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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3
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1answer
31 views

Logistic regression link defined in terms of $\pi_i$ and not $\mu_i$

I am confused about the terminology used when discussing the logistic GLM. When dealing with any glm, we have that for the response $Y_i$: $$ E(Y_i) = \mu_i $$ and $$ g(\mu_i)= x_i^T \beta = \eta_i ...
2
votes
1answer
13 views

How to predict from glm created with average values?

I want to predict count data (example: people visiting a beach) based on some predictors (example: temperature, cloudiness). I have created a generalized linear model (with Poisson distribution and ...
0
votes
0answers
21 views

GLM: Is it necessary to include lower-order interactions when testing higher-order interactions

It appears to be a general principle that one should include all main effect terms when modelling interactions in (generalized) linear models (as argued here, for example: Including the interaction ...
0
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0answers
17 views

Predicting the status of an individual across time [on hold]

In my case my training data frame is like the following : Id|Month|Status|Others 1|01/16|O|X 2|01/16|O|X 3|01/16|O|X 1|02/16|E|X 2|02/16|O|X 3|02/16|F|X 1|03/16|E|X 2|03/16|O|X 3|03/16|F|X ...
0
votes
1answer
17 views

Generalized model with different kinds of variables

I'm working with captive jaguars behavioral data to answer how several independent variables affect the incidence of a certain behavior "E". My dependent variable is the number of times the behavior E ...
0
votes
0answers
6 views

Compare GLM with different dependent variables (that have not the same variance)

I would like to know if it is possible to compare the effects of one predictor on different response variables that follow e.g. a Poisson distribution. I have already read this topic with excellent ...
0
votes
0answers
8 views

How to understand the constraint of loglinear modelling of zero-sum?

could anyone help explain to me how this constraint for loglinear models come about? Better with a concrete example of a contingency table? Thank you! c The context of the question: loglinear ...
2
votes
0answers
16 views

How to determine counts of viruses from limiting dilution assay

My question relates to a common experiment in the field of virology. Let me give some background so you understand why I'm asking this question. When producing virus for an experiment you typically ...
0
votes
0answers
14 views

Logistic regression in R when data when variable is a number of observations in a set of categories, not indicator (dummy variable) [on hold]

I have data in the form, let's say, factor1 | factor2 | number of observations in given levels of fctr1, fctr1. How do I perform logistic regression?
1
vote
1answer
11 views

AIC of a two-part/hurdle model?

I have continuous data with a point mass at zero, so my plan is to use a two-part model where I first model whether an observation is zero or non-zero in a logistic regression and then model the ...
1
vote
1answer
29 views

Using logistic regression to estimate whether probability of an outcome is greater than chance (and by how much)?

I have an outcome variable that is subjects' correct or incorrect responses to a single question asked at two time points (before and after the experiment). I want to know if subjects were better than ...
0
votes
0answers
5 views

Why is the parameter and the random variable swapped in this conjugate distribution pdf?

I'm reading a journal titled Claims reserving in the hierarchical generalized linear model (hglm) by Gigante, Picech and Sigalotti. In the distributional assumption for the unobserved risk parameters, ...
0
votes
0answers
32 views

Testing the linearity assumption in binomial logistic regression

I would like to test predictors (risk factors - binary and continuous) for linearity in binominal logistic regression (outcome death - binary). If I am correct, the relationship between predictors ...
0
votes
5answers
82 views

Is there a way to run this logistic regression with separation?

I want to run a logistic regression with a binary outcome (correct vs incorrect) and three predictors: condition (2 levels: A and B), and time (2 levels: before and after), and their interaction. I've ...
1
vote
1answer
22 views

Really weird results for a logistic model - is it due to high frequency of one value on response variable?

I am trying to test whether experimental group (a vs b) influences the probability of some binary outcome, but the model results are strange. The code I'm using: ...
0
votes
1answer
43 views

Level of factor taken as intercept?

I am using a GLM to analyze binomial data from one factor (Group) with three levels: Control, Control Treatment and Treatment. ...
0
votes
0answers
8 views

Compare effect sizes in GLM, in order to do species grouping

In an analysis of mine in ecology, I'm trying to quantify the effect of some crop surface on the abundance of several species. What I'd actually like to do is to see if the effect of crop surface is ...
0
votes
0answers
11 views

Having distribution of response variable instead of conditional distribution of response given observation in generalized linear regression

I'm trying to model the distribution of response variable $t$ given the observation $x$ using generalized linear regression. In order to do so, I should assume a parametric distribution from ...
0
votes
0answers
10 views

Multilevel modeling of time series coss sectional data with a binary outcome using glmer

Background Although my data should have a multinomial dependent variable, I have settled for a binary as I could not understand too much of MCMCglmm. The data is a time series cross sectional, so am ...
0
votes
1answer
12 views

Compare the goodness of fit between two GLMs that are NOT nested

as far as I understand, AIC or BIC can be used to judge the goodness of fit between two GLMs, but they two has to be one nested inside the other. If I have two models that are not nested and based on ...
0
votes
1answer
34 views

Which proportions are particularly high or low - compare confidence intervals, or logistic regression?

I have a large database of binary decisions (accept or reject), broken down by state of the applicant, so that for each state I can calculate a proportion of positive decisions. e.g. ...
0
votes
1answer
29 views

Which statistical model to use for car speed measurements ~ time of day?

I have received some data that contains measurements of vehicle speed in function of time of day. The point is to predict speed for this measure point at any given time of day. I'm using R so I tried ...
0
votes
1answer
29 views

Logistic regression categorical variable interpretation after transformed into dummy variable

Before training a glm model (in R), predictors were transformed into matrix and highly correlated/near zero variance variables were excluded: ...
0
votes
0answers
13 views

What's the unbiased way to estimate two categorical covariates effect in GLM when they are correlated

I have a data set with presence/absence as response. Treatment (T1 and T2) is conducted at four different sites (A, B, C and D). However, the treatment is not randomly assigned to sites, sites A and B ...
0
votes
1answer
34 views

Generalized linear mixed model - glmmADMB - date as random effect

I have a couple related questions about using a generalize linear mixed effects model to analyze data from an agricultural field experiment. I have found several posts that are similar to this ...
0
votes
1answer
22 views

Can I use a binomial model with logit link function when dealing with continuous proportions?

I have continuous proportion data (i.e. ranges from 0 to 1). Or it could be percentage too if multiplied by 100. This is the proportion/percentage of overlap in home range between animals, therefore I ...
0
votes
0answers
7 views

Multivariate regression, non-normal response variable

I am attempting to run multivariate regression model with interactions terms to understand the combined effect for categorical variables and 2 other continuous variables. My response variable is ...
0
votes
0answers
16 views

Normal vs. non normal distributed data. Choice of model

I have a question about normally distributed and non normally distributed data. I am going to do a genotype association analysis but I want to build a model for each trait that I have to know which ...
0
votes
0answers
9 views

Is it possible to test the difference of two regressors in a GLM in matlab?

In matlab, the function fitglm can be used to fit a GLM and test the hypothesis that the weight of a regressor is not zero. However, is it possible to use the result for post hoc multiple comparisons ...
0
votes
1answer
29 views

How to show probability satisfies probit model

This is an old past paper question that I am struggling to understand, so any help or hints would be appreciated... Consider the choice between two options, such as two product brands. Let $U_0$ ...
0
votes
0answers
5 views

Probit model rate of change

Consider a generalized linear model with a binary response variable Y and a predictor variable X and a probit link function. So the probability of success, $\pi(x)$, has the form ...
0
votes
0answers
12 views

What family of GLM when response is Bray-Curtis dissimilarity? Should I use “adonis” instead?

I have some questions about testing for effects of different experimental levels on community similarity. I'll explain my planned experimental design before I ask the questions: I have two methods ...
0
votes
0answers
26 views

Constructing A Simple Hypothesis

In the book Applied Longitudinal Analysis, 2nd Edition there is an example in the chapter "Marginal Models: Generalized Estimating Equations (GEE)" in "Muscatine Coronary Risk Factor Study" ...
0
votes
0answers
20 views

z-score to compare coefficients between two regression models

I conducted a choice-based conjoint analysis to find the preferences towards smartphone features. To estimate the model parameters, I used the generalized linera model (glm). I have used 6 levels in ...
0
votes
0answers
11 views

How to deal with different distribution families in multivariate regression?

Say that I have 2 dependent variables, one continuous and one count: amount of adrenaline (continuous) number of remembered digits in a task (count) and I want to check if these two variables ...
2
votes
2answers
56 views

What are the error distribution and link functions of a model family in R?

When building models with the glm function in R, one needs to specify the family. A family specifies an error distribution (or variance) function and a link ...
0
votes
0answers
42 views

Why are the both of two models' AIC the same?

I would like to ask a question of AIC when we use Generalized Linear Model with R. I show you 4 my models. "x" is continuous variable. "f" is categorical variable and has two levels, C and T. "x*f" ...
0
votes
0answers
4 views

Should you use converted currency when fitting a Generalized Linear Model [migrated]

So long story short, my data set has a bunch of currency values (ie: Dollars, Euros, Pounds) over the span of several years. The original idea was that I should use a daily conversion rate to ...
0
votes
0answers
19 views

R linear model where data points are grouped/correlated

I am attempting to build a linear model where I technically have multiple responses for each observation. I am working with corn field data. Management practices for each field were recorded like ...
0
votes
0answers
22 views

Expectation of y given u when it follows Poisson distribution

So I was reading Generalized Linear Models with Random Effects by Youngjo Lee, in chapter 6 about Hierarchical GLMs there's this example: Suppose $y|u$ is Poisson with mean $\mu = E(y|u) = ...
0
votes
0answers
28 views

Intuitions from standard deviation to p-value?

A cake is specified by a 3-vector $x=(x_1,x_2,x_3)$, where $x_i$ is the percentage of the $i$-th ingredient, and therefore $\sum_ix_i=1$. I have a price list $y$ for different $x$'s: ...
0
votes
0answers
21 views

How do I analyze “Analysis of deviance” in R?

I ran an ANOVA in R in the library (mvabund) which resulted in an output table "Analysis of Deviance", and I am confused as to what the "DEV" means in the output below. I am including here part of the ...
0
votes
0answers
13 views

Incorporating shifting spatial autocorrelation into a GLMM

So I'm examining a series of sites across a landscape for how wildlife use of these sites changes following treatment (reclamation). Treatment of these sites randomly took place over three years, and ...
1
vote
1answer
24 views

linear model to predict pairwise differences in R?

I would like to set up a model where the predictor variables and response variables are pair-wise differences between the subjects. More specifically, I have a set of biological populations and I want ...
0
votes
0answers
11 views

What does fixed regressor say about our linearity condition?

The linearity condition states that $\mathbb{E}[y_i]=(\vec{x}_i)^{T}\vec{\beta}$ for all $i$. Now, if we have fixed regressors, $\{\vec{x}_1,\vec{x}_2,\cdots\}$, our linearity condition only says for ...
4
votes
3answers
244 views

Linear probability model, dummy variables and the same standard errors on all estimates

I am fitting the linear probability model, $$ Y_i=\beta_0 + \sum_{j=1}^J \beta_j G_{ji} +\varepsilon_i $$ where $Y_i \in \{0,1\}$ and $G_{ji} \in \{0,1\}$, for $j=1,\ldots,J$ and $\sum_{j=1}^J ...
0
votes
0answers
15 views

Estimating unconditional effects in double hurdle models

I trying to implement a double hurdle model in JAGS and am struggling to understand how to estimate the unconditional effects of each predictor variable on the count process. I have implemented the ...
6
votes
2answers
365 views

How does OLS regression relate to generalised linear modelling

Can anyone please shed some light on the relationship between OLS and generalised linear model? Has it to do with the distribution of the error terms, general linear model requires normality in the ...
1
vote
0answers
22 views

Categorical variable intercept in generalized linear model

I am running Generalized Linear Model and I have one continuous dependent variable, two categorical fixed factors and 22 continuous independent variables as covariates. When I run the model, I get ...
0
votes
0answers
13 views

Poisson regression residual analysis

In a three factor poisson (log-linear) model $(A*B*C)$, when the highest interaction term $(A:B:C)$ is dropped, the response/raw residuals are exactly the same for different levels of two of the ...