Linked Questions

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
765 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 ...

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