Linked Questions

45 votes
3 answers

How to decide which glm family to use?

I have fish density data that I am trying to compare between several different collection techniques, the data has lots of zeros, and the histogram looks vaugley appropriate for a poisson distribution ...
C. Denney's user avatar
  • 705
62 votes
4 answers

Choosing between LM and GLM for a log-transformed response variable

I'm trying to understand the philosophy behind using a Generalized Linear Model (GLM) vs a Linear Model (LM). I've created an example data set below where: $$\log(y) = x + \varepsilon $$ The ...
Marc in the box's user avatar
19 votes
3 answers

Poisson or quasi poisson in a regression with count data and overdispersion?

I have count data (demand/offer analysis with counting number of customers, depending on - possibly - many factors). I tried a linear regression with normal errors, but my QQ-plot is not really good. ...
Antonin's user avatar
  • 409
27 votes
1 answer

Goodness of fit and which model to choose linear regression or Poisson

I need some advice regarding two main dilemmas in my research, which is a case study of 3 big pharmaceuticals and innovation. Number of patents per year is the dependent variable. My questions are ...
Nitzan's user avatar
  • 361
12 votes
3 answers

Comparing regression models on count data

I recently fit 4 multiple regression models for the same predictor/response data. Two of the models I fit with Poisson regression. ...
Daniel Standage's user avatar
8 votes
2 answers

Understand Link Function in Generalized Linear Model

I am still trying to learn (may be the terminology issue) what does "link function" mean. For example, in logistic regression, we assume response variable is coming form binomial distribution. The $\...
Haitao Du's user avatar
  • 37.1k
10 votes
2 answers

Should I use Poisson distribution for non-integer, count-like data?

It's my first question here, I hope I'll ask it correctly. I am trying to find out how to analyse non-integer, count data (yes!). I am looking at the effect of a given treatment on habitat suitability ...
Guillaume Lavanchy's user avatar
4 votes
2 answers

Difference between linear regression and neural network

I am obviously confused with terms, and different concepts behind it. Each websites gives different intuitions. With all intuitions my brain is full of confusion now. Please help me to address what is ...
DrunkenMaster's user avatar
2 votes
2 answers

Autoencoder for sparse data

Suppose I have a big (1,000x20,000) sparse (95% of elements are zeros) matrix with counts. I want to use autoencoder to encode-decode this matrix. How should I do it? Are there any tricks or ...
chipolino's user avatar
6 votes
1 answer

Why glm() can't recover the true parameters?

glm() of the following data gives intercept 0.56916 and slope x .018. But the true slope should be 1/10. Does anybody know why glm() can not recover the true slope? Thanks. ...
user1424739's user avatar
1 vote
3 answers

Is there a reason we need to make a logistic regression linear using the logit?

My understanding is that we use the logit function to convert the sigmoidal curve of a logistic regression to be linear. As a result, we go from a curve modeled as P = ea+bX / (1 + ea+bX) to one that ...
theforestecologist's user avatar
3 votes
2 answers

Model Evaluation for Discrete Regression

I've building a model to predict count variables, i. e. the quantity I'm predicting is a positive integer. I know that for regression a usual metric of model quality is the R-squared coefficient, but ...
Guillermo Guardastagno's user avatar
4 votes
0 answers

Family in GLM - how to choose the right one?

When modeling data sampled in the field, I often come across the problem of determining the Family of the dependent variable for GLM (or GLMM). An example: in an ecological study, I have ~ 60 patches. ...
yenats's user avatar
  • 427
3 votes
1 answer

What r parameter is used in a negative binomial regression?

From my understanding of the negative binomial regression, we have $Y_i|X_i; \theta$ is distributed $Neg Binom (r_i, p_i)$, where $r_i$ is known and fixed (analogous to a fixed $\sigma^2$ when we ...
Zslice 's user avatar
  • 135
3 votes
2 answers

Should my residuals have the same distribution as my likelihood function in a Bayesian linear regression?

If I have a standard linear regression model: $$ y \sim \mathcal{N}(\beta_0 + \beta_1X + ..., \sigma) $$ With whatever priors on $\beta$'s and $\sigma$. Does that imply that my residuals $r = y - (\...
eadains's user avatar
  • 41

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