Questions tagged [multicollinearity]
Situation when there is strong linear relationship among predictor variables, so that their correlation matrix becomes (almost) singular. This "ill condition" makes it hard to determine the unique role each of the predictors is playing: estimation problems arise and standard errors are increased. Bivariately very high correlated predictors are one example of multicollinearity.
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GLMM Model Averaging with Predictor Multicollinearity
I am running GLMM models to determine how environmental factors influence bird collisions. I've obtained a list of candidate models with delta AIC less than 2, and I want to perform model averaging.
I ...
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how to find the singular values of singular value decomposition (SVD) [closed]
I am a bit confused as to how we find the singular values and therefore condition index number. Some mathematicians say the singular values are the square roots of the eigenvalues of the correlation ...
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No Multicollinearity between highly Correlated IVs
Suppose I'm building a multiple regression model, with y as dependent variable and X1, X2,..., Xn as independent variables. I've ...
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Any Insights on the adoption and use of the Healthy Akaike Information Criterion (hAIC)?
Recently, I came across the Healthy Akaike Information Criterion (hAIC), introduced by Demidenko in his 2004 book "Mixed Models: Theory and Applications with R." Despite its (potential) ...
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Test for multicollinearity with binary and continuous independent variables
I have a question concerning multicollinearity: I have several independent variables. Some are binary and some continuous. The dependent variable is binary. Can I use the Pearson correlations to test ...
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How to address multicollinearity when adding random effects in gam model and include random slopes?
I'm working with a generalized additive model (GAM) using the mgcv package in R. My dataset includes measurements collected over several years at different sites. I ...
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Understanding the coefficients of highly correlated features in generalized linear models
I am trying to fit a generalized linear model, for simplicity assume that is a linear regression. I have a bunch of features and I fitted a linear model to it, the feature ...
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Is lasso preferable to ridge or principal component regression in multicollinear settings?
Consider a $N\times p$ data matrix $\mathbf X$ with columns $\mathbf x_j$. ESL recommends standardizing the inputs before performing ridge regression, which I understand to mean centering the columns $...
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Connection between multicollinearity and problem of identification in Simultaneous Equations Model
Is there any connection between multicollinearity and problem of identification in Simultaneous Equations Model?
I know Multicollinearity is the occurrence of high intercorrelations among two or more ...
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What is the best model for this case?
I have the following problem:
A data set, which is about the soft drink consumption of people, that
covers 300 subjects are available to us. Using Excel tabulations and
graphing capabilities only:
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Regression with small sample size - LASSO or remove variables?
I'm trying to run a regression, but I only have 14 observations, each being a different city in the US. My dependent variable is the total number of trips per capita, and my explanatory variables are ...
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Is this multicollinearity, and how can I specify my model better?
I'm analyzing data from the usual care period only of a stepped wedge cluster-randomized trial. The goal is to describe the usual care period as though it was a cohort study because much higher ...
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Understanding differences in collinearity across Stata commands
Here's a simple example of a regression of y on x including time and id fixed-effects and both a linear and a quadratic time trend. If my t starts at 1, these 3 different regressions get the same ...
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Does Factor Analysis completely mitigate the singular covariance matrix problem?
Background
I have been trying to understand Stanford CS 229’s lecture about Factor Analysis and the accompanying lecture notes. The lecturer introduced Factor Analysis as a way to mitigate the ...
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Check_model performance package interpretation plots
I am fairly new to R and statistics and I am building GLMs for frogs occupancy and abundance using a dataset with 57 observations and 13 independent variables. As some variables are correlated the ...
