Linked Questions

10 votes
1 answer

Logistic regression output and probability [duplicate]

What is the interpretation of the number that is the output of the logistic regression function? The logistic function $$f(\vec{x}) = \frac{1}{1+e^{-g(\vec{x})}}$$ (where $g$ is a linear function)...
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  • 1,731
2 votes
1 answer

Probit or Logit in Generalized Linear Model [duplicate]

I'm trying to apply GLMs on a dataset in which dependent variable Y is dichotomous. I applied either logit and probit models, and probit fitted better than logit model. How do I justify the choice of ...
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  • 45
0 votes
1 answer

What exactly are some fundamental differences between probit model and logistic regression [duplicate]

It seems that both these refer to cases where the regressed (dependent) variable can only take certain values, as opposed to a linear regression. So what is the difference between probit and logistic ...
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  • 6,055
3 votes
0 answers

What is the meaning of the different links in the binomial family of a GLM model in R? [duplicate]

In the help of the glm command in R, I read the binomial family the links logit, probit, cauchit, (corresponding to logistic, normal and Cauchy CDFs respectively) log and cloglog (...
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  • 81
1 vote
0 answers

Generalized linear model v linear model [duplicate]

Bioinformatician here. I'm wondering what the main differences between both of these approaches is. I just want to clarify that a formal mathematical treatment would likely be too much for me. From ...
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1 vote
0 answers

Logit versus Probit [duplicate]

Possible Duplicate: Difference between logit and probit models I have data in which the response variable is binary. So, I fitted logit and probit models and obtained the results. How can I ...
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  • 929
1 vote
0 answers

Meaning of link functions (GLM) [duplicate]

I am performing ordinal regression on several datasets, I have 5 ordered response categories and only one explanatory variable X. For each dataset I run the analysis 3 times, each time using a ...
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  • 895
0 votes
0 answers

Likelihood values from Sigmoid [duplicate]

Repost of Mathemetics StackExchange question. There are multiple doubts of mine associated around this theme: In MLE, we try to find the PDF parameters ($\theta$) which maximise the likelihood of the ...
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  • 143
0 votes
0 answers

Comparison between Logit and Probit models [duplicate]

I'm new in econometrics and I'm working in a probabilistic model and using Stata for this, but when I was going to compare the Logit and Probit I did not know which one win in this case, because there ...
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389 votes
9 answers

What is the difference between fixed effect, random effect and mixed effect models?

In simple terms, how would you explain (perhaps with simple examples) the difference between fixed effect, random effect and mixed effect models?
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  • 5,668
118 votes
5 answers

Comprehensive list of activation functions in neural networks with pros/cons

Are there any reference document(s) that give a comprehensive list of activation functions in neural networks along with their pros/cons (and ideally some pointers to publications where they were ...
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138 votes
3 answers

What is the difference between linear regression and logistic regression?

What is the difference between linear regression and logistic regression? When would you use each?
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  • 2,903
87 votes
4 answers

What is the difference between a "link function" and a "canonical link function" for GLM

What's the difference between terms 'link function' and 'canonical link function'? Also, are there any (theoretical) advantages of using one over the other? For example, a binary response variable ...
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  • 1,802
59 votes
6 answers

Alternatives to logistic regression in R

I would like as many algorithms that perform the same task as logistic regression. That is algorithms/models that can give a prediction to a binary response (Y) with some explanatory variable (X). ...
61 votes
2 answers

What does having "constant variance" in a linear regression model mean?

What does having "constant variance" in the error term mean? As I see it, we have a data with one dependent variable and one independent variable. Constant variance is one of the assumptions of linear ...
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  • 787

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