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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Selecting the best GLM (generalized linear model)

GLM (family=binomial) is foucusd on when the response is dichotomous(yes/no, male/female, etc..). I'm wondering how to judge if the model we built is good eough? As we know, in OLS regression some ...
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2answers
191 views

Interesting Logistic Regression Idea - Problem: Data not currently in 0/1 form. Any solutions?

I am attempting to conduct a logistic regression for a tennis analytics project, endeavoring to predict the probability of a player winning a point in which he is the server. My response variable ...
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1answer
18 views

Categorical explanatory variables in Poisson regression

I want to perform a Poisson regression to explain Abundance (Counts of individuals) through a number of continuous and categorical explanatory variables. Some of the categorical variables have more ...
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10 views

Multiple categorical and continuous variables in ANCOVA

I have performed an experiment where insects in a certain order were captured and identified at set distances from a forest boundary. At each point I have measured a number of environmental variables ...
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14 views

Determinin fixed effects' contribution to variation with ANOVA results

I have performed an ANOVA (Linear model in SAS EG) to determine the role of country, farm, sex and year-season on performances of pigs and ran pair-wise bonferroni tests on country, farm and sex (LSM ...
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16 views

Error message in diagnostic plots of GLM in R

I created a generalised linear model in R: mod10 <- glm(RRBEE ~ SNH3000 + PAG1000 + SR250 + C250 + HE250, data = data1, family = poisson) I ...
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9 views

Use of R function “visreg” on GLM object when applying an offset() in the GLM calculate response variable [on hold]

I am modelling effects of forest age and topographic exposure (Meso) (independent variables) on forest species richness (count data; dependent variable) using GLM (poisson family) in R. Each datapoint ...
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28 views

Largest set of numbers qualifying Exponential distribution [on hold]

For a given set of numbers $ Y = \{ .... \} $ and a given exponential distribution expressed by $ f(y; shape, scale, family )$, there should be a subset of numbers that can be derived from $ X \subset ...
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1answer
35 views

Logistic Regression modeling in R

Consider this model: $Y_i$ ~ Bernoulli($\pi_i$) $X_i$ = 0,1 logit($\pi_i$) = $\lambda^{X_i}$ * $\beta_0$ This model simplifies to logit($\pi_i$) = $\beta_0$ , when $x_i=0$ , and logit($\pi_i$) = ...
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8 views

Comparing hurdle models to negative binomial models

I'm trying to compare the AIC or log-likelihood of a negative binomial GLM to a hurdle type approach, consisting of a binomial GLM for the presence/absence of a count and the counts modelled with a ...
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1answer
53 views

Does one need to transform percentages/proportions for a multiple linear regression?

I am aware that one should transform percentages and proportions when using them in an ANOVA, due to the values being bounded by 0 and 1. I have seen suggestions that the best transformations are ...
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39 views

Calculating Odds Ratios from R output

I've fitted a GLM with a binomial link function in R, and need to interpret the result of the fit. How do I calculate the Odds Ratio for country 2 relative to country 1? Also, how do I calculate the ...
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13 views

A regression test like a glm, but measures the difference in betas

Like the title says, is there a regression test, similar to glm, that says whether a beta coefficient is (statistically) significant based on its difference from the Intercept? I'm currently doing a ...
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23 views

Modelling overdispersed counts - past negative binomial

I'm modelling overdispersed counts. I began using a GLM with Poisson error structure, then moved to quasi-Poisson, and then finally negative binomial. The residuals versus fitted values plot is still ...
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1answer
48 views

Do I cross-validate my entire dataset, even the validation and test set?

I have the following dataset where binary_peak is a binary response variable and I have (not shown) 9 explanatory variables (also binary). ...
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36 views

Inconstant logistic regression coefficients each time algorithm is run [SOLVED] [closed]

I'm running a logistic regression to find a relationship between falls and drugs taken by someone. What happens is that every time I re-run the algorithm it gives a different result. The table is ...
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1answer
157 views

Difference in output between SAS's proc genmod and R's glm

I'm trying to replicate a model fitted in SAS in R but the fit I'm getting gives me slightly different coefficients and standard errors. Data: ...
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12 views

Polynomial ANCOVA glm in R

I have a data set of success and failure counts with one continuous independent variable and one factorial variable: ...
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16 views

Is there a way to do logistic regression model selection of up to 5 variables each from a pool of ~70 variables

I'm trying to determine the best logistic regression model to estimate the probability of 0 in streamflow rates. My response for the glm object is one vector of the sum of all the days the recorded ...
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1answer
14 views

GLM Dependent Variable Incorporating Independent Variables?

A bit of a newbie statistics question. In a GLM, would it be sound to use a dependent variable that incorporates one (or more) independent variables? For example, if my dependent variable is ...
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11 views

How do I assess seasonality with a glm using a cosine curve in r?

I'm new to R, so I'm sure I'm just doing something dumb. I have a dataset of kereru counts from two different decades, the 80s and 2000s. I am looking to describe how kereru counts have changed ...
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9 views

Effectiveness of bio-security measures

I want to find out whether certain farm bio-security measures are effective in the control of a disease(dependent variable with binary outcome). My independent variables contain both categorical and ...
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26 views

Simulation of logistic regression power analysis - output given

This question is in response to an answer given by @gung in regards to this question I am also wanting to use simulation to conduct a power analysis on a multiple logistic regression. To keep it ...
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1answer
17 views

A model for technical measurement data with many zeros - pros and cons of Tweedie

I analyze technical measurement data with the aim of developing a forecasting model. The data is given as a non-negative time series (data per hour). The data looks quite wilde and contains many ...
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1answer
28 views

spike and slab models

Kevin Murphy discusses in this book (http://www.cs.ubc.ca/~murphyk/MLbook/index.html) the spike-and-slab model. I am struggling to understand the prior linked to this model. Why, if $\gamma$=0, and we ...
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9 views

Hypothesis testing in GLMMs - How to set up sequential LRTs

I'm fitting a generalized linear mixed effects model to my data. I have three fixed effects, and one random effect nested within one of the three fixed effects. The response variable is a count, ...
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1answer
71 views

naming convention: what does GLM stand for?

I encountered the term "Global Linear Model" which has the same abbreviation as "Generalized Linear Model". Since I couldn't find useful information on the former, my question is whether the two ...
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2answers
70 views

How to fit a glm with sum to zero constraints in R (no reference level)

Question has been rewritten I am trying to fit a glm to find out how the rate of events happens (counts/exposure) related to some covariates, with Poisson error. Counts is the number of events ...
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1answer
21 views

What are the different frameworks for solving regression problems in Machine Learning (like GLMs)?

I'm going through Andrew Ng's lecture notes on Machine Learning. The notes start with introducing linear regression and intuitively explaining what the cost function for the problem should be and how ...
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1answer
74 views

How to specify logistic regression as transformed linear regression?

I am trying to reproduce the following example of logistic regression with a transformed linear regression: ...
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1answer
53 views

Is my understanding of Generalized Linear Models correct?

I'm going through Andrew Ng's lecture notes on Machine Learning & I just learnt about Generalized Linear Models there. I want to check if I know correctly what Generalized Linear Models are. ...
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Deriving Linear Regression update rule: Why do we set the derivative of cost funtion to 0 to minimize it?

(This question may be more about Math in general but I have a specific context in mind and hence am asking this here as opposed to math.stackexchange) I'm going through Andrew Ng's lecture notes on ...
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74 views

Mixed effects model for longitudinal data

I have the following data: a group of patients for which I measure blood pressure (bp) 1 hour before treatment where they are given a placebo, then during treatment with the drug, and then 1 hour ...
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190 views

How many distributions are in the GLM?

I've identified multiple places in textbooks where the GLM is described with 5 distributions (viz., Gamma, Gaussian, Binomial, Inverse Gaussian, & Poisson). This is also exemplified in the family ...
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3answers
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Is setting lambda equal to zero the same thing as not applying regularization at all?

If I set the regularization parameter to 0, does it essentially mean I'm not applying regularization (I've boxed the regularization bits in red)? Also, what is this type of regularization called?
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Which test-statistics do I use if I just want to test one particular regression coefficient

Hello my Question is this. I want to test a model which has the form: $Y_{i,j}= \beta_0+\beta_1 I_{treatment}+ \beta_2j+\beta_3j*I_{treatment},$ (1) where $i$ is the Identifikation Number and $j$ is ...
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1answer
42 views

Interpretation of quasibinomial glht (Tukey) results

I'm analysing chick survival between 3 different years using a glm with quasibinomial error structure. Hence, my response variable is a cbind of fledged chicks and dead chicks, and one of my ...
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1answer
32 views

lsmeans analysis is creating data that does not exist in my input file

Good afternoon, I am trying to see if the expression of a given gene (conc_ul) is explained by 3 categories (Sample, background and Experiment). Initially I am trying to check which is the most ...
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1answer
88 views

How to compute the residual standard deviation from `glmer()` function in R?

I want to extract standard deviation of residual from glmer() function in R . So I wrote : ...
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1answer
20 views

Getting a specific X from a logistic curve

I have data that can be fit, more or less, by logistic growth functions. Hence I used this tutorial to do this. Now I want to get an x value for a specific y value from the model. Maybe this is too ...
2
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1answer
20 views

Backtransform coefficients of a Gamma-log GLMM

I am analysing data from an exclosure experiment, this means for several years, goats were kept outside a fence and inside the fence, plants could grow without being grazed. Outside the fence, grazing ...
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31 views

Implementing GLM for a poisson model using matlab function fminunc

I am trying to simulate a linear non-linear poisson model. The problem is that as far as I know, the log likelihood function in this case should be a convex one, but that does not seem to be true, ...
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7 views

Softmax Regression Large Inner Product Float Overflow

In softmax regression, the probability $P$ that an item is part of class $l$ is given by $$P(y^{(i)}=l | x^{(i)};\theta)=\frac{e^{\theta^Tx^{(i)}}}{\sum_{j=1}^k e^{\theta^Tx^{(j)}}}$$ I have ...
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1answer
16 views

weighted glm model selection

Can AIC values between different weighed models be compared to select the best model (ie the model with the lowest weighted AIC)? For example, if my response variable is the 'Average Sales Per ...
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35 views

Accounting for overdispersion in binomial glm using proportions, without quasibinomial

I am doing binomial GLM using relative abundance, for example: model<-glm(cbind(number_pres,number_abs)~Var1+Var2+Var3+Var4..., family=binomial, data=Data). My sample size is about 700, and I have ...
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1answer
33 views

Generalized Linear Model for Weibull distribution

Consider the Weibull distribution with parameter $\theta$, fixed $\lambda$ and p.m.f : $$ f_Y(y)=\frac{\lambda y^{\lambda -1}}{\theta^{\lambda}}\exp(-(\frac{y}{\theta})^{\lambda}) $$ It can be shown ...
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47 views

Should I distrust the G.O.F for a logistic regression with weights perfomed with lrm {rms}?

General question When I perform a logistic regression using lrm and specify weights for the observations, I get the following warning message: Warning message: In lrm(Tag ~ DLL, weights = W, ...
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1answer
48 views

How to interpret the output of Generalised Linear Mixed Model using glmer in R with a categorical fixed variable?

I have computed GLMM using glmer in R. My response variable is species richness and my explanatory variable is grazing treatment (with three categories: cattle, sheep and ungrazed). In the model I ...
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12 views

multivariate general linear model

If I want to test if losing control over eating during pregnancy (my independent variable) can predict birth weight (dependent variable) and to do that I did general linear regression. Now if I want ...
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7 views

What is the mathematics behind the GAM prediction intervals?

From the R gam function available in the gam and mgcv package there is the option to obtain ...