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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Accounting for neighboring geospatial values in SAS

I originally asked this question in Overflow but someone suggested I post it here. I'm trying to model the number of parks in a neighborhood as a function of education, land area (both continuous ...
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1answer
34 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 ...
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16 views

Best way to deal with heteroscedasticity in R?

I have a plot of residual values of a linear model in function of the fitted values where the heteroscedasticity is very clear. However I'm not sure how I should proceed now because as far as I ...
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15 views

How to model the following dependent variable? [on hold]

If I got a dataset which the (count data) dependent variable has the following distribution, how should I model it? I am aware of the zero inflated model and the negative binomial model, but are ...
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12 views

“Factorial” Profile Analysis, does it exist? Or is there maybe a better statistical method I should be using?

I am working on a visual search project that I created in Psychtoolbox with Matlab. As with most visual search studies, there will be within subjects tests, since each subject completes many trials. ...
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1answer
34 views

Exploring effect of treatment on count data

I've collected data on animal visitation at four different points in time. The four time points represent the total animal visitations over a three day period, i.e. 3 days of monitoring at four ...
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18 views

interpretation of coefficients from glm model, normal family with log link vs. linear model with logged outcome

I was fitting a linear model where the outcome was log transformed. The outcome is overdispersed and skewed and logging dramatically improved model fit. For reasons that relate to the software ...
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16 views

Intuitive explanation of failed convergence for GLM

Sometimes the software complains about some matrix not being positive definite. Is it the singularity that's causing issues (because the iterative algorithm involves inverting matrices)? Also, what ...
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1answer
22 views

I need a matrix in order to calculate g-inverse of it [closed]

I want to calculate g-inverse of a matrix, which has 4 rows and not a square matrix and has no inverse. Please help me find such a sound (good) matrix. I only need a matrix. You can suggest a book or ...
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1answer
157 views

How to implement GLM computationally in C++ (or other languages)? [closed]

I want to implement the GLM model in C++ for a commercial package (ie. this is not for fun), including but not limited to normal, binomial distribution etc. I'm not so sure how the implementation ...
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truncated binomial samples with GLM

We have a binomial process that yields samples of 60 trials. To save time, once 2 failures have been observed the process is reset. So if a test series hits 2 failures early, the resultant sample ...
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1answer
41 views

GLM Interaction Lasso

Apparently the stepwise produce in R is not a good way to automatically select the best glm model. Different sources suggest using lasso instead. I had a look at the glmnet packages but I do not ...
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13 views

Lambda's from glmnet (R) used in online SGD [migrated]

I'm using cv.glmnet from glmnet package (in R). In the outcome I get a vector of ...
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40 views

Extracting model equation and other data from glm function in R [closed]

I've made a logistic regression to combine two independent variables in R, using pROC package and I obtain this: ...
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1answer
24 views

R Linear model step NA values

My aim is to carry out a generalized linear model (glm) with 1 response variable and 13 explanatory variables.Unfortunately 3 out of the 10 explanatory variables contain NA values (2/3 of data set of ...
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26 views

R Linear model step NA values

My aim is to carry out a generalized linear model (glm) with 1 response variable and 13 explanatory variables.Unfortunately 3 out of the 10 explanatory variables contain NA values (2/3 of data set of ...
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0answers
17 views

Using QIC in glm (geeglm) when there is a warning of glm.fit: fitted probabilities numerically 0 or 1 occurred

I am using geepack in R to look for the best model for my data. I run the gee function with family=binomial(link="logit"). From the results I use QIC to find the ...
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20 views

Appropriate test (in R) for proportion data that aren't normally distributed, aren't based on counts, and include 0's and 1's?

I'm studying differences in tree health among 5 species of trees across 3 different green infrastructure types. Here are the first few lines of data: ...
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9 views

Estimating Risk (OR) for a Cohort over time

I am trying to assess the changing risk (OR) of group A relative to group B over time adjusting for a number of covariates. One approach I have attempted is to split the data set using a monthly ...
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1answer
27 views

Zero-inflated gamma - how to write down the cdf?

My goal is building a predictive model to give probabilistic forecasts. My response variable has lots of zeros but otherwise looks close to a gamma. I fit the whole dataset using some classification ...
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16 views

How to interpret parameters of GLM with binomial family for proportions

I would like to use glm with binomial family for proportions. However, I am wondering how could I interpret the parameters that is important in my case. In binary logistic regression one can interpret ...
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15 views

How to use GLM to decide between three factors based on binary data?

I have some problems with the interpretation of my glm results. I measured the disturbance of differently cultivated/ used site on a binary scale. 1 being influenced by the land use and 0 being not ...
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18 views

“fitted probabilities numerically 0 or 1 occurred ” in R? [duplicate]

I am trying to build a logistic regression that would tell me if it's worth sending a letter to a client. I have 11 significant variables in the model: What can I do to eliminate the warning ...
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8 views

Slope testing for regression lines between non-normal explanatory and response variables

I would like to estimate a regression line between my explanatory and response variables. The explanatory and the response variables are (paired) instrumental measures of the same thing under ...
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11 views

Robglm warning: 'algorithm did not converge'

I am trying to perform a bootstrapped robust GLM, but for some of the replications I get the warning: 'algorithm did not converge'. I use the triangular dataset below, where each row represents an ...
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15 views

I can't get my head around how to fix coefficient 1 not defined because of singularities problem

I am trying to use a GLM to test whether groupsize affects the frequency of butting in termites, I have non-normal data and I want to see if average weight of the termite has a random affect on the ...
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Skewed response variable LM [duplicate]

I have a positive asymmetric response variable in a regression model. One of the assumptions about linear model is that the stochastic component of the model is normally distributed. If I have a ...
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18 views

Generalized linear model Gaussian distribution Linear Model

Is a generalized linear model with a Gaussian distribution the same as a linear model?
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143 views

Overdispersion in GLM with Gaussian distribution

To check for overdispersion in GLM with a Poisson distribution one can compare the residual deviance with the residual degrees of freedom. If they are equal the Poisson error assumption is appropriate ...
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How a tweedie glm handles an offset?

I am trying to fit a model with a glm using a tweedie family. I use a index parameter p between 1 and 2 to get a compound Poisson Gamma distribution to fit my data. But I want to use an offset only on ...
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20 views

random effect for mixed model

I have ran mixed model with random effect. I have design 4 models iteratively following Zuur et al. the simplest one, and then I have added the other main and interaction effect (all with 1|subject as ...
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1answer
26 views

Tree model with poisson distributed response variable

If I understand correctly the R tree model - library(tree) - can only be used if the response variable is normally distributed. Is there a way to create a tree model with poisson distributed response ...
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15 views

Mechanics of converting success/failure values to logit in glm?

While I am reasonably comfortable with performing and interpreting the output from logistic regression using glm in R, I had a question about the mechanics of the calculation to better understand what ...
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49 views

What is the difference between lm(log(y) ~ x) and glm(y ~ x, family = gaussian(link = “log”))? [duplicate]

Is all in the title. I would like to know if there is any difference in terms of coefficients, residuals, p-values, but also conceptually.
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1answer
24 views

perfect variable separation, determine cutoff via ROCR package in R

I am developing a logistic regression model where perfect variable separation occurs. I want to calculate a cutoff from this data. Interestingly, the length of the slot ...
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6 views

apply fitted model to data and obtain loglikelihood [migrated]

I would like to do the following in Python, preferably with the statsmodels package (but if you know a solution with another package, I would be glad to hear about it as well): I have data ...
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18 views

Proper Model Selection Randomized Block with Count Data

I have a data set on insect counts that looks like this: ...
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1answer
26 views

Generalized linear model with random effects for skewed data

I'd like to use SPSS Generalized Linear Model to analyze a dataset of insects collected from one particular species of vegetables. I have following variables: NUMBER (number of insects collected) ...
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Relationship between the parameters of the Normal distribution and parameters in the probit with multiple predictors?

According to A. Agresti (2007, p. 73) in binary probit regression: "The parameters of the normal distribution relate to the parameters in the probit by mean (mu = -alpha/beta) and standard deviation ...
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1answer
22 views

Weighted GLM without weights

Suppose we have at our disposal a glm() that's got all the typical features except the ability to specify weights. Intuitively, I can trick it into using weights ...
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1answer
26 views

Difference between multilevel GLM and mixed linear models when the family is Gaussian and link function is Identity?

In Stata 13, there is now the new command "meglm" (multilevel generalized linear models) to analyse hierarchical models. My question is, what is the difference between the "meglm" with family of ...
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1answer
42 views

Appropriate regression-like model where the response is on half-integers

What is an appropriate model for the above scatter plot? I am not fully satisfied with a simple linear regression model. Any suggestions? Y in this problem is discrete in nature. It only increments ...
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50 views

Fitting GLM with Quasi-Newton method

I'm trying to code my own quasi-Newton algorithm for fitting GLMs in R. My results do not match up with glm and I've been over my code many, many times so I'm ...
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1answer
23 views

Can two separate regression coefficients be added to estimate their mutual effect?

Let's say I perform a Cox regression including 3 predictors that relate to the survival: Hazard ratios (HR) for predictors Sex: Hazard ratio for males = HR 1.5 Treatment: Hazard ratio for being ...
3
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1answer
50 views

Estimates of random effects in binomial model (lme4)

I'm simulating Bernoulli trials with a random $\text{logit}\, \theta \sim {\cal N}(\text{logit}\, \theta_0, 1^2)$ between groups and then I fit the corresponding model with the ...
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2answers
42 views

GLM over time and space?

is it possible to fit a GLM over space and time? I have shrimp densities measured over 8 months, and in each month there are 4 stations sampled along a freshwater-saltwater gradient, so I have 32 ...
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22 views

Testing model fit and running model validation plots for averaged models

I have carried out a model selection process and averaged the models within the top 2 AICcs (using MuMIn in R). I would like to test the model fit using both the chisq and validation/residual plots. ...
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1answer
31 views

difference between R square and rmse in linear regression

When Performing a linear regression in r I came across the following terms. ...
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13 views

Parameter estimation in generalized linear models

I have a bunch of questions on parameter estimation in GLM. They are all inter-related. I have tried to maintain a logical sequence of questions in the following. Bear with me, if the order doesn't ...
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2answers
54 views

count data as a dependent variable consist of five levels likert scale

I have few Likert type questionnaires (items) as responses recorded from 1 to 5. I am using these responses as a dependent variable against three independent variables( one contentious and two others ...