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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17 views

Using the confidence intervals to improve predictive model success rate

I'm trying to build a binomial predictive model based on glm. My overall prediction is very low, in the order of 60%. But when I go for the datapoints that have the both boundaries in one side, for ...
0
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0answers
22 views

Modeling continuous abundance data with a GLM in R: how to select the correct distribution family?

I have abundance data (counts) that I have standardized by area sampled, making them continuous. I would like to explain them with my two independent variables using a GLM but I am having trouble ...
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0answers
8 views

Poisson glm overdispersion. Calculating QAICc for model comparison

I'm having some difficulties with my glm . The models are overdispersed so I want to run them as quasipoisson. I would like to be able to compare the models using QAICc but the packages I have ...
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0answers
8 views

post hoc nested

Hy, need to do post hoc test for nested factor in SAS. I have two factors, A and B with B nested within A and I want to perform post hoc test in GLM in SAS. Is it possible? Stevan
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15 views

How to select GLM model and account for sampling time? [duplicate]

I'm trying to detect relationships between species abundances (counts) and time (years) for many species using either Negative Binomial or Poisson regressions (depending on degree of dispersion). ...
6
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1answer
25 views

When to use offset() in negative binomial/poisson GLMs in R

I'm trying to detect relationships between species abundances (counts) and time (years) for many species using either Negative Binomial or Poisson regressions (depending on degree of dispersion). ...
0
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0answers
23 views

Is my nested random-effect model non-hierarchical?

I have a problem with model structure because of the way factors are nested in a potentially non-hierarchical way. I'm not sure if I fully understand the issue but I can't find a way to specify the ...
0
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0answers
13 views

RandomGLM: data (responses) is not read as numeric [on hold]

I'm relatively new to R and I have a problem with randomGLM for prediction of a continuous variable. I read in four datasets which are comprised of a training ...
0
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0answers
14 views

How to determine the random variable distribution to fit the generalized linear model in R? [closed]

I have a trouble with a sample from a random variable. I cannot determine its distribution. I would like to calculate the generalized linear model then with this variable treated as the dependent ...
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0answers
22 views

find best fitting model from GLM output

after running biglm on the data I am a bit confused regarding the output. I normally use GLM and so the biglm output looks rather different. A summary of the object outputted by biglm is ...
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0answers
12 views

Method to analyze data which has no repetitional measurement

I am trying to find a good method to analyze my data, and I am really lost. My data is only from one riverstretch, no repetitional measurement. I look at three stretches of the river, near ...
4
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56 views

Generalized Linear Models vs Timseries models for forecasting

What are the differences in using Generalized Linear Models, such as Automatic Relevance Determination (ARD) and Ridge regression, versus Time series models like Box-Jenkins (ARIMA) or Exponential ...
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0answers
11 views

bestglm starting values error

I am trying to get the best subset regression for a gamma glm, so I am using the bestglm package, but this error continues appearing: ...
1
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1answer
19 views

Calculating CI and SD for individual regression lines from a multiple factor glm

We have 2 correlated variables and a lot of binomial factors (around 200), here illustrated with just $f1$ and $f2$: ...
3
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0answers
31 views

simulate GLM with square root link in R

I'm trying to simulate a fitted GLM using basic functions, not using the simulate() and predict() functions that are widely questioned and answered. I get different results when I compare my math ...
0
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1answer
14 views

Repeated Measures with a covariate

I have two continuous variables; a pretest and a post test. They seem to have significance in paired samples; p=.000. However I wonder what caused the post test to be better than the pre test and I ...
3
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3answers
38 views

Managing complex models/formulae

I'm building a logistic model on a fairly large dataset (~90 features). I have enough data to include many different features, nonlinearities and interactions between them without worrying about ...
0
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1answer
104 views

How to interpret “main effects” in a GLMM?

Recently, I asked a question about what procedure to use to analyse mixed data with dichotomous outcomes, see [here][1]. Now I started running some first analyses (mainly with SPSS, but I'll post the ...
1
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1answer
160 views

what if response variable is 'yes or no' in R?

How to analyze above the data to predict the probability that people have disease with a model? Factors thought to influence infection include city, age, and diet. BUT, I don't know how to do ...
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0answers
8 views

Including an interaction term in GLM with full factorial experiment data

I collected data from an 2 X 2 X 2 full factorial experiment. Because a DV is a count variable and unequal mean and variance, I ran a Negative Bimonial Model. It was hard for me to come up a good ...
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1answer
28 views

Reduce the polynomial terms in logistic regression (glm)

I've three categorical variables A, B and C with 5 levels each. The model I'm trying to fit is glm(Y~A+B+C, family=binomial()) How can I remove the higher order ...
7
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2answers
210 views

Is there a way to force a relationship between coefficients in logistic regression?

I would like to specify a logistic regression model where I have the following relationship: $E[Y_i|X_i] = f(\beta x_{i1} + \beta^2x_{i2})$ where $f$ is the inverse logit function. Is there a ...
0
votes
1answer
15 views

vglm: Error in vglm.fitter, due to matrix dimension?

I've looked to other Error in vglm.fitter-related posts but they don't seem (or I cannot) related to this one. This the error I get when running vglm for multinomial regression (classification): ...
0
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1answer
32 views

confidence interval on expected number of cases in a set given a model

I have a set of units, where unit $i$ has: a binary outcome $y_i$ ("case" or "non-case") a binary vector of treatments $\mathbf{t_i}$, where $t_{i,j}=1$ if unit $i$ received treatment $j$. several ...
2
votes
1answer
26 views

How can I use the set of linear models to obtain a single equation?

This is my new attempt to rewrite the previous question about combining a few linear regression models into single equation. The background is that I have a set of dependent variables Y which is ...
2
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1answer
110 views

glm.fit: algorithm did not converge -Tweedie

I'm trying to estimate $p$ in tweedie regression, but I got the following message: glm.fit: algorithm did not converge I'm using public data from "GLMs for insurance data" book by Piet de ...
0
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0answers
11 views

Predicting continuous response with a mix of categorical and continuous variables

What regression method should I use to construct a model predicting a continuous response with a mix of categorical and continuous variables? I would do this with SPSS (16.0) and was thinking of using ...
0
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0answers
23 views

How should I model a [0,1] censored variable?

I have a variable which is based on a count of occurances devided by the total number of potential occurences for each unit. This variable thus varies between zero and one. It should be used as a ...
0
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0answers
12 views

Two ways to model pre/post/treatment setting. Which one is preferred and why?

I have 20 individuals randomly distributed into two groups(treatment vs non-treatment) and test_score was measured before/after the treatment. My central goal is to measure the effect of the ...
0
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1answer
6 views

GLM - X.intercept equal to NA [duplicate]

What does it mean X.Intercept equal to NA as a result of glm summary ? Thanks. Coefficients: (1 not defined because of singularities) ...
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0answers
7 views

Negative Binomial Anscombe Residuals

I need a help :) I'm working on a Generalised Linear Regression model, using the 'so called' NB2 model, in other words I'm using the Negative Binomial regression for count data. I would like to graph ...
1
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2answers
40 views

How can I block by location in R?

Still new and learning how to use R, but I'd like to get some help with figuring out how to block my data by location. I found this tutorial, but I don't understand where to put my data into the code. ...
1
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1answer
46 views

Bootstrap glm and extract pvalue

I am running a glm model using bootstrap, I can extract the coefficient mean and the confidence intervals for all the factors in my model. But how can I get the pvalue from there? Model: ...
0
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0answers
29 views

How does one do a Post-hoc test for a Poisson glm in R?

My data consists of the following categories: Site - 3 sites - Boulder, Rubble and Cul-de-Sac Season - 4 types - warm1, warm2, cold1 and cold2 Behaviour - 6 behaviours scored - Basking, ...
3
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1answer
30 views

Selecting variables using SAS and R - all effects are significant even when shuffling the data

Dear all: I need to test which effects I should include in my model for genetic evaluation of cows. I was using the following code in R: ...
0
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1answer
23 views

Full effects from Poisson GLM

I am running a Poisson GLM with count data as response variable and both continuous and categorical variables as predictors. I made use of the following (dispersion is OK): ...
2
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0answers
39 views

glmer coefficients extraction

I would need to extract some coeffients after an analye performed with glmer in R. As I am not an expert I have tried to simulate data to see where I need to find those coefficients. But I am even ...
0
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0answers
11 views

Different results from quasipoisson models when using glm and winbugs

I used glm and winbugs to estimate quasipoisson models. I think the results should be very similar theoretically but they are not. Coefficients from winbugs are quite larger than those from glm. Does ...
1
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1answer
22 views

bayesglm (arm) versus MCMCpack

Both bayesglm() (in the arm R package) and various functions in the MCMCpack package are aimed at doing Bayesian estimation of generalized linear models, but I'm not sure they're actually computing ...
2
votes
1answer
39 views

Type of inference to use with log-linear Poisson glm on contingency table frequency counts

I was doing some log-linear models to test for interactions/associations in multiway contingency tables (based on the tutorial here, http://ww2.coastal.edu/kingw/statistics/R-tutorials/loglin.html). I ...
0
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0answers
22 views

Produce a GLM intercept that does not include reference levels for categorical variables?

I realize that a similar question to this has been asked, but it was not ultimately resolved. I have tried the suggestions posted to that question here, but have had no success. I am using the ...
1
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1answer
76 views

Logistic Regression Assumptions

I am preparing a presentation on logistic regression. I applied logit model to a data set and now want to check whether my model meets logistic regression assumptions. I don't exactly know how to do ...
1
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0answers
20 views

Measure of explained variance for Poisson GLM (log-link function)

I am looking for an appropriate measure of the "explained variance" of a Poisson GLM (using a log-link function). I have found a number of different resources (both on this site and elsewhere) that ...
0
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0answers
6 views

Methodology for OCR Content Parsing

From a PDF book I have used built-in PDF OCR to retrieve text contents. But the PDF pages have 1-2 real book pages in one PDF page. I want to separate these pages. Example for txt page i: ...
0
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0answers
45 views

How do we generate the ROC curve for Linear Discriminant Analysis method

I know the method to generate the ROC curve for other methods such as naive Bayes where the tuning parameter is the threshold like also in logistic regression. If we want to generate the ROC curve ...
2
votes
1answer
43 views

How to account for overdispersion in a glm with negative binomial distribution?

I'm analysing count data with a generalised linear model in R. I started with a Poisson family distribution, but then realized that data was clearly overdispersed. I then took the option of applying a ...
0
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0answers
42 views

R gam() throws error: “Can't correct step size”

I am computing a GAM on a large set of data sets. Almost all of them work, just this one data set makes gam() throw an error. I paste a code that reproduces this error here: ...
0
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0answers
8 views

Coefficients flip sign in general linear model depending on what predictors are included: collinearity is NOT a problem [duplicate]

I have a general linear model with several predictors (~10). The sign (beta) of one of the predictors (Pred1) is negative when all predictors are included. It's STILL negative when the most correlated ...
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23 views

Adding real zeros to a dataset vs. presence-only modeling?

I have a fisheries dataset for which I have calculated the number of fishing sets in each grid cell (100 km x 100 km) for each month of every year. Fishermen in this fishery are legally required to ...