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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Is mean cetering required in regression? if so, what does it do?

Let say we have a dataset, $\mathbf{X}$ of $m$ instances, and $n$ features, and a target scalar variable $\mathbf{y}$ ($m$ instances). Now I want to do a regression so, I try to fit a hyperplane $ y ...
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how to construction glm poisson , which is y and x to be used in R [duplicate]

The Independent newspaper tabulated the gender of all candidates running for election in the 1992 British general election (Table 2: The gender of candidates in the 1992 British general election ...
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35 views

how to construction glm poisson Y and X [on hold]

The Independent newspaper tabulated the gender of all candidates running for election in the 1992 British general election (Table 2: The gender of candidates in the 1992 British general election ...
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Data analysis : replication, pseudoreplication and mixed models

I have several questions concerning analysis of data, especially when there are replications and/or pseudoreplications. First, I read an example in « pseudoreplication is a pseudoproblem » where we ...
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GAM and ordered probit regression in R

I want to check the fit of the ordinal probit regression model with the help of GAM (generalized additive models). There are plenty of examples how to construct GAM for dichotomous responses in R. ...
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43 views

Basic question about getting esimated probabilities in R

I've got what I think is a fairly basic problem. I'm not sure if it is a conceptual question or software question, but I'm fairly new to using R and these kinds of stats, so it could be either or ...
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18 views

The way to evaluate the importance of an independent variable in a regression model

In a regression model, like y~( x1, x2, x3). Is there a test or a way to evaluate which independent variable, x1, x2, or x3, is ...
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1answer
28 views

Is it acceptable to transform data for use in a GLM using Poisson? [duplicate]

I have transformed my explanatory variables to a normal distribution as these variables include, proportions (logit transformed) and non normally distributed data (various transformations). The ...
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1answer
24 views

Specifying the LHS for a proportional-hazards survival regression

This is a basic question to understand how datasets for survival analysis are constructed. I understand the terms in the model, given by this equation: (P.41, G. Brostrom, "Event History Analysis ...
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1answer
100 views

Low sample size: LR vs F - test

Some of you might have read this nice paper: O’Hara RB, Kotze DJ (2010) Do not log-transform count data. Methods in Ecology and Evolution 1:118–122. klick. Currently I am comparing negative binomial ...
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1answer
30 views

Evaluating a binomial (success vs. failure) glm

I'm familiar with (some) approaches to evaluating the fit (or accuracy) of a binary (logistic) model (e.g. AUC). Are there methods/approaches that are particularly well-suited for a binomial (success ...
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1answer
20 views

Finding Bias$(s^2)$ in incorrect linear model

I am unable to find the bias of the sample variance estimator in this problem. Unfortunately I keep coming up with Question: Suppose that the true linear model is $y = X_1\beta_1 + X_2\beta_2 + ...
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2answers
114 views

What makes a GLM Hierarchical?

Wikipedia defines a Hierarchical GLM as: Hierarchical linear models (or multilevel regression) organizes the data into a hierarchy of regressions, for example where A is regressed on B, and B ...
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Mean centering variables NOT in a moderation/interaction term [duplicate]

I am trying to assess the impact of multicollinearity in a regression because I have two separately measured variables which have the reversed signs problem (one predictor is +b regression weight, the ...
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1answer
239 views

Nonlinear vs. generalized linear model: How do you refer to logistic, Poisson, etc. regression?

I have a question about semantics that I would like fellow statisticians' opinions on. We know models such as logistic, Poisson, etc. fall under the umbrella of generalized linear models. The model ...
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2answers
100 views

Simplifying variable effects in a GLM in R

Apologies, but it looks like my question is off topic for this forum. Thanks for all the excellent replies though. For those who have come across this question if they've been looking for something ...
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Model Specification tips for increasing the power of a GLM model with small sample size? (SAS)

I am working on a model that uses a wide variety of categorical predictors vs. a continuous dependent variable. Unfortunately, my usable n ends up being 28. I am having difficulty getting significance ...
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1answer
15 views

Model averaging when linear and quadratic effects are modeled in a global model

I am trying to derived estimates of model-averaged parameter effects on a fairly complicated set of models using an information-theoretic approach. I have several models that investigate continuous ...
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1answer
23 views

Plot one predictor and its quadratic term versus response variable (GLM binomial distribution)

I have the following model with four independent variables: Model_A <- glm(GRSP~ppt+tem+density+land+I(land^2), family=binomial()) When I plot the variable ...
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Logistic regression variance [duplicate]

So far I have checked the tolerance value, VIF and condition indexes. But checking the variance of the regression coefficients I have to wonder: how little variance of the regression coefficient ...
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2answers
34 views

Beta regression

I have a data set where the response variable Y is a rate between 0 and 1, where the histogram of Y is bimodal. So I feel the linear regression is not suitable.s I have been reading papers about ...
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Standard errors for the CV error curve using the boot package

Does anybody know how to obtain the standard errors for the CV error curve using the boot package? I understand the boot package can compute the K-fold CV for a fitted model, but I'd like to know if ...
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1answer
36 views

Is the linear probability model generalisable to ordered logit/probit regressions?

I have a set of data where the dependent variable is an ordered response with 7 levels and I've fitted an ordered logit model to the data, and now I want to conduct some robustness checks on the ...
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GEE with exchangeable working covariance vs. GLM and using Clustered Robust standard errors?

I'm analysing a dataset including 100 individuals. These 100 individuals provided self-reported depression sores on equally spaced 4 occasions (every three months). The main independent outcome is ...
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1answer
43 views

Using ordered factor as predictor in R [duplicate]

This is a really simple problem I am having, yet for the life of me I can't find a solution searching around. In theory I can simply recode the data, but that is an extreme solution I would rather not ...
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1answer
18 views

Predict after using Box Cox Transformation

I am doing a Multiple Linear Regression on a data set where: The response variable is continuous One of the explanatory variables is continuous and the rest are binary(categorical) 1 if it is there 0 ...
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1answer
33 views

Make sense of contrast in general linear model (GLM)?

I understand how to make sense of the design matrix in a general linear model (GLM). Basically, each column of the design matrix describes one condition under which the data are observed. For ...
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1answer
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Mixture of binary or multinary columns in design matrix?

I am designing the design matrix for my general linear model (GLM). Besides the dummy constant column, I wish to have 4 regressors (columns) in my design matrix. They are diagnosis, age, gender, and ...
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Can I model standard deviations in a linear model?

Is it possible to put standard deviations or variances into a linear model, as the data to be explained? I have a predictor which I think will linearly increase the standard deviation of a measure, ...
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21 views

Linear model with longitudinal data, predicting difference

I have a set of data for 2 visits in patients and I would like to see whether there is a effect of a difference of one variable on another. So, lets say, my variables are A, B, age + gender. I want ...
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1answer
43 views

Is it possible to get a covariance matrix of fitted values for a GLM model in R?

I would like to get a covariance matrix of fitted probabilities for a logistic regression model in R. I would like to do this because I want to find the variance of the difference between the two ...
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2answers
170 views

Can correlated random effects “steal” the variability (and the significance) from the regression coefficient?

I have time-series count data $N_{i,j}$ (population sizes in site $i$ and year $j$) and I want to correlate year-to-year changes with the environmental conditions $x_{i,j}$. For this, I am fitting ...
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24 views

How to apply transformations to the predictors of a GLM?

This post discusses why we need to transform $Y$ before estimating the predictors exponents in order to reduce the problem to a linear fit. The example builds on $Y$ log-normal. In the case of a GLM, ...
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2answers
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How to calculate confidence intervals in a GLM using the profile likelihood?

I've been trying to better understand how JMP does regression and associated models. I can compute the correct parameter estimates for a GLM, by using iteratively re weighted least squares. But now ...
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40 views

Can someone look at my method for fitting a GEE to my data?

I’ve been doing some statistical analyses in R on some data. It’s for use in a manuscript I’m hoping to get published in a biological journal. Unfortunately, the tests I ended up having to run are ...
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1answer
24 views

GLM link function for bimodal probit fitting?

I am trying to model a set of data I have physical reason to believe can be represented by a bimodal normal cumulative distribution function (Technically it is a bimodal log-normal CDF, but I think I ...
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Why do my boostrapped CI's (using boot.ci in R) not include the point estimate?

I'm interested in estimating an average treatment effect $$ \operatorname{ATE}\left(A', A''\right) = \mathbb{E}\left( Y\ |\ A'' \right) - \mathbb{E}\left( Y\ |\ A' \right) $$ with a generalized ...
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23 views

Best way to examine mortality tables?

I have a set of tables containing mortality rates (hazard rates) and I want to see how well these values reflect the influence of the covariates (age, sex, issue year, etc.). I also have actual ...
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47 views

Random variables of mixed models

I am thinking about using mixed models as part of my research, but I am having trouble understanding its application. In particular, I have two somewhat related questions regarding mixed models. ...
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0answers
11 views

Fisher information and asymptotic covariance matrix [duplicate]

I am reading the Categorical Data analysis by Dr. AGRESTI. Here, it explains "The liklihood function of for the GLM also detemines the asymptotic covariance matrix of the ML estimator Beta_hat. This ...
2
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1answer
43 views

When to use zero-inflated poisson regression and negative binomial distribution

I have a fairly simple dataset looking at the relationship between the first nesting date of a bird in a given year (Date) and the birds overall fledgling production from that year (Fledge; count data ...
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Model selection using an artificially insignificant covariate

This is continued from my other post on model selection. Let me provide more details first. 1) I have a factorial design. Factor A has 5 levels, B has 2 levels, C has 2 levels. Let us assume that ...
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how to interpret Interaction term in generalized linear model [duplicate]

I have an experimental condition (dummy-coded) as an categorical predictor, and one continuous predictor variable (treatment frequency). The dependent variable (Y) is consumer satisfaction This is ...
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17 views

Partial pseudo $R^2$ with GLMs

Is a partial pseudo $R^2$ (pseudo $\eta^2$?) even a valid concept when dealing with a GLM? This, of course, presumes that partial pseudo $R^2$ is valid at all. If it is valid, how would one go about ...
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R: Prediction using glm() [migrated]

I am using glm() function in R with link= log to fit my model. I read on various websites that fitted() returns the value which we can compare with the original data as compared to the predict(). I ...
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16 views

Pareto two-tailed GLM regression

How can I perform a Pareto two-tailed GLM regression? Any reference to link functions and code in R?
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1answer
20 views

GLM Prediction Variance with Average Observations

Suppose I have data set where the observed values are averages and not necessarily individual data points. For example, suppose record 1 has the observed value of $Y_1 = 2.0$. However, I know that the ...
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1answer
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Modeling remaining duration for prediction

Suppose we're in the business of repairing broken specialty widgets and reselling them. At each point in time, we want to predict how much cash we'll make in the next 30 days on the existing ...
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1answer
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adjustment of covariates in linear model

I am trying to understand the adjustment of covariates in the linear model such as multiple logistic regression. How does adding a covariate adjusts the coefficients for that covariate (any intuitive ...