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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27 views

Best way to account for time lags in logistic regression (GLM or GLMM)

I am trying to determine the best, most conservative way to account of time lags in a logistic regression type analysis (a generalized linear model with or without mixed effects). I am working with ...
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3answers
51 views

Interpreting random effect variance in glmer

I'm revising a paper on pollination, where the data are binomially distributed (fruit matures or does not). So I used glmer with one random effect (individual ...
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0answers
19 views

How do you use mathematical notation to write out an equation for a GLM with a gamma distribution and a log link?

Basically, I'm trying to estimate a GLM in Stata with a log link function and a gamma distribution and I need the mathematical notation behind the data analysis. My model has a price variable as its ...
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0answers
14 views

Estimating treatment effects on unequal groups

The setup is quasi-experimental: treatment and control groups, chosen ahead of time, are given a pre-test and a post-test. They are close to equal in size, and while membership was selected based on ...
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0answers
18 views

Find sample size of all interactions for cross classification GLM in R [on hold]

Suppose that I have a response variable called A and four explanatory variables that are all factors (all with at least 3 levels) called B, C, D, and E. I know that the total sample size of my data is ...
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14 views

Getting LS means of the response from logistic regression in SAS

So, I know this is more of a programming question than a stats question, but I thought I might try here anyway. I have a logistic regression model with a combination of categorical and continuous ...
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0answers
12 views

Must the response variable be gamma distributed to appropriately use a gamma-log model

I'm responsible for challenging a gamma model with log link. The developer claims that an assumption of the gamma-log generalized linear model is that the response variable, in this case average ...
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1answer
13 views

Clarification on using train vs glm vs rpart for classification problems in R

I am using the glm function in R to perform logistic regression. I converted the outcome variable to a numeric between 0 <=y <= 1 as follows ...
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16 views

K-fold cross validation for a glmer model with nested data

I'm working on a data set that contains a hierarchical data structure (i.e., GPS locations nested within individual animals). I'm using a generalized linear mixed effects modeling procedure (lme4 ...
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22 views

Fitting a GLM for binomial data in R

I am an R beginner and out of my depth! I am trying to build a model to analyse data regarding fertility in 2 populations of different levels of sexual selection(M and P), both of which have undergone ...
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0answers
24 views

How to return score vector after glm in R? [migrated]

I am trying to get the score vector (first order derivative of log likelihood) from probit in R. Here is my sample code: ...
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1answer
40 views

What are good resources to learn about GLM? [duplicate]

Generalized linear models (GLM's) are apparently widely used, but I'm having some trouble to find comprehensive but still simple resources to explain it to someone who is not a statistician but has a ...
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1answer
21 views

Interpreting GLM Interaction Contrasts in R (using glht)

I am trying to do a BACI analysis on count data, and I am having trouble interpreting the output from multcomp::glht. I don't understand how the contrast's coefficients are related to effect size ...
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13 views

How does GEE (Generalized Estimating Equation) treat different cluster size?

I have a population of 200, 000+ patients and their hospital visit information. I'm trying to see if having a certain disease would have an effect on whether they will have readmission or not (this is ...
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0answers
11 views

How do I relate the estimates of a Logit Binomial and Log Poisson model where the same response counts are used

I am looking for any material that explicitly links (ie formulae) a Logit binomial GLM model where the response is counts of the outcome as proportions of some varying denominator (number of trials) ...
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0answers
14 views

glm using high dimensional data

glm using high dimensional data Hello all. I am new to all of this. I am trying to use GLM in R to do a logit regression. I have a high dimensional data set (each datapoint/vector has about 1000 ...
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1answer
34 views

What measures the y-axis in stat_smooth, ggplot2 in R? [closed]

I am fairly new with logistic regression. I have a binary response. And did this plot. The binary response is: Y = 0: The student fails Y = 1: The student succeed ...
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15 views

GLM model Interpretation

I am interested in the interpretation of a GLM model (my output is from SPSS) I will take the following example: Suppose that we have a study of alcohol consumption and cirrhosis sufferers. We have ...
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0answers
10 views

Generalized linear models - Generalized linear mixed models

I am using R and I have to perform a binomial GLM with random effect. The only problem is that I have too many predictors and also some significant interactions between them. The model is very large, ...
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0answers
26 views

Inverse probability weighting in logistic models - large weights irrelevant when using additional covariates?

I am using propensity scores for IPW in a logistic GLM in R. Two of the propensities are quite small and thus the resulting weights are quite large - much larger than all the others. I expected these ...
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0answers
31 views

Poisson regression: loglinear vs linear

In Poisson regression, there are two possible ways we can relate the dependent variable $y$ with the independent variables $x$: $E[y|x] = w^Tx$ $E[y|x] = e^{w^Tx}$ The likelihood functions are: ...
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0answers
10 views

Difference between binomial, binomial() and 'binomial' [migrated]

What is the difference between binomial, binomial() and 'binomial' when using glm. They are not identical, as can be see by following code: ...
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2answers
35 views

How can I make this biological relation into a glm model?

I have a biological relation: Y/m = (X * b) / (1 + X * b) where Y and X are variables, m and b are parameters. m is greater than Y, and everything is greater than 0. I have some training data with ...
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0answers
17 views

How does non-linear least square in R do inference?

Looking for some validation of a conclusion I've made. I have a binary variable Y and a feature vector X. I want to build a classifier with the logistic regression model. In R, if I fit a binomial ...
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0answers
11 views

OddsRatio in Glimmix

I'm using Proc Glimmix with a poisson distribution specified for my count data. How can I get the odds calculated for my results - since ODDSRATIO does not working with a log link function?
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15 views

Effect size for two-sample test with complications such as multiple data per subject

I have a simple hypothesis, that my dependent variable depends on a two-level factor Condition. Ideally I would like get a measure of Cohen's D, with confidence interval. The problem is my model is ...
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1answer
18 views

Post-hoc test after a GLM with binary data

I have run various logistic regressions (GLM) from the binomial family and they have produced some very interesting results. I would now like to run post-hoc tests to find out which levels of the ...
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9 views

Distribution of dispersion submodel

In double generalized linear models where we assume $y$ follows an exponential dispersion model, where the mean can be modelled as $$g(\mu_i)=x_i^T\beta,$$ and the dispersion $(\phi)$ can be modelled ...
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0answers
22 views

Glmer random effects model vs. dummy-coded fixed effects

I'm trying to analyze the data from an experiment I conducted, and could use some guidance in relation to fixed vs. random effects. The experiment was related to risk-seeking behavior in the context ...
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0answers
17 views

what method for regression on pourcent

I would like to predict/model a repartition between 3 classes (A,B,C). I have a pool of person, and for each pool I have a repartition : A+B+C = 100% this could be my dataset : ...
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1answer
29 views

How to relate water quality scores to land use percentages?

I have a question related to application of a linear mixed effect model. I have land use data in percentage, which is the predictor, and a water quality score (e.g. 100) as response variable for 100 ...
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1answer
24 views

GLM Model checking Plots - Quasi Poisson - Poisson

I wonder whether accounting for overdispersion in a GLM (Quasi - Poisson instead of Poisson family) has an effect on the model checking plots (plot of residuals against fitted values, a scale–location ...
2
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1answer
38 views

So many significant explanatory variables and so small auc

Have you ever seen a model with almost every significant variable and such small auc (area under the ROC curve) ? What might be the cause of it? When I saw summary of a model I thought this model will ...
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53 views

Fitting a Bayesian Hierarchical Poisson Regression in R

I'm trying to fit a Bayesian hierarchical poisson regression. To do so, I'm using MCMChpoisson function from MCMCpack in R. Based on this package, the model is: $$Y_i \sim Poisson(\lambda_i)$$ ...
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20 views

Multiple starting value/convergence warnings when running glm.nb in R

I'm running a negative binomial GLM in R. Running the model with the pooled dataset works just fine, but I'm encountering several warning messages when I attempt to run the same model for some of the ...
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0answers
25 views

distribution of residuals in logistic regession

I am fitting binary outcome using generalized linear mixed model (glmm). I checked the Studentized and Pearson residual and they do not seem to be normal. Is it expected that residuals in logistic ...
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0answers
6 views

Discordant significance of OR’s confidence interval and p-value in glm(quasibinomial) model [R]

I’m currently trying to test if differences in proportions of people infected by malaria (RDT positive) between clusters with high or low coverage of control intervention are significant. Therefore ...
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0answers
33 views

What is the difference between Average Partial Effects (APE) and Average Marginal Effects (AME)?

In this answer, the terms Average Partial Effects (APE) and Average Marginal Effects (AME) are used interchangeably. But in this paper, the terms are used to mean different things (page 75). But it's ...
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0answers
8 views

Bootstrap Step-Wise Selection For GLM in R [migrated]

What I'm trying to do is the following: Resample (bootstrap) the dataset containing my predictors and outcomes, say 1000 times For each bootstrap sample fit a glm using stepwise selection and then ...
3
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1answer
64 views

Testing if the difference between two count variables is different from zero

I have two count variables for several hundred thousand comparisons, one expected and one observed, and I would like to test if the counts are significantly different. One possible approach I have ...
2
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2answers
42 views

Is parametric equivalent to linear?

Some supervised learning techniques, such as GLM (e.g., logistic regression), are linear and parametric. On the other hand, one of the claimed advantages of nonparametric supervised learning ...
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1answer
25 views

Can we write the likelihood of a GLM in generality?

So I know we can explicitly write down the likelihood of any specified GLM model, for example the likelihood for the logistic regression model would be ...
1
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1answer
75 views

glm model fit - can't find a family/link combination that produces good fit

I am having difficulty finding a correct glm model to fit my data. The outcome is the length of time in months a person will spend in prison (sentence length). It's technically a count, all positive ...
1
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1answer
48 views

Discrepancy between logistic regression and logistic regression results?

Suppose I have a data set of 200 controls (group A; has no memory problems) and 100 cases (group B; has memory problems). And I'm looking at the relationship between memory and cognitive test score ...
3
votes
1answer
166 views

What's the right interpretation for parameter estimates in loglinear modelling?

I'm doing a loglinear analysis of the following data. Male is coded as 1, Female as 2. Senior workers are coded as 1, middle level as 2, and shopfloor as 3. A is coded as 1 and is the most ...
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0answers
23 views

Poisson/Negative Binomial/Gamma log-link for continuous dependent variable (scale DV)

In my research about sport injuries in football, I am trying to obtain Incidence Rate Ratios (IRR) comparing my categories with the reference category. I have number of days a player was absent due to ...
2
votes
1answer
42 views

Model to predict categorical outcome from continuous and categorical variables

I have to fit a model to test whether Learning (1=learned, 0=failed) depends on lizard sex (M or F), Lizard SVL (snout-vent length), or an interaction of the two. I am new to both R and this website. ...
2
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1answer
59 views

Best way to deal with heteroscedasticity in R?

Originally posted on stackexchange but I was told that it fits better here. I have a plot of residual values of a linear model in function of the fitted values where the heteroscedasticity is very ...