# 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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### d-prime as dependent variable in mixed effects model

d-prime refers to the sensitivity index in the signal detection theory, calculated as the z(probability of hit)-z(probability of false alarm). If I have to build a linear mixed-effects model with d-...
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### Standardizing predictors for Neg Binom Regression

tl;dr: If I include an interaction term in Negative Binomial Regression, should I standardize the predictors? I am analyzing a very large dataset (over 60 million observations) that has the following ...
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### Can we model a bimodal response variable using a mixed effect model?

I have a response variable that is bimodal (basically, 2 normal distributions that are sticked together) and want to model it using a linear mixed effect model. Here is a quick example (in R): <...
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### How do I find unadjusted & adjusted Odd Ratio in SPSS if I am using Generalized Linear Models [on hold]

Where do I find data on the SPSS output window if I want to report the unadjusted and adjusted Odd ratio with 95% Cl using Generalized Linear Model.
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### Linear combine non-linear transformations

Could someone tell me what it is called if you linear combine a non-linear transformation such as: $$y_i = \beta_1 f(x_{1i}) + \beta_2 f(x_{2i}) + \ldots + \beta_n f(x_{ni}),$$ where $f(\cdot)$ is a ...
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### Concave downward link function for a glm?

I seem to occasionally find datasets where the relationship between X and Y is concave downward. It seems like it should be trivial to find a link function that fits a concave downward curve, but they ...
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### Test to know when to use GLM over Linear Regression?

Generalized Linear Models (GLMs) are more general than Linear Regression by construction. Nearly the same question was asked here: When to use GLM instead of LM?. However I'm not very satisfied of the ...
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### Graphing Grouped Logit With Fixed Effects - Predictions Outside Bounds

I have a dataset of aggregated binomial data: have a list of candidates and the proportion of their ads that have certain characteristics. Since I calculated it myself, I also have the total number of ...
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### Significance of intercept only in Logistic Regression analysis

Having performanced a logistic regression in R with the glm function, I'm not sure how to interpret the results for the Intercept (as shown below). So I found that my intercept is significant but all ...
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### gene differential expression with R [closed]

I'm trying to find out if a gene is differential expressed. A extract of the data is in my code below. ...
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### How reliable is a linear model on log-transformed data

I have collected timing data in which the residuals are non-normally distributed. I log-transformed the data, and then conducted a linear mixed-model regression analysis. (The residuals from the log-...
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### glmStepAIC model is doing better that other models

I am training a model on an imbalanced dataset (about 5-20% of positive class) and trying out different algorithms in R using caret package. I have 57 predictors and around 2000-3000 observations in ...
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### Numerical instability vs infinite odds ratio

This is a subtle question which I don't think has been precisely asked so please read carefully before voting to close: It's well known that GLMs, notably logistic regression, can spit out bizarre ...
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### How can I compare GLM models that have different covariates [closed]

I want to compare two GLM models that have different distribution and therefore different covariates. How can I do this? Is AIC/BIC enough?
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### Count Analysis - How to analyze the Impact of a Predictor

Say you have data as shown below and the data represents Incidents of a certain type at a Toll Plaza. The incident count is directly related to the Vehicles Per Day (VPD), so you can calculate the ...
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### generalized linear model with log link using log transformed fixed/random effects?

I am modelling a longitudinal dataset consisting of a continuous response variable (mutation count) with a binary predictor (medical history, ie previous medications) while accounting for time and ...
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### poisson vs ordinal regression

I have an outcome variable in my dataset that follows a Poisson distribution Y Frequency 0 52121 1 2831 2 34 0 - None, 1 - Moderate, 2- Severe : ...
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### compare frequencies between 6 samples

I'm new to statistics so this might be a really newbie question.. I have a table in R as such: ...
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### Generalized linear model in R; what family to use?

I'm trying to find out which family I need to use for a generalized linear model in R where the outcome is continuous (Biotic indices like Shannon and water framework directive water quality measures)...
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### Which statistical test works for paired and grouped data?

I have a small set of samples of plants where I've made some changes to some but not others and want to know if there was any significant effect on the number of leaves as a result of this change. My ...
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### Standardized Regression Coefficients Comparisons

I came across this claim in the Statistical Analysis section of a clinical research article. GEE was used to derive standardized regression coefficients, which in any one regression equation are ...
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### Partial Regression Plots and Effect Displays

I cannot grasp how to illustrate a multivariable general linear model. Suppose I have a multiple regression in which I predict blood pressure with age, sex, and %fat. Now, suppose I would like to ...
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### How do you deal with factors, in factorial design, when some or all levels of a factor are meaningless for one or some levels of another factor?

( Introduction: My question is essentially neither about software nor about data preparation. It's about understanding a principle in statistics topic of factorial design of experiments. In reality I ...
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### Gamma GLM - Derive prediction intervals for new x_i

In a Gamma GLM, the statistical model for each observation 𝑖 is assumed to be $Y_i \sim Gamma(shape, scale)$, where $E(Y_i) = \mu_i = f(X_i\beta)$, and $f$ is the link function. I've used MLE to ...
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### What GLM family should I use?

I have data on reproductive success of Drosophila throughout their whole lifetime. It is mainly proportional data I have, as each observation is of the type (#successes = WT in my case, #failures = ...
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### What stats should you include on poisson regression plot

I have this quasi poisson regression plot and I will be presenting in at a conference (obviously it will look better than this). What statistics should you be presenting? I know likely the p value and ...
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### Can you use regression to predict values if you imputed data using MICE?

I used multiple imputation on a data set that had some missing values (I had to do this as the sample size was low so I couldn't just exclude the NAs). I know you can do ...
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### Variable Importance Intepretation for GLM

I'm having a confusion and can't find any answer from the docs.ai that H2O Team provided. I'm creating a summary from my glm and receive a variable importance table. But i can't understand what is ...
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### Glmer: output of model when scaling a continuous dependent variable

I'm exploring the use of generalized linear mixed effects models with lme4's glmer function and I have a question regarding the scaling of independent (continuous) ...
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### Feature Importance for Linear Regression

Is there a way to find feature importance of linear regression similar to tree algorithms, or even some parameter which is indicative? I am aware that the coefficients don't necessarily give us the ...
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### inverse gaussian glm residual deviance

I am currently modelling crash severity data with an inverse gaussian glm with a log link. I read that model residual deviance ~ $\chi^2_{n-p}$ Would there be an obvious reason why the residual ...
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### Is an offset term necessary for a count model of a behavior where subjects determine trial length?

We are modeling data from a behavioral study in which subject pairs' conversations are coded for specific types of utterances (say "Type A"). Subjects decide when their trial is over and we count Type ...
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### Can top levels be fit as random effects while lower levels are fitted as fixed effects?

I am analysing a data set with a cross-classified structure, using a GLMM with a logit link. The unit of observation is clustered within two crossed hierarchies: one has three levels, the other has ...
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### What is the special name for linear regression when there are many parameters in y instead of one?

I rememeber reading about something that is exactly a linear regression A x = y Except that y for each x, is not just one point but rather a vector. Can any one please remind me what's that?
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### Time Series equivalent of the Generalised Linear Model

I have a time series $y_t$ which is measured at regular intervals over a long period of time. The values of $y$ are between $0$ and $1$, it represents a proportion, and these values change slowly over ...
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### Dealing with NAs in Poisson Regression estimators [duplicate]

I am trying to fit a Poisson regression in some soccer matches. I want to be able to predict matches of the first league for a new season, which means that there will be some new teams that have been ...