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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Suggest ways to represent additive fixed effects?

The dataset I'm working with has additive fixed effect where each term is significant. I want to plot the model fit with one of the factors temp here on the x-axis ...
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Suggestions for an independence test in a complex design

In an experiment I surveyed the effect of two treatments (pre & post) in different species. After every experimental run I tested whether the measured average effect was greater, smaller or not ...
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What kind of analysis can I extract from bayes glm output?

I have 40 files (40 variables) that start like this from a bayes glm model output as csv files. I am a total beginner at model evaluation. ...
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Help me understand data output from R bayes glm model

I have been given a bunch of csv files which are ouputs of a bayesian glm model run. I have minimal understanding on machine learning, so apologies beforehand. I'd like to understand what the ...
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Testing difference between coefficients of nonlinear regression models

Let us consider following data showing sigmoidal dose-dependence for two distinct compounds (blue and red): I wonder about the best approach of comparing the blue vs red "curves" with ...
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Is it normal for simple logistic regression to significantly outperform any other statistical ML algorithm?

I'm working on a simple classification project with an imbalanced (minority-to-majority-ratio ~ 0.2) dataset that has ~4000 rows and ~200 features. I noticed that, for my dataset, a simple logistic ...
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How to adjust a response variable by extracting random effects?

I am working on dataset where I need to eliminate the random effects from the input response variable. For this, I am first extracting the random effects by using extract_random_effects() function ...
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Multinomial logistic regression R vs Python

Does anybody have experience with the SKlearn multinomial regression (model = linear_model.LogisticRegression())? My data looks like this: ...
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glm() vs. glmer() on repeated measures

Say I am curious about whether the relationship between class rank (rank) and passing a final test (pass) is dependent upon days until summer vacation (days until summer). In my dataset, I have Mary, ...
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Ascertaining GLM link visually

On pg. 125 in Agresti's Categorical Data Analysis, it's suggested by a plot of the dependent variable (a count) vs an independent variable (categorized version of continuous width variable) that the ...
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How does GLM coefficient standard error transfer to prediction variance and relative risk variance?

How does a coefficient's standard error in a GLM transfer to what you do with that coefficient? For example, for a logistic regression model, $e^{\beta_1}$ is the odds ratio. If the standard error on ...
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Variance function vs distribution variance

In Categorical Data Analysis by Agresti it is stated on page 150, in relation to quasi-likelihood in GLMs, that "with binomial sampling, $E[Y_i]=\pi_i$ and $var(Y_i)=\frac{\pi_i(1-\pi_i)}{n_i}$.&...
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Purpose for the conditions of Link Function

I am studying GLM at the moment and have a few questions regarding link functions. Why are the conditions of the link function to be smooth monotonic function? What properties are preserved by having ...
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How to handle with NA's when doing glm in R (and not removing entire rows)?

I want to make my first glm but in some of the variables I use are NA's. I can't find the right information about how to clean these columns so it can be used in glm. Is it possible to handle this at ...
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Logistic regression: variable coefficient is statistically significant but not statistically significant as an exponentiated odds ratio? [closed]

As mentioned in the title. I came across this instance using GLM in R. Is this an error? EDIT: The p-value of the coefficient was calculated by GLM in R and is less than 0.05. I then plotted the odds ...
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Interpretation of the linear predictor of a OLS model on binomial data

If i have some simulated standard normally distributed data: $$µ_i = β_0 + β_1X_{i1} + β_2X_{i2} +···+β_kX_{ik}$$ where $$Y_i \sim N(\mu_i, 1)$$ Created with function: (with python in this case) ...
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Test for equal parameters of two regression models: compare coefficients directly or check if interactions are zero?

I have two data sets and obtain regression models with coefficient vectors $\beta_1$ and $\beta_2$. I want to test $$H_0: \beta_1 = \beta_2$$ against the alternative that the two vectors are not equal....
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Split linear predictors with link function

Is it possible to split linear predictors contribution up when talking glm of non-normal distributions? If: $$µ_i = g^{-1}(η_i)$$ and $$µ_i = g^{-1}(β_0 + β_1X_{i1} + β_2X_{i2} +···+β_kX_{ik})$$ Is it ...
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How to consider categorial ordinal variable?

First, I would like to apologize because I'm a beginner in statistics and I'm surely confused on some points. I would like to explain a continuous quantitative variable, which is a species activity, ...
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Should I transform numerical variables to categorical variables in GLM?

I'm building a GLM with couple of variables and I have a problem with how to organize my data. I have both numerical and categorical data and I'm struggling as to how should I structure 3 variables. I ...
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Logit GLM and logit beta regression: Practical difference in the interpretation of the coefficients?

Terminology: By logit GLM I mean a generalized linear model with a binomial distribution and a logit link function. By beta regression I mean beta regression with a logit link function. I understand – ...
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why the p value changes when I run a linear mixed model instead of a simple regression? and how random effects affect the output?

I ran two models, a linear regression model and a linear mixed model, I did this because I was suspecting that there were some levels or hierarchy in my data, specifically in my subjects and ...
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Number of variables to include in my GLM and how to interpret the surrogate value analysis

I am working with genes and I am designing a model that its dependent variable must be diagnosis, whereas the rest of potential variables to include are sex, ethnicity(2 levels), rin(RNA integrity ...
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What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)?

I read not too long ago Nelder and McCullagh's book Generalized Linear Models and thought the book was fantastic and I consider it a useful manual on the subject. Not surprising that's the case, ...
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How to build a regression equation for a gamma GzLM and how to interpret it?

I am trying to analyze if referral programs (1/0) have an impact on the average monthly spending of a user. I am confident that the gamma GzLM is the best model for my distribution: According to ...
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Inference of nonparametric tests as linear models

I found some statements throughout the web that suggest that most common statistical tests can be performed as general(ized) linear models (cf. here). For a Wilcoxon test the author of the referenced ...
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Variance functions for Poisson, negative binomial

I'm having some trouble understanding how the variance functions of the Poisson or negative binomial tie in to the standard errors on the coefficients. I'm mentioning 2 models because I'm not sure if ...
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How is the standard deviation of random effects estimated?

For example, the sd of the random intercept reported by lme4 when I use lmer or glmer is much higher than if I just calculated the sd on the list of intercepts generated from ...
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Comparing multiple glm models with different y-variable for best fit

I have seven different GLM models. Let's say: ...
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Generalized linear mixed effects models - Poisson family with log link

When fitting a GLMM with family=poisson(link="log") in lme4, I understand that the coefficients for the estimates of ...
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Interpretation of GLM models with covariance

Interpretation of both GLM-models. How is cognitive function associated to PTSD symptom severity outcome over time between the two groups? Groups exists of treatment and control group. Because of a ...
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Different types of residuals revealing different stories on model fit

I have a series of observations as y_actual and fit a glm model with binomial family using <...
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Difference between generalized logistic regression and logistic regression

I have received a weird comment from a referee of pretty decent Journal. I stated in the methods section that "The association of the exposure with the outcome was investigated in terms of odds ...
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GLM with Gamma distribution: Choosing between two link functions

I need to perform a GLM based analysis on a purely positive, continuous, and highliy right skewed (inflated around low values) outcome variable. I tested several combinations of distributions and link ...
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Differences between P value and confidence interval for calculated OR

I have returned to a piece of work and was re-running some statistical analysis. I found differences in the P values (which is > 0.05) and OR confidence intervals which was greater than 1. Is there ...
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Averaging SVM and GLM results: sensible or stupid?

I have taken two different approaches to calculate probability: using a GLM and an SVM. They are giving slightly different results (which is understandable, they are completely different approaches). ...
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1 answer
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Fitting a multinomial glm for a very large dataset

I have compositional data where for two groups, where each is represented by two ages, there are 100 possible categories for which I observed counts: ...
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How to deal with groups of different sample sizes in phylogenetic GLMs (PGLS)?

I am currently working on a project that aims to characterise in R on a pool of 500 bird species the traits that may be at the origin of their introduction outside their natural habitat and thus ...
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How to deal with bias caused by data collected with different methods on a GLM?

I have two datasets from different years (2020 and 2021) that I needed to merge because my sample was too small. I'm investigating which morphological features (sex, age, weight, fat reserves and ...
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Is there a way to correct for degrees of freedom when using a generalized linear model with a Poisson distribution featuring random effects?

I am running a generalized linear mixed effect model with a Poisson distribution to analyse count data. The model has a random effect that takes into account multiple observation obtained by the same ...
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Does DHARMa test if random intercepts are normally distributed?

If if have a GLMM that contains a random effect (random intercepts), would the residual plots that DHARMa generates give any indications if the random intercepts are not normally distributed?
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Blocking Binomial Data (Bernoulli Trials)

Imagine you have a colony of roaches and you want to compare the efficacies of two insecticides. On one day you apply insecticide A to 50 insects and record your "outcome" as "1" (...
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Probability curve and significance in ordinal models

I ran a generalized ordinal model with 4 classes: (150:302='1'; 65:150 = '2'; 30:65 ='3';0:30 = '4'"). ...
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Should I use GLMM or GAM in my analysis?

I am analyzing data on polar bears and trying to figure out if different variables influence their movement. My data has a mix of categorical (e.g. bear ID number) and numerical variables (e.g. bear ...
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How to compare trend of two different datasets?

I have two different datasets (ID 1 and 2) which show the number of animals per hectare for each year. I need to know if the trend for the first periode is different then the trend for the second ...
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Design matrix for getting desired contrasts in 3x3 design with 2 controls

We have nine conditions in our study (two category factors each with three levels; A: a1, a2, a3; S: s, c1, c2). I did two-way ANOVA; however, we are interested in a more nuanced question. c1 and c2 ...
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In a typical clinical longitudinal trial, why do marginal models (MMRM, GEE) are much more common than conditional ones (mixed)?

I read statistical analysis plans and reports of over 80 longitudinal trials. I noticed, that in 70% they were analysed using a marginal model, namely the MMRM approach (mixed-model repeated measures)....
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multilevel logistic regression via maximum likelihood in R

I want to develop a mixed effect logistic regression in R using likelihood function and compare the results (estimated parameters) with the output of glmer function. I couldn't find a good material ...
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PCA versus mixed effects model: Incorporating relationship between loadings?

I have species abundances with associations between environmental variables. I realize the RDA will only be able to tell you the strength of the relationship of all the species abundances with the ...
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1 vote
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Does my predictor in my multiple regression have too many variables?

So I am trying to work out what is the best predictor of a) awareness over environmental issues, b) concern over environmental issues and c) pro-environmental behaviour from a set of sociodemographics ...
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