Linked Questions

77
votes
3answers
40k views

Diagnostics for logistic regression?

For linear regression, we can check the diagnostic plots (residuals plots, Normal QQ plots, etc) to check if the assumptions of linear regression are violated. For logistic regression, I am having ...
57
votes
5answers
101k views

How to calculate pseudo-$R^2$ from R's logistic regression?

Christopher Manning's writeup on logistic regression in R shows a logistic regression in R as follows: ...
32
votes
3answers
83k views

How to calculate goodness of fit in glm (R)

I have the following result from running glm function. How can I interpret the following values: Null deviance Residual deviance AIC Do they have something to do with the goodness of fit? Can I ...
28
votes
4answers
63k views

Pseudo R squared formula for GLMs

I found a formula for pseudo $R^2$ in the book Extending the Linear Model with R, Julian J. Faraway (p. 59). $$1-\frac{\text{ResidualDeviance}}{\text{NullDeviance}}$$. Is this a common formula for ...
15
votes
4answers
21k views

Interpreting random effect variance in glmer

I'm revising a paper on pollination, where the data are binomially distributed (fruit matures or does not). So I used glmer with one random effect (individual plant)...
14
votes
1answer
6k views

R-squared in linear model verses deviance in generalized linear model?

Here's my context for this question: From what I can tell, we cannot run an ordinary least squares regression in R when using weighted data and the survey package. ...
13
votes
4answers
51k views

Reporting results of a logistic regression

I have the following logistic regression output: ...
12
votes
0answers
6k views

Is the percent of total deviance explained a useful model summary? [duplicate]

My question is regarding the interpretation of the percent of deviance explained (and other $R^2$ anaologs or pseudo $R^2$ values for GLMs. Is this a meaningful summary statistic for models other ...
11
votes
3answers
10k views

Residuals for logistic regression and Cook's distance

Are there any particular assumptions regarding the errors for logistic regression such as the constant variance of the error terms and the normality of the residuals? Also typically when you have ...
9
votes
1answer
4k 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 ...
7
votes
2answers
7k views

What is a “good fit” Brier score and Harrell's C Index

This is a question I originally posted on r-help but it is more suited here. I will post the question and the answer I received from Dr. Winsemius and would be most grateful for any additional answers ...
6
votes
2answers
3k views

Find out pseudo R square value for a Logistic Regression analysis [closed]

My name is Tuhin. I came up with a couple of questions when I was doing an analysis in R. I did a logistic regression analysis in R and tried to check how good the model fits the data. But, I got ...
6
votes
1answer
499 views

Is a logit model with a pseudo-R^2 of less than 0.5 a worse model than a coin toss?

I have recently encountered the remark that if a logit model's pseudo $R^2$ is lower than $0.5$ the result is completely worthless because a coin toss is a better model. Is this interpretation correct?...
5
votes
1answer
1k views

Fewer variables have higher R-squared value in logistic regression

I am testing out 3 modeling approaches for malnutrition in children. Theoretically, distal determinants (education,poverty) operate through proximal determinants (water, sanitation) in determining ...
5
votes
2answers
1k views

Which measure of model fit to report when performing likelihood based regression: AIC, BIC, Pseudo R-square?

I'd like to hear your opinions on the following: What parameters would you report when estimating different likelihood based regression? AIC, BIC, Pseudo $R^2$? What is the standard to report? It ...

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