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.
1
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0answers
9 views
Nested test to remedy pseudoreplication
Data structure:
I have two datasets from two protected areas that differ in protection status. Both areas contain 43 and 37 sites each.
Question:
I would like to know which test would be the best ...
1
vote
0answers
19 views
GRM or mixed effect models
I need your help.
In experiment I have measured the grow of one plant on 40 locations. At one location different number of plants were measured, but always the same species. The distribution of ...
0
votes
0answers
41 views
Is my random variable affecting my response?
I performed an experiment on coral colonies (10 colonies, randomly chosen in repeated measurements); manipulating aragonite/carbon chemistry and temperature (fixed effect) to analyse effects on the ...
2
votes
0answers
35 views
What does the Scale parameter mean in linear regression?
I have started to use the GENLIN procedure in SPSS more than any of the specific dialogues, but I don't understand the Scale parameter or why it has the effects it ...
0
votes
0answers
27 views
Interpreting coefficients of a Poisson regression model
How can I interpret the coefficients of the Poisson regression model if the predictor variables have more than two levels?
Example:
...
3
votes
2answers
53 views
Does there exist a likelihood based experimental methodology book on undergraduate level for psychology / social science students?
I've been looking for an introductory book in statistics and experiment design for a methods course for psychology students. I think I've been looking through at least 30 books and all go through the ...
0
votes
1answer
26 views
GLM conditional distribution from R GLM
I want to obtain the full distribution of a Gamma (or Inverse Gaussian) distributed $y_i$ given a vector of $\bar x_i$ that have been used in the linear predictor of a coefficient. Suppose also for ...
10
votes
2answers
353 views
How are regression, the t-test, and the ANOVA all versions of the general linear model?
How are they all versions of the same basic statistical method?
1
vote
1answer
28 views
Separate specifications for p and n in a binomial GLM
For a binomial GLM, both the probability and the number of trials are important for each data entry. Using the glm fucntion in R, how do I specify them separately ...
0
votes
0answers
16 views
How to embed a dictionary into R glm formula? [migrated]
I'm trying to predict a class (its binary classification) of a tweet, based on those users, who did a retweet. And I want to include into formula not the count of retweets, but every user who did it. ...
2
votes
3answers
73 views
Which glm algorithm to use when predictors are numerical as well as categorical?
I just need a direction on which regression algorithm (preferably glm or similar) algorithm to use when the predictor variables are a mix of numerical and categorical variables. The output is ...
2
votes
1answer
61 views
Goodness of fit in a GLM with scaled deviance
On this page, I am interested in the section “goodness of fit”, which is near the bottom of the page and contains the table of deviance functions.
The author states that the scaled deviance, i.e. ...
1
vote
1answer
17 views
Starting Coefficient Vector for GLM
I would like to know how R chooses its starting coefficient vector for a GLM when its start argument is left blank and defaults to ...
0
votes
0answers
25 views
How to interpret results of GLM with a beta distribution in R?
I'm currently working on a project where my model is a GLM with a beta distribution. The dependent variable is bimodal. I've done this before, in Stata, but I'm having some difficulty interpreting ...
0
votes
0answers
22 views
Error with Syntax for GLM with inverse.gaussian family in R
In my data set, I have 3 covariates ($V_1, V_2, V_3$) and response ($V_4$), generated by a program specifically written to generate random variables using an Inverse Gaussian distribution, in which $ ...
1
vote
0answers
18 views
0
votes
0answers
26 views
3
votes
1answer
87 views
Using R for GLM with Gamma distribution
I currently have a problem understanding the syntax for R for fitting a GLM using the Gamma distribution.
I have a set of data, where each row contains 3 co-variates ($X_1, X_2, X_3$), a response ...
0
votes
0answers
28 views
Reference for R VGLM function
Could someone please either explain to me what the VGLM function in R does or point me towards comprehensive documention? Currently, the only documentation I can find does not explain what a Vector ...
1
vote
0answers
34 views
GLM with unequal sample size
I am trying to figure out how to run a GLM with a poisson distribution in R. The data has 4 treatments: A(n=16), B(n=17), C(n=16), D(n=20), and 2 time periods. This data is count/area so there are ...
4
votes
0answers
44 views
Which model for panel data with dependent variables from [0,1]?
I'm stuck with a regression modeling problem. I have panel data where the dependent variable is a probability. Below is an excerpt from my data. The complete panel covers more countries and years, ...
0
votes
0answers
18 views
Calculation of Semi-Elasticities
I have one question regarding the calculation of elasticities for gamma and negative binomial regression.
I know that for a poisson regression one gets semi-elasticities. Dividing by the mean of the ...
1
vote
1answer
30 views
Generalized Linear Model estimating multiple parameters concurrently
Is there an extension of the generalized linear model that is able to estimate (using maximum likelihood) more than one parameter of a distribution at once. For example, I know that for a gamma ...
4
votes
1answer
91 views
Which is more accurate glm or glmnet?
R glm and glmnet use different algorithms.
I notice non trivial differences between the estimated coefficients when I use both.
I am interested in when one is more accurate than another, and the ...
0
votes
1answer
67 views
Why is Hedonic Regression used instead of Linear Regression
Why is Hedonic Regression used (especially in housing prices) instead of Linear Regression?
There do not seem to be any libraries in Python (and R) for Hedonic regression, is it too niched a ...
0
votes
0answers
17 views
Prepare data for generalized linear regression [migrated]
I want to perform glm for the dataset Titanic in R. I did the following steps to prepare the data and run glm
...
0
votes
0answers
27 views
incorporating averaging models from AIC and still using k-fold cross validation?
Ive a county/district that Ive divided into ~300 grids that are 15km^2 in size attributed with various habitat and economic variables that have been summarized and standardized. I then have 2 types ...
0
votes
2answers
44 views
Transforming nonlinearity in genearlized linear models
Are the buldging rules applicable in generalized linear models? Specifically, to transform the independent variables?
I've only seen it disscused/used in OLS regression.
Thanks in advance
0
votes
1answer
42 views
Simulating responses from a factorial experiment for power analysis
I am thinking about a factorial experiment with two factors. Both factors are ordered factors. Factor 1 has two levels: small and large. Factor 2 has four levels: never, sometimes, frequently, and ...
0
votes
0answers
23 views
Factorial design with repeated-measures and binary outcome variable
I have a question on how to analyze some data from a psychological experiment.
The design of the experiment is a mixed 2x2x4 factorial: the first 2 factors are manipulated between-participants, but ...
0
votes
0answers
30 views
Restricting model parameters in logistic models in R
Is there any function in R that can solve the problem like this in SAS?
Thanks in advance!
1
vote
2answers
63 views
Should the predictor variables be normally distributed for Poisson glm?
This is probably a really basic question, but it's the first time I've created a model that defines Poisson as its error family.
In setting up my variables to make the model, should I be concerned ...
5
votes
4answers
301 views
What regression analysis should I perform on my data and why?
I am a law student researching which factors influence the CSR (corporate social responsibility, GSE_RAW) behavior of companies. As my studies didn't offer any ...
0
votes
1answer
61 views
How to see how good is my model?
I am using glm to find a model using training data and then use test data to see how well the model is behaving. My response variable is ...
0
votes
0answers
17 views
Estimating the variance of linear prediction coefficients and parameters
I'm using linear prediction with singular value decomposition (LPSVD) to analyze signals that are damped sinusoids. If my understanding of the theory of linear prediction is correct (and it may not ...
0
votes
0answers
16 views
Choosing prior distribution in LDA
how do you set prior distribution of K in LDA and can it be used for feature selection to improved selection accuracy of document. Abbey
3
votes
2answers
83 views
What is the expected distribution of residuals in a generalized linear model?
I am performing a generalized linear model, where I have to specify a family different from the normal one.
What is the expected distribution of residuals?
For example, should the residuals be ...
-1
votes
0answers
68 views
Mixed-effects GLM with repeated measures in R [closed]
I am working as a biologist and I am currently trying to analyze temporal variation among three types of forest stands. There are have three repeated measures (every 10 years) for each type. I am ...
-1
votes
0answers
16 views
how to get covariates for these type of data [closed]
i have data on teachers-students responses on the usage of some particular teaching methods, I intend to use ancova for the analysis but don't know how to get the covariates. please advice me?
0
votes
0answers
17 views
Testing the effect of spatial differences
I have a closed room. I release some bacteria in the middle of it and let it settle onto previously located plates on the floor. Once the bacteria settles I collect the plates and count up how many ...
1
vote
1answer
99 views
Plotting predicted values in GLM in R
I am trying to find a more aesthetic way to present an interaction with a quadratic term in a logistic regression (categorisation of continuous variable is not appropriate).
For a simpler example I ...
0
votes
3answers
76 views
Regression for a Rate variable in R
was tasked with developing a regression model looking at student enrollment in different programs. This is a very nice, clean data set where the enrollment counts follow a Poisson distribution well. I ...
3
votes
6answers
95 views
Time spent in an activity as an independent variable
I want to include time spent doing something (weeks breastfeeding, for example) as an independent variable in a linear model. However, some observations do not engage in the behavior at all. Coding ...
0
votes
1answer
53 views
GLM model explanation
Can anyone please tell me what kind of model is being run with the following R call:
glm.fit(covar,pred,family = binomial())
I know what ...
5
votes
1answer
198 views
Wald test in regression (OLS and GLMs): t- vs. z-distribution
I understand that the Wald test for regression coefficients is based on the following property that holds asymptotically (e.g. Wasserman (2006): "All of Statistics", pages 153, 214-215):
$$
...
1
vote
1answer
70 views
Generalized LM or LM in ecological dataset
I have previously posted a question for the same dataset but now I had somme issues with the models and I wanted to re-phrase my question.
My dataset contains 50 morphometric characters (which we ...
3
votes
0answers
115 views
Are GAMMs/GLM the best choice for calculating number of germs on hands?
We would like your opinion on whether GAMMs are a good option and how best to go about implementing for the following:
During a period of patient care, a nurse will accrue $Y$ colonies of bacteria on ...
0
votes
0answers
38 views
glmnet standardization details
I'm using glmnet for a binary variable. I'm trying to use the resulting coefficients to understand what variables are driving the responses. In doing so I've come across the ...
0
votes
0answers
26 views
How do you generate synthetic sparse binary linguistic data for logistic regression?
I am trying to generate synthetic linguistic data (boolean features) to fit a binary logistic regression model. This is similar to 8260771 on StackOverflow and several synthetic data questions on this ...
2
votes
0answers
57 views
Finite mixture models with bounded data
I am trying to fit a finite mixture model to a dependent variable which is bounded (practically) between -0.594 and 1 (theoretically, the latent variable is bounded between -Inf - 1). The data are ...
