Linked Questions

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 ...
3
votes
0answers
692 views

calculating R square for a logistic regression [duplicate]

can anyone help me with how to calculate R-squared for a logistic regression- How do I use the deviance for this Purpose ? Additionaly, the question in matlab: I am calculating a psychometric ...
0
votes
1answer
447 views

Choosing between alternate pseudo R squared methods [duplicate]

My project uses logistic regression and I needed to calculate pseudo r squared to understand the explanatory power of each model. I have been using the formula below to calculate what I thought was ...
0
votes
0answers
402 views

Reporting Cox & Snell and Nagelkerke in logistical regression [duplicate]

As part of an assignment, I've been asked to report Cox & Snell and Nagelkerke values for my (logistical) regression analyses. I have been asked to interpret them as well as discuss what they ...
0
votes
1answer
163 views

Adjusted R Square For Binomial [duplicate]

mtcars=data.frame(mtcars) m=glm(vs~mpg+cyl+disp+hp+drat,family="binomial",data=mtcars) m=glm(vs~wt,family="binomial",data=mtcars) I seek to estimate pseudo-r-...
0
votes
0answers
16 views

About binary logistic regression [duplicate]

I have been told that Nagelkerke should not be used in a model of binary logistic regression, but instead a R2 as a measure of goodness of fit. So, how can I apply R2 if I am not using a linear ...
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: ...
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 ...
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 ...
13
votes
4answers
51k views

Reporting results of a logistic regression

I have the following logistic regression output: ...
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)...
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 ...
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 ...
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. ...
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 ...
4
votes
2answers
5k views

Measuring the performance of Logistic Regression

Being quite new to the field, it occurs to me that there are multiple and fundamentally different ways of assessing the quality of a logistic regression: One can evaluate it by looking at the ...
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 ...
2
votes
1answer
3k views

Variation explained in ordinal logistic regression models

I have made these three ordinal logistic regression models: ...
0
votes
3answers
7k views

how to calculate R-squared in glm?

I came up with below for my glm analysis but I need to calculate R-squared to cite in the paper? anyone can help me with this please? summary(lrfit) Call: ...
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 ...
4
votes
2answers
2k views

Goodness of fit in GLMs

I am searching for a good criterion to measure the "goodness of fit" in generalized linear models. To make clear: I am not searching for a criterion which gives me an answer to the question "does ...
4
votes
1answer
2k views

Pseudo-$R^2$: what are the null models for linear and non-linear regressions?

I have data from an experiment. The independent variable is time, the dependent variable is mass loss of organic matter. Now I want to compare whether a linear or a non-linear model fits better. From ...
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 ...
1
vote
1answer
2k views

Regression model for ordinal dependent variable and categorical independent variables

If I'm using R, which regression model should I use for my dataset? (I need to get the R-squared value.) I have 1 dependent variable and 6 independent variables as follows: 1 dependent variable: ...
3
votes
2answers
604 views

Are the $1-SSe/SSt$ and $cor^2$ calculations of $R^2$ always equivalent?

I am trying to calculate the $R^2$ value for a production constrained spatial interaction model, using Fotheringham and O'Kelly (1989) as my guide. I get dramatically different values for R-Square, ...
2
votes
0answers
3k views

Nagelkerke $R^2$ interpretation

I used logistic regression and found that my model fits well: ...
3
votes
1answer
1k views

How to assess GLMM performance on a new data set?

I built a generalized linear mixed-effects model (GLMM) using glmer function from the lme4 package in ...
1
vote
0answers
2k views

pseudo R-squared for model estimated with maximum likelihood

I want to compute a pseudo-$R^2$ for a model whose parameter estimation was based on maximum likelihood (function likfit(), package ...
5
votes
1answer
815 views

Why is it futile to use the deviance as a goodness-of-fit measure for Bernoulli data?

In Ordinal Data Modelling by Johson & Albert, page 102-103: For Bernoulli observations [...] the asymptotic chi-squared distribution of the deviance statistic may not pertain. Indeed, for ...
6
votes
1answer
509 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?...
3
votes
1answer
635 views

Understanding R output in Logistic Regression

I am following an example here on using Logistic Regression in R. However, I need some help interpreting the results. They do go over some of the interpretations in the above link, but I need more ...
0
votes
0answers
1k views

McFadden R Square

I did multiple regression analysis, and i have one little problem about interpreting the output. What does McFadden R-Square means, if it is 0,196. What it shows me? Is Mcfadden the best solution, or ...
0
votes
1answer
762 views

Cox & Snell $R^2$ rule of thumb threshold

Like p value is usually compared to 0.05, What is the magic number that is considered a good fit for a logistic regression Cox &...
1
vote
2answers
294 views

Logistic regression: forcing linearity (automated feature creation)

Suppose we want to fit a logistic regression on a binary outcome y and we have limited set of continuous independent variables x1...
1
vote
2answers
106 views

How well does my logistic regression model fit?

I performed a logistic regression to my dataset which has 6 variables. I got output from R as the following: I used the step() ...
2
votes
0answers
730 views

Why is R2 not reported for GLMs based on last iteration of IRLS weighted least square regression with which it is fit

Given that GLMs are generally fit using iteratively reweighted least squares (based on a Fisher scoring algorithm to maximize the max likelihood objective, which is a variant of Newton-Raphson, see ...
1
vote
0answers
392 views

How to interpret coefficients (and R²) of an -oprobit- model (STATA 13)?

I'm fitting a "oprobit" model in STATA 13 and I can't wrap my head around how to interpret the coefficients. This is the model that I'm running: ...
1
vote
1answer
200 views

Should I report the pseudo $R^2$ value for full or final logistic regression model after removing NA's & running stepwise selection?

I'm working with a logistic regression model in r. model <- glm(response~., family="binomial", data) and I'm using ...
2
votes
0answers
218 views

Why could pseudo $R$-squared (pseudo $R^2$) increase when I remove variables?

I have a multinomial logistic regression with $11$ independent variables. When I remove variables, the pseudo $R$-squared increases. Isn't this not supposed to happen? Why could this be happening?
1
vote
2answers
164 views

Excluding the effect of control variables in the assessment of a logistic regression model

I have a logistic regression model with ten independent variables of which two are included as controls. While their inclusion is necessary for correctly assessing the coefficients of the other ...
1
vote
1answer
124 views

In binary logistic regression, must the binary Y be interpreted as the dependent variable?

If I have a binary variable, say sex, and I want to test whether multiple other variables are associated with it. To do this, I run a logistic regression of the form \begin{equation} logit(...
3
votes
0answers
201 views

theoretical concerns in logistic regression

I have a dataset with 260 patients. I aim to study factors associated the certain finding in magnetic resonance imaging. I use logistic regression with six predictors. Regression yields to several ...
5
votes
0answers
151 views

What are the pros and cons of different metrics for evaluating a logistic regression model?

In the data science world, I have always evaluated the performance of logistic regression models simply using ROC/AUC. However recently, I've read from some traditional statistics source about some ...
0
votes
0answers
178 views

Likert Scale for Linear Regression vs Ordinal Logistic Regression - R Square Interpretation

I'm fitting a response variable that assume values between 1 (Very Dissatisfied), 2 ,3 ,4 and 5 (Very Satisfied). My explanatory variable assumes also values between 1 and 5, in other words, dependent ...
0
votes
1answer
71 views

Comparing GLME fits for different data

I have a data set consisting of two behavioral response variables where each response variable is associated with a relevant physiological predictor, with many trials for each subject. The hypothesis ...
1
vote
1answer
25 views

Explaining variance with Nagelkerke's R2 - null vs full model?

I was hoping someone would be able to help me understand this - I have calculated Nagelkerke's R2 for the generalised linear model results with covariates included. ie: ...
0
votes
0answers
14 views

understanding R2 in probit

I try to create a model to predict football (socker) results with a performance variable. It doesn't really matter how this performance is calculated since any performance variable is an adequote ...