Questions tagged [generalized-linear-model]

A generalization of linear regression allowing for nonlinear relationships via a "link function" and for the variance of the response to depend on the predicted value. (Not to be confused with "general linear model" which extends the ordinary linear model to general covariance structure and multivariate response.)

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How to check for linearity assumption in GLM?

Let g be the link function, y be the target variable, and $\beta_1x_1$+... $\beta_nx_n$ for some $n \in \mathbb{N}$ be the linear predictor. One of the assumptions for a GLM states that there exists a ...
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Cross-validation metric for Gamma GLM model

I have a very highly skewed positive data and I want to use fit a gamma model to the data. For cross-validation, there many different metrics to use to choose the hyperparameters such as: Mean Gamma ...
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glmmTMB: AR1 models fail to converge

I am trying to utilize the first-order autocorrelation [AR(1)] covariance structure abilities of the glmmTMB package (described here by Kasper Kristensen) to model experimental time series data ...
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How do I choose the correct power in a tweedie GLM?

I'm developing a GLM with a tweedie family distribution. How do I choose P correctly and how much does it matter? I know it has to be between 1 and 2, and when I try different values, I don't see an ...
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Can't reproduce confint profile likelihood results

I'm trying to follow this answer here and the linked paper. But I can't seem to get the same results as the confint gives. Sometimes I get something very simmilar, ...
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Interpret (Tweedie) GLM - do the coefficients need to be exponentiated?

I have built a Tweedie GLM and I am trying to interpret the coefficients. I am not sure how I should do this, for example by first exponentiating these coefficients or not. And even then, what would ...
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Best way to set an interaction in a regression between two factors when some interactions are impossible?

I'm interested in determining how territory size (a continuous variable) changes as a function of species (a factor with 3 levels for the 3 species) and region (a factor with 2 levels, north and south)...
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Is it possible that feglm model regression coefficients are statistically significant but its APEs coefficients are insignificant in R?

I am using R as my computation tool to run a regression analysis where I have a dependent variable as a binary outcome (0/1) and a few continuous independent variables. I want to run a feglm logit ...
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Interpreting p-values of glm with multiple predictor variables

I am looking to test if certain characters change with a predictor variable in significantly different ways for different species. For instance, I want to know if width against height. When I use the ...
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Error: coef/vcov not consistent with basis matrix

I am trying to understand predictions from distributed lag no linear models. I use trial data from R and I run a glm model with crossbasis matrix from DLNM package. When I am trying to get the ...
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How to present seasonality in time series?

I study the temperature related mortality using daily data of 25 years and I am confused about the effect of seasonality. In many papers, the only variable is used to present seasonality is Time (Time=...
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Derivation of Neuhaus, Jewell(1993)

I wish to ask a derivation problem in Neuhaus, Jewell(1993) - "A geometric approach to assess bias due to omitted covariates in generalized linear models" The statistical True model dealt in ...
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GLM: effect of link function on choice of transformation of covariate

It struck me that if I have data of the form below, ...
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How to remove residual patterns from GAM?

My data is modelling the proportion of 'foraging' clicks vs overall clicks in a 30 minute interval using GAMs. I have multiple explanatory variables and factors. I have tried including all my ...
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What drives the assumptions for ANOVA, if we know that it's actually a likelihood ratio test and can be used to work with any GLM model?

ANOVA, as a likelihood ratio test, can take any two nested models and compare them. This assesses the main effects (equivalent to joint test of appropriate regression model coefficients). And one can ...
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Question about interpreting the DHARMa residual diagnostics

I have a question about how to understand what is going on in the residuals plot So I have this graph, and two of them are in black while the other one is in red. I know you want all of them to be in ...
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1answer
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Variable selection for logistic regression with Firth's bias reduction method

I'm dealing with a sample of moderate size, and the binary outcome I try to predict suffers from quasi-complete separation. Thus, I apply logistic regression models using Firth's bias reduction method,...
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Why do we model noise in linear regression but not logistic regression?

The canonical probabilistic interpretation of linear regression is that $y$ is equal to $\theta^Tx$, plus a Gaussian noise random variable $\epsilon$. However, in standard logistic regression, we don'...
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Unexpected behaviour of logit regression with glm in R

I recently was puzzled by the behaviour of R's glm when trying to compute a logistic regression ...
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1answer
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'Translate' ANOVA comparison on regression parameters into linear mixed model

I am analysing data from a medication study. Participants did the same task twice; in one session they were given a certain drug and a placebo in the other one. The order of the sessions was perfectly ...
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Is the analysis of residual variance still ANOVA? What about the regression, generalized models, quantile regression?

I noticed the term ANOVA used in many contexts. The one we are taught is ANOVA using the general linear model and only categorical independent variables. We ask if the variables affect the continuous ...
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Gamma Regression as the Last Layer of the Neural Network

My current task involves predicting data that follows a Gamma distribution. To avoid confusion of notations, in the following discussion, the p.d.f will be $$\mathbb{P}(y|\alpha, \beta)=\frac{\beta^\...
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1answer
32 views

checking collinearity in a glm

I'm new to checking the VIF value for a glm model so I just want to make sure i"m understanding this correctly. I have 4 predictors for my count model and the model looks like this: ...
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Can I include the categories of things I am counting as a predictor variable in a poisson GLM?

I am wanting to do a GLM on some count data where I have 31 samples from two groups in which items of interest has been identified. I am then looking for 4 different markers on the items and counting ...
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Will non-linear data always become linear in high dimension?

I was reading the Hands on ML book and I'm on the SVM and Logistic Regression chapters. I started looking up more on these algorithms and apparently they are "linear" classifiers i.e the ...
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Question about significance in glm and plotting effects model

Okays I have a quick question that I'm a little confused about. During my glm model selection, the AIC from my most complicated model (model with say 7 predictors) and then the dredge function ...
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When to include interactions in a glm?

I am using glms to model if the inclusion of a predictor variable is significant in the ability to predict the dependent variable bu comparing residual deviances between the models: ...
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How to interpret the p- values and intercept in a Poisson glm with catagorical predictors

When looking at a glm with non categorical predictors I am given to understand that the intercept the the predicted value of your measure when all predictor variables are at 0. This therefore means ...
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Comparison of fit from OLS, GLS, GLM, ARIMA and DLM

I've been comparing the fit of OLS, GLS, GLM, ARIMA and DLM modelling approaches to my 20 observation time series data set. I had originally done so using RMSE, but wondered whether you could use AIC ...
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1answer
41 views

What's the best approach for analyzing non-normal multivariate time-series data?

I'm comfortable with simple linear models and GLMs but I've never used either for multivariate analysis. My data is counts of three developmental stages of an insect, over time, in randomized complete ...
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GLM or LME for studying risk factors?

I want to predict risk factors for getting fat. My dataset has 2 timepoints per patient (all of them 3 years apart) with Fat measures at visit 1 and visit2. I have information regarding clinical ...
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1answer
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Should you standardize when using a Log link?

If I use a model with a log link function should I still standardize independent variables (since they differ in the scales range) or the log transformation is enough?
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How to compare estimates between glm models?

I have a dataset with counts of four different markers per gene and samples from two origins. I have performed a glm to see if the origin of the sample is ...
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Write out Mixed GAM model mathematically

How do I write out the following mixed Generalised Additive model? ...
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How to estimate relative risk from DLNM?

I want to estimate relative risk from a DLNM model. I use the code bellow: pred<-crosspred(crossbasis, model, cumul=TRUE) Relative_Risk<-(pred$allRRfit-1)x100 ...
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What is a generalized linear model

Generalized linear models have become so central to effective statistical data analysis, however, that it is worth the additional effort required to acquire a basic understanding of the subject. I am ...
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Can we compare the effects of continuous covariate and categorical covariate on response variable in generalized linear regression?

I want to construct a linear model among several variables. The model is $y = \beta_0 + \beta_1 x + \beta_2 z + \varepsilon$, in which $x$ is a continuous variable, and $z$ is a dummy variable, i.e. $...
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Learning more about glm parameters, how to dig deeper?

I have a Poisson distributed glm where I have identified the origin parameter to be significant through comparison to a nest model dropping that parameter: ...
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Specifying several independent priors in stan_glm() in R

I am using the function stan_glm() in R. I am using 4 predictor variables and I want to specify a univariate independent prior for each regression parameter. Right ...
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1answer
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reporting results of a multivariate logistic regression using the glm function in R

I would like to use the glm() function in R to run a multivariate logistic regression. I have also run bi-variate statistics for each variable but want a test that controls for all variables at once (...
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Why does level means coding only work for one (dummy) variable?

Consider the following reproducible example: ...
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Why are the AIC values returned by R's glm function incorrect for these families?

from the R documentation for the glm function: aic A version of Akaike's An Information Criterion, minus twice the maximized log-likelihood plus twice the number of parameters, computed via the ...
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Selection of covariance structures in SAS PROC GLIMMIX

Stroup and Claassen (2020) recently published an article titled Pseudo-Likelihood or Quadrature? What We Thought We Knew, What We Think We Know, and What We Are Still Trying to Figure Out in the ...
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1answer
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Quasi Poisson vs Negative Binomial

I read in several sources that the Quasi Poisson model and the Negative Binomial, should produce (on average) the same results. I tried a simple example and, although very close to each other, the ...
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3answers
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Why should we compare estimates of generalized linear model with its corresponding standard errors?

There is one concept in Statistics that I don't feel clear, and I could not find it in textbooks. Why sometimes do people compare coefficient estimates with corresponding standard errors? Here is the ...
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Tukey hsd test vs DMS test

I am analyzing the differences on my dataset. This dataset has different values according to each group (called season_sized). I have no normality and heterocedasticity, so I performed a GLM to fit my ...
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What has more statistical power when determining glm parameter importance, comparing models with a dropped parameter or coefficient pvalues?

I am looking to determine the significant of a parameter in a negative binomial distributed glm to determine if origin (either isolate or free) is important in the ...
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1answer
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Odd-logs ratio is too large for linear term?

I have the following glm: ...
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1answer
34 views

Equivalent of R-squared in Generalized Linear Model Regression Results? [duplicate]

How do we assess degree of fitness in a Generalized Linear Model (GLM) since R-squared is not given. For example, following are results of regression in iris ...
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Use gamma distribution or log-transform with OLS?

I have blood levels of a chemical as the response or dependent variable. The minimum can be 0 and it has distribution as shown in figure below: I believe this is a gamma distribution. I have to ...

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