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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how should I do regression analysis where response is number in each category

I try to find genes that related to output which is numbers in three category. The simplified analogy is: first we take an zygote and measure the expression of a gene, and clone this zygote to many ...
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42 views

Square root link function

I'm running a glm that estimates gaussian variable of production in kilogrames using different independent variables. i found a problem of heteroskedasticity so i tried different transfromations of my ...
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1answer
18 views

Estimating model fitness for variable importance in linear regression

I am interesting in finding the relative importance of variables in a GLM model. The dependent variable is binary, while the independent variables are a mix of continuous and categorical. To do this, ...
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2answers
32 views

Beta distribution GLM with categorical independents and proportional response

My data is percentage disease data of different varieties of plants that had been inoculated with disease from several different sources. having conducted two-way ANOVA in SPSS (using the log10+1 of ...
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1answer
4 views

How to judge, which random effects to include in a mixed model?

I am currently writing my master thesis about the effect of an insecticide (clothianidin) on the microflora of bumblebees. I received the bumblebees from an experiment with a nested study design. 16 ...
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14 views

Separation problem in GLM

So I think I have a separation problem in my logistic regression model. The response variable (that only takes 0 or 1 values) takes 99.2% of its cases as 0 and only 0.8% as 1. This is the ROC curve ...
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28 views

What assumptions are made about the link function in GLM's?

There are posts in similar vein about this topic, but what I want to know exactly is whether there is a list of properties/assumptions about link functions in generalized linear models, or if the link ...
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3answers
55 views

how to calculate R-squared in glm?

I came up with below for my glm analysis but I need to calculate R-squared to cite in the paper? anyone can help me with this please? summary(lrfit) Call: ...
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3answers
75 views

How does the logit link handle binomial (1/0) data?

I have a data set that contains a continuous explanatory variable and a set of responses as binary success and failures. For example, ...
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1answer
58 views

Difference Between Discrete Time Proportional Hazards and Logistic Regression

My data consists of one row per person, per month that person was "exposed" to an event. So the month is the discrete time and the row corresponds to one "person-month". There are a few independent ...
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9 views

future prediction by inverting GAM model coefficients [closed]

I am new to R. I am trying to fit GLM and GAM model to my species data against few variables, which I have already done using GLM and mgcv package (GAM). I got some parametric coefficients. The next ...
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2answers
86 views

Log vs square root link for Poisson data in R

I am currently working to model deaths from AIDS over time using a GLM in R. I know that there are two possible options for the link function for Poisson data, log and square root. I know that square ...
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42 views

Estimating medians and modes of skewed distributions using GLMs

Edited question (less vague hopefully) I am wondering why for generalized linear models with Gamma, Poisson and Negative Binomial distributions that there appears to be no discussion about estimating ...
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11 views

Getting percentage range of variance accounted for by DV in confidence interval

I have a confidence interval that has a range of values. How can I turn those values into a percentage range that represents the possible variance accounted for by the DV? ...
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2answers
170 views

Poisson GLM with non-count data (rate data)

My question is related, but not the same as the following question: Fitting a Poisson GLM in R - issues with rates vs. counts Here's some fake data: ...
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8 views

GLHT with GLM negative coefficients?

I'm trying to do a glht with this glm, but I'm getting some weird results. My glm is ...
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1answer
29 views

How good is a model if it can't predict a single positive class?

I have a training set of over a 100,000 points that is used to train a Logistic Regression Classifier (logit, since response is binary). The model is testing/fitted on a test set of 20,000 items. The ...
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1answer
33 views

Interpreting interaction effects in probit regression model

I have run a probit regression model with one 2-way interaction and am having trouble interpreting the results. Both variables are categorical and so one level of ...
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9 views

Is it possible to use a categorical covariate/regressor to predict (classify)?

I am working on constructing models in which the response variable is binary, and so I use the logit link function. For my first model, my covariates were continuous values and using the ...
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115 views

OLS vs. Poisson GLM with identity link

My question reveals my poor understanding of Poisson regression and GLMs in general. Here's some fake data to illustrate my question: ...
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24 views

glht: difference between Tukey and manual matrix

I am testing for contrasts in contrasts in my dataset. Going in, I thought Tukey showed all the contrasts between the coefficients, but it seems that manually putting in a matrix yields different ...
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15 views

Paired design and building a GLM

I am blocking a bit on the choice of a model for my data. The study is applying 2 different stimuli (S1 and S2) on some animals. Each stimuli has two 'treatments': either 'on' or 'off' (off being the ...
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2answers
38 views

GLM diagnostics and Deviance residual

From my understanding, the deviance residual of a GLM model, when plotted against the fitted values, should give a scatterplot distributed with mean 0 and constant variance? Does this hold for any GLM ...
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2answers
49 views

Model approach for count data with a large range of y values

I am modeling ridership data for specific routes by month over a number of years. Some routes have as little as about 1000 riders per month while other routs may have over 20,000 riders per month. I ...
3
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2answers
178 views

Why are there huge differences in the SEs from binomial & linear regression?

I have data from a simple experiments where people put (a fixed number of) balls either to the left or to the right of them (each ball is just the same with regards to consequences of putting them to ...
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29 views

Generalized linear mixed model (different F-statistics dependent on formation of fixed model)

I am doing a GLMM on the number of days between two events in wheat development. I did it once in Genstat with the fixed model like factor 1*factor 2 and got a ...
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2answers
67 views

Can I use logistic regression if the distribution of proportions is skewed & lies in the middle of the [0,1] interval?

I am conducting a logistic regression in order to predict the service point win percentage of a tennis player. In terms of data - I have (for each player A) approx 300 matches - for each match I have ...
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40 views

Generalized gamma as a member of the exponential family

I want to show if this generalized gamma (GG) distribution is a member of the exponential family. I don't know how to start since the exponential family has only 2 paramters and the GG has 3 ...
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19 views

General linear model ----How to improve the fitness?

I am doing the research of optimizing the signal integration high speed margin test parameters, and my goal is to find the parameters combination with the highest probability of "PASS". See the data ...
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60 views

Pearson VS Deviance Residuals in logistics regression

I know that Pearson Residuals are obtained in a traditional probabilistic way: $$ r_i = \frac{y_i-\pi_i}{\sqrt{\pi_i(1-\pi_i)}}$$ and Deviance Residuals are obtained through a more statistical way ...
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36 views

A Regression to predict tennis player's service point win percentage - Which of these two models makes more sense?

I am conducting a regression in order to predict a tennis player's service point win % i.e. the percentage of points he wins when he is the server. Model 1 If my DV data lies in the range 0.3-0.9, ...
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9 views

PROC GENMOD: I get non-estimable least squares means, but an estimable difference in least squares means

I ran a PROC GENMOD code in SAS (see below). Q is a binary variable, while X and W and ternary variables. The output shows that the least squares means for both levels of a binary variable, "Q", are ...
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12 views

Explore the possibility of autocorrelation for discrete factors

I have a dataset looks something like the following: ...
6
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1answer
61 views

Closed form function relating $\mu$ to the natural parameter for the logarithmic series distribution?

While answering another question here, I mentioned the logarithmic series distribution as a possible model for species per genus. In the course of looking at the pmf while answering that I realized ...
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1answer
26 views

Understanding categorical variables in ZINB and other models

This might sound very basic question but not getting the logic from the outcome or unless I need to code my categorical variables in a different way. I am trying to model risk factors for a disease ...
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21 views

Variance as a function of the mean. Why does this affect linear regression? [duplicate]

I'm currently studying university level statistics and I'm struggling to wrap my head around the concept of variance as a function of the mean. How does this affect linear regression and why does it ...
0
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1answer
21 views

Time Series/Dynamic Problem: Is this an appropriate way of accounting for form/recent trend in my model?

I am attempting to conduct a dynamic or time series regression for a tennis analytics project, endeavoring to predict the probability of a player winning a point in which he is the server. For a ...
2
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3answers
105 views

GLM high standard errors, but variables are definitely not collinear

When I use a GLM using R, my standard errors are ridiculously high. It can't be because the independent variables are related because they are all distinct ratings for an individual (i.e., interaction ...
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12 views

A general approach to getting around model packages which cannot handle rank deficiency

A few packages which employ variations of generalized linear models (pscl and VGAM), specifically the functions ...
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3answers
59 views

How to deal with “non-integer” warning from negative binomial GLM?

I am trying to model the mean intensities of parasites affecting a host in R using a negative binomial model. I keep getting 50 or more warnings that say: ...
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4answers
227 views

What is the upper bound on $R^2$ ? (not 1)

It is a well known fact that if you add additional independent variables in a linear regression, the $R^2$ of the new model is at least as large as the previous model. So you obtain a lower bound for ...
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32 views

Is Multiple Regression the right choice for my statistical design?

I am studying the relationship between mental health offices and judgments of perceived care expected within those environments. Participants will rate different photographs of mental health offices ...
0
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1answer
24 views

Need GLM ideas for nonlinear biochemical model

I have a function from molecular bio where I am trying to estimate the parameters $\alpha$ and $\beta$. $\frac{Y}{M} = f(\alpha + \beta X)$ where $0 \leq Y \leq M$, and $f(a) = \frac{a}{1+a}$. ...
0
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1answer
66 views

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 ...
4
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3answers
303 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 ...
2
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1answer
20 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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0answers
13 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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0answers
16 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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21 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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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($\pi_i$) = ...