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 ...

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9 views

Using gbm to eliminate variables before glm

I have a classification problem I am attempting to model using logistic regression (via the glm package in R): ...
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9 views

data Blocks in glm function [on hold]

i know is it too short and poor of information but, someone know hoy to do this "aov( response ~ item + Error( restaurant / item ) )" with glm . this: ...
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13 views

Permutation to test SNP interactions

sampleID case/ctrl var1 var2 var3 var4 var5... sample1 0 1 0 0 1 0 1... sample2 1 0 0 0 0 1 0... sample3 0 1 1 1 0 0 0... sample4 1 0 0 0 0 1 0... ... case/ctrl status is binary, variant status is ...
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10 views

How to combine different levels of a factor in GLM Modeling?

I am running a GLM Model and I have include both continuous(Binned up to 50 class) and categorical variables in my model. When I ran my model, I was not happy with the trend of some variables and then ...
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13 views

How to get average of squared residuals vs. Predicted frequency graph with variance function line in R for GLM model

How to get average of squared residuals vs. Predicted frequency graph with variance function line in R for GLM model. fitted my data into GLM model using glm.nb function. Was looking to get the ...
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1answer
19 views

Data centering for logistic regression estimation

I read in some articles that we can use data centering as per-process of logistic regression. Centering is $X-Mean(X)$ for every value of $X$ input? What is interpretation of coefficients in this ...
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1answer
32 views

Generalized inverse solution to system of linear equations proof

I'm going through a set of course notes for an introduction to the theory of linear models class at my university. Unfortunately, the professor who wrote this note set is no longer at this school, and ...
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2answers
62 views

Stata automatically tests collinearity for logistic regression?

I'm using Stata for logistic regression. This software automatically checks for collinearity and remove (drop) some variables as we can see below: ...
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2answers
64 views

Offset in a Poisson GLM (R)

I am trying to model disease counts (d) by using population (p) as offset to control for exposure. In R, I found two possible ways to go: ...
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9 views

What's the drawback of using interaction terms to analyze the pre-post control data?

I am trying to analyze the data with the pre-post-control design in the context of RNA-seq analysis. I have read Best practice when analyzing pre-post treatment-control designs, but I am still ...
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30 views

Gini Coefficient - Variable Importance Measure

There is a whitepaper for selecting important variables in a linear regression model. The URL of the whitepaper is http://support.sas.com/resources/papers/proceedings15/3242-2015.pdf . It explains ...
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12 views

two-way repeated measures generalized estimating equations

I have a procedural question more than anything. I have a two-way within subjects design with a dichotomous outcome (success/fail) that I want to analyze using generalized estimating equations (GEE). ...
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15 views

glm, do I need to code my predictors data to categorical data first ?

Sorry if this is a naive question. I am kind of new to GLM package in R. When I am trying to use it on my categorical data with rating from 1 to 20, the package can distinguish my predictor as ...
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1answer
89 views

One of the independent variables in a logistic regression is the ratio of other two inputs. Is this okay?

Suppose that I have a logistic regression with continuous independent variables $a$,$b$, and $c$. In my logistic regression, $c = \frac{a}{b}$. Is it all right to include variable $c$ in the ...
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1answer
28 views

GLM or arcsine and two-way ANOVA

I am trying to analyse data on how long deer have been vigilant in a 2 minute observational period and how this varies between males and females and also whether they were in the centre or edge of the ...
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1answer
17 views

Using sample standard deviation as the dependent variable in a Gamma regression

I need to do a regression with sample standard deviation of a distribution as the dependent variable. The approximation the number of trials is the independent variable. My question is: what should ...
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10 views

How to address the problem of underdispersion in a binomial GLM (data in proportions)

We explored the effect of seed size, and seed-coat thickness on seed removal rates (proportion of seeds removed) and seed predation rates (proportion of seeds preyed upon)by rodents. Seed size was a ...
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1answer
25 views

Meaning of MATLAB logistic regression coeficients [duplicate]

Suppose that I'm using this function to implement logistic regression in MATLAB R2015a: ...
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25 views

glm and glmer power analysis in R

I am trying to do a sample size calculation for a parallel cluster randomized trial with .8 power. There are 32 clusters with 100 people per cluster. This is the data fram that goes into the glm. ...
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1answer
91 views

Why is the Quasipoisson in glm not treated as a special case of Negative Binomial?

I'm trying to fit generalized linear models to some sets of count data that might or might not be overdispersed. The two canonical distributions that apply here are the Poisson and Negative Binomial ...
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1answer
138 views

Multinomial logistic regression vs. generalized linear model?

Background While dealing with data, I very often find my data to be "halfway between discrete and continuous". For example, let's say my variable of interest is age. In principle, age should be ...
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12 views

Interpreting GLMM output in SPSS

In SPSS I carried out a linear mixed models analysis by going to Analyse - Mixed models - Generalized Linear... I know how to interpret my categorical data output (I hope) - one of the categories is ...
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16 views

Dummy regression, reference group selection, Mallows' $C_p$ criterion, correlation

I am using glm (target, formula = target~.,family=binomial) to predict binary outcome. I have 9 grouped predictors. I convert them into factors so that I can test ...
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43 views

Linear regression model vs logistic regression

I am looking at enrolling in a university paper on modelling but am faced with an option of either a paper based on linear regression or logistic regression. To be honest I don't quite know the ...
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11 views

How can I fit a GLM using R's glm() evaluated at specific values (e.g. median)? [migrated]

I would like to fit a generalized linear model in R, using glm(). More precisely, it's a nb.glm(). Apparently, coefficients ...
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1answer
72 views

Why does the linear test statistic of GLM follow F-distribution?

As a MATLAB user, I have been using coefTest to perform linear hypothesis testing. For example in $y=\beta_0+\beta_1x_1+\beta_2x_2+\beta_3x_3$, if I want to test if ...
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Interpreting scaled betas for quadratic terms in a negative binomial regression

I created a negative binomial model where the final model included 5 quadratic predictors (each with a corresponding linear term). I am considering two ways to interpret the beta coefficients for each ...
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12 views

GEE model returns GLM results

I have simulated some longitudinal data with 100 subjects and 7 measurements per subject. My data has random intercept which will induce "exchangeable" correlation matrix. My goal is to fit two ...
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2answers
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1answer
183 views

Estimating Multilevel Logistic Regression Models

The following multilevel logistic model with one explanatory variable at level 1 (individual level) and one explanatory variable at level 2 (group level) : ...
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log-likelihood of regression model with exponential distribution as response

I would like to calculate the log-likelihood of a simple regression model, where the response variable $y$ is exponentially distributed. I thought I could just use least-squares (LS) to find the best ...
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14 views

Issue in Interpreting SPSS output of GLM repeated measures

I have a data set with repeated measures for each individual. That is there a re two groups, treatment and control. And each individual is measured at 3 time points. But there are missing values. That ...
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3answers
149 views

How does the GLM handle collinear predictors?

In the case of an ordinary least squares GLM with two nearly collinear predictors, how does this shared variance get reflected in the parameter estimates? My understanding is that the parameter ...
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1answer
400 views

When to use Poisson v. Geometric v. Negative Binomial; GLM, the Three Count Variable Distributions

I'm trying to layout for myself when it's appropriate to use which regression type (geometric, poisson, negative binomial) with count data, within the GLM (only 3 of the 8 GLM distributions are used ...
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1answer
30 views

Proper use of model inference (AIC) (Burnham and Anderson) - when to explore more models

I am starting an analysis, for which I have a binomial response variable (species relative abundance) and continuous predictors (habitat variables). I have done some data exploration, and there is ...
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42 views

Compute log likelihood of data after fitting the GLM?

Please forgive my inexpertise in stats. I am playing around with a toy general linear model (GLM) with MATLAB glmfit. ...
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2answers
92 views

GLM with multiple imputation or mixed model

I have a data set with repeated measures with two treatment groups where each subject is measured at 3 time points.But the data set includes missing data. In SPSS if I use ...
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23 views

Using the linear equation with log transformed data

If I have log transformed axes and then produce a nice linear regression model. How do I use the equation of the line? i.e. Can I use my raw data $x$ values to predict real values for $y$? Is the ...
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21 views

GLM: selecting link function for log/logit

If I'm trying to link a linear predictor given by: ${\beta }'X + \varepsilon$ with the error term which follow a log-normal distribution (bounds for the variable being modeled is supported on ...
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1answer
64 views

Finding a reaction norm in R using logistic regression with binomial errors.

I am trying to calculate 'reaction norms' for a fish species. This is essentially the length at which the probability that a fish become mature equals 50% for a particular age class. I know I have to ...
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10 views

Analysis of weighted discrete distance data

I would appreciate some assistance with the following simplified version of my experiment: Imagine a row of boxes, e.g. each box is 0.5 by 0.5 m, lined up in a column of 20 boxes. At the short end of ...
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16 views

Pseudoreplications and the methodes used to explore the correlations

In my experiment I have measured growth of different trees on predefined circular plots (x, y, z, a). On each plot all trees were measured. For each location I have one treatment information. Now ...
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18 views

Interpreting GNM/GLM regression using R

I have a training dataset that contains input features. It's an input file which is tsv separated. I had input data for 200 rows with 4 cols. That last col is the target variable. I have also loaded ...
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48 views

How do statistics packages (e.g., fitglm in MATLAB, SPSS/SAS/Stata) handle mean centering for higher order terms? [closed]

Many questions (e.g., Centering in linear regression & How to include a linear and quadratic term when also including interaction with those variables? ) have been asked about mean centering (aka ...
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1answer
33 views

Python: GLM multidimensional input

I'm trying to fit a GLM model to some data. My response variable, y, is a vector of length 24, my inputs x are a 24 * 24 data frame. My code looks like this: ...
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1answer
65 views

Poisson regression for count data - predictions

This is probably an elementary error in either my understanding or my R implementation: I am trying use a Poisson model to make some predictions. The original data is discrete count data. I would ...
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1answer
59 views

Write code and explain some term in Logistic regression analysis by R

We want to do the logistic regression analysis to consider the effect of Age, CD4 on drug resistance mutations. The code that we wrote is: ...
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38 views

need overall p-value on glm binomial model

I have a glm with a binomial response variable and one binomial factor and one categorical factor. When I run a summary on this model, I get p-values for every level within the categorical factor ...
4
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1answer
48 views

pvalues of glm coefficients and heavy tailed distributed residuals

I've seen this post but I have still some additional questions. I have a ordinary linear regression model with more then 300 predictors (which represents different conditions). I want to know which ...
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20 views

Non significant Tukey's pairwise tests for glm with significant anova

I have run a glm poisson log model for counts in R and obtained a significant p value for the anova. However, my Tukey's pairwise comparisons are all not significant. What should I do to identify ...