to identify the main determinants of space heating in the ...
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1answer
13 views

Centering variables for interaction, continuous x catagorical (2 levels)

For a multiple regression analyses I'm looking to assess whether an effect of a contiuous independent variable on a continuous dependent variable is different between two levels of a catagorical ...
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34 views

Multivariable Regression on correlated / collinear variables

Let us consider a variable $Y$ on which we want to apply a multivariable linear regression on the variables $X_1$ and $X_2$. $X_1$ and $X_2$ are collinear by construction, with $X_1=ab$ and $X_2=bc$, ...
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Detecting collinearity in Logistic Regression model

I'm running a predictive model using the logistic model in SAS and, currently, I'm trying to perform some diagnostics about the collinearity issue in the estimated model. To do that, I followed step-...
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10 views

Principal Component Analysis handled multicolinearity in data [duplicate]

How principal component analysis handled high multicolinearity in data set?
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2answers
32 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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15 views

Explaining suppression effects in mediation analysis

The study focus is mediational effects of spirituality on the relations between engagement and wellbeing. Rather than the mediator decreasing the relations between engagement and wellbeing when it ...
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8 views

Conflicting intuitions re linear systems in levels vs shares

Suppose I have a set of components that sum to some total value, different in each period. Call the total Y. I have a set of equations, linear in the (sometimes transformed) variables, each ...
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31 views

How to calculate variance inflation factor (VIF) for a GLMER?

I've never worked with GLMER models before and I was wondering if calculating VIF is in any way different than in simpler regressions. In R, for instance, I usually use the ...
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23 views

VIF interactions

I would like to check for multicollinearity in logistic regression analysis. Independent variables are categorical (always binary) and continuous. Sample has limitted size (N=176, 36 events), so I can ...
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13 views

Univariate analysis, Multicollinearity

I am developing multivariate binary logistic regression model in order to find out risk factors for death (outcome). Should I use univariate logistic regression in order to "reduce" predictor´s list ...
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2answers
33 views

log log model: multicollinearity and interpretation

I would like some advice on a small multiple regression model. The model is a log - log one, with log(investments) as the dependent variable. My issue is that I would like to introduce log(GDP per ...
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1answer
33 views

Collinearity testing between predictors

I would like to test a collinearity between possible "predictors (risk factors)" for binary outcome (death). Possible "predictors" are categorical (always binary) and continuous... For two ...
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1answer
116 views

What to do when lasso does not remove correlated variables?

The very essence of lasso is that it is supposed to select only one of two correlated variables. However, when I include two highly correlated predictors (they are correlated with each other at level ...
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1answer
25 views

What is the difference/relation between variables that are multicollinear, confounding, interacting

What is the exact difference between two variables that are multicollinear and two variables that are interacting and two variables that are confounding? Are they multiple meanings for the same thing?...
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27 views

Colinearity and eigenvalue

I'm doing a binary regression with two predictors, one continuous and one binary. To check for collinearity I looked at the correlation .444, at the VIF 1.13 and 1.18 and the % of eigenvalue share. ...
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Shapley Value Regression for prediction

I've been successful in using the relaimpo-Package for R in SPSS through STATS_RELIMP to calculate the Importances of different predictors (in cases of multicollinearity). What im wondering now is how ...
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Does the variance inflation factor make sense for regularized regression?

I have a logistic regression model fit using L1 regularization. There are two variables that entered the model that have a correlation of over 0.90. The VIF for these variables are each about 60 which ...
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297 views

Strange outcomes in binary logistic regression in SPSS

I did a binary logistic regression with SPSS 23 and I found some strange outcomes. This is for NOACprev until No_Prev_treatment, the last 6 variables. First of all they have very high outcomes for B, ...
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28 views

Statistic test for correlation among multiple variables || statistical test for confirming sales pattern among multiple items

While thinking about sales of a company, I came across a question. I have data like below: Day1 day2 ......day30. item1 10. 20. ....... 30. ...
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1answer
42 views

Multicollinearity - continuous and dummy variables

I know that one of the assumptions of Gauss-Markov is no perfect multicollinearity. If I want to run a model that estimates the effect of gambling on wages, would this model be appropriate: Wage = ...
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3answers
54 views

Does practical insignificance mean no relationship?

I have two problems: 1) I have a regression coefficient that is very significant (large dataset), but has low practical significance. Can I say there is no relationship? And I mean really tiny, like ....
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How to control serially correlated independent variables?

I'm interested in studying the impact of one variable (e.g., R&D expense at year T) on future firm performance (e.g., Sales in year T+5), I know it's incorrect to specify the following model: ...
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8 views

Gross capital and consumption expenditure

I am doing a multivariate regression with gross capital formation and final consumption expenditure as the dependent variables. This is more in the realm of economics, but will the nature of the two ...
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1answer
22 views

Can feature with high positive correlation have opposite weights sign? [duplicate]

I have two features for a binary classification problem which are highly positively correlated. (0.79) But when I build a logistic regression classification then I see their weights are opposite in ...
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29 views

Should the intercept be included when you check the condition index?

Many sources state that a condition index >30 constitutes a multicollinearity problem. When I've tried to implement this check in practice, I've realized that the condition index (and VIFs) change ...
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27 views

Perfect collinearity between one level of two categorical variables

I am setting up a logistic regression model with mostly categorical independent variables (answers to survey questions). Some of the variables have levels like "High-Medium-Low-NotApplicable". The ...
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2answers
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Why aren't we simply using $R_j^2$ instead the VIF?

After all we calculate the VIF by $1/(1-R_j^2)$. A VIF of $5$ corresponds to an $R_J^2$ of $0.8$. To me, the information given by $R_j^2$ just becomes more obscure when I apply the VIF formula. Why ...
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Why are VIFs below ten not still considered very worrying?

I've been trying to read up on multicollinearity, and I think I have a decent grasp of it, and of what VIF tells me. But there is one aspect of the advice that seems quite universal, but makes me ...