Questions tagged [pseudo-r-squared]

a measure of how well variables of the model explain some phenomenon

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R package that calculates out-of-sample pseudo $R^2$ used to compare probit models [closed]

I have multiple binomial datasets that I fit probit models to. I would like to compare how well each model fits each dataset. One way I want to do this is using McFadden's out-of-sample pseduo R^2. Is ...
scott.pilgrim.vs.r's user avatar
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How can we justfify the assumption of equal scale/variance in the definition of R-squared from Deviances in GLMs?

If we take the R-squared to be the comparison of Deviances between models (the model of interest, the saturated model, and the constant model), we can write it as (see this answer CC BY-SA 4.0): $$R_{...
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Pseudo $R^2$ for probit model: In-sample or out-of-sample?

I have a dataset test_data that measures mortality in response to dosage of a pesticide. I used a probit model that evaluates the efficacy of a single pesticide. ...
scott.pilgrim.vs.r's user avatar
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Mixed effects model for repeated measures approach in R

I'm using mixed effects models for repeated measures (MMRM) in R with the nlme package for the first time as part of a research project and have read lots of posts ...
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Nagelkerke's Pseudo-R² is negative? How to fix this?

I am working on my Master's Thesis and I was fitting various glms on my data; and since I can't calculate adjusted R² values for my models, I opted for Nagelkerke's pseudo R². I used the rcompanion ...
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Relative importance in hurdle model: which metric to use?

I want to calculate the relative importance of predictors of a hurdle model, my first choice is dominance analysis. For that I would need a suitable metric of model quality. My first thought is to use ...
M. Riera's user avatar
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Easy goodness of fit tests when using svyolr package (ordered logit regression on complex survey data)?

for a uni project I am running an ordered logit regression on survey data, using the svyolr package in R. I am looking to report some sort of goodness of fit for the model but am struggling to find ...
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How can I directly compare the effects of a predictor on a continuous and a dichotomous dependent variable?

I want to assess how a predictor differs in its associations with 2 different dependent measures (i.e., a continuous measure of objective memory and a categorical measure of self-rated memory). I also ...
may.the.bee's user avatar
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statistics linking McFadden's $R^2$ to the relationship between two binary variables, akin to correlation (Copula with Bernoulli margins?)

My goal is to create a visualization of the strength of the McFadden's $R^2$ of a (multinomial) logistic regression, where McFadden's $R^2$ is $1-\dfrac{LL(M_1)}{LL(M_0)}$, involving the ratio of the ...
Dave's user avatar
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How to interpret the UCLA "adjusted count" logistic regression pseudo $R^2?$

Here, UCLA gives a number of pseudo $R^2$ values for evaluating logistic regression models. Despite the issues with doing this, the last two deal with hard classifications rather than the ...
Dave's user avatar
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Bootstrap difference in R2 between two models

I apologise beforehand if this is already answered elsewhere, but I have searched for this particular question and didn’t find a post that matched it. I have two logistic regression models: a base ...
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How to confirm the correlation between independent variables and dependent variable when Pseudo-R² is low?

I have a logistic regression with 10 independent variables. I want that one of these independent variables is correlated with the dependent variable, others are covariants. In this regression model, p ...
you lin's user avatar
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Quantile Regression Pseudo R-Squared

When computing the Pseudo R-squared value in quantreg in R or statsmodel in Python, what is an acceptable range to justify goodness of fit? Also, what is the functional form of Pseudo R-Squared ...
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Logistic regression: How to improve the pseudo-$R^2?$

Firstly, I would like to ask you opinion about my model: I have a logistic regression model where the dependent variable is late payments (1=late/0=not late) and diesel prices and interest rates as ...
user373185's user avatar
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Confused by the pseudo R2 for my zero-inflated regression

Data Here is the data I am using, which records my coffee consumption and productivity habits by day: ...
Shawn Hemelstrand's user avatar
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McKelvey and Zavoina's pseudo-$R^2$ score range

I am trying to build a probit regression model. Looking for some proper goodness of fit indicator for my model I read that McKelvey and Zavoina's pseudo-$R^2$ could be the best index for this purpose. ...
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How to calculate a r-squared for a zero-truncated poisson mixed model (glmmTMB)

I am interested to calculate the pseudo-r-squuared for a zero-truncated poisson mixel model (using glmmTMB). The r.squaredglmm (package MuMin) gives a message that it can not calculate pseudo-r2 for ...
Wil Tamis's user avatar
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Why we do not use r squared for logistic regression? [duplicate]

Why we do not use R squared for logistic regression? What is the logic behind it?
Aslı's user avatar
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Are consistently negative Efron's pseudo-r2 in logistic regression possible?

I am conducting logistic regression and looking to calculate pseudo-R2 values alongside AIC and BIC for model evaluation. I selected Efron's pseudo-R2 because of its simple calculation and the ...
stat_is_quo's user avatar
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Comparison of predictor performances in different models

My intention is to test the power of a single predictor x in predicting different responses: y1 that is presence/absence and <...
Kryštof Chytrý's user avatar
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What is the best way to compare fixed and random effects of a GLMM?

I am doing a research in which i am trying to measure the importance of the doctor that is in charge of a patient in a medical decision. For that (and others reasons) i have used a GLMM using lmer4 ...
Thomas_9210's user avatar
3 votes
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Why does McFadden's Pseudo-$R^2$ yield different values for the same model depending on data grouping?

Just stumbled accross the problem that McFaddens Pseudo-$R^2$ yields different values in logistic regression, depending on the grouping of the data. When predictor values occur more than once, there ...
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Calculating different pseudo-$R^2$ for a betareg model

Sorry if this is a bit long.. I've been trying to fit models predicting the % of area infested in a field (response between 0 and 100%, total of 61 fields), with four explanatory variables, two ...
Roni Gafni's user avatar
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Can we create confidence intervals around pseudo-R-squared Random Forest based on the forest?

For linear regression it is possible to place confidence intervals around the R-squared, either by formula or bootstrapping. Random Forest models, as regression model, return the "explained ...
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AIC, pseudo-R2, or log likelihood to compare models?

I am comparing the effect of climate, across three different time brackets, on a variable. I am interested in choosing the model that best predicts the variable to answer across which timescale the ...
lvh's user avatar
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Calculate change in odds and Pseudo-R-Squared with R

I have a question that touches on both technical solutions in R and statistics. I have a huge dataset with 2,400 respondents in total. I performed a logistic regression in order to analyze views on ...
Nicosc's user avatar
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Nakagawa's R2: what does it tell practice?

I am having a hard time figuring what Nakagawa's R² really "means". I understand that in simple linear regressions, R² indicates the amount of variance in the dependent variable explained by ...
yenats's user avatar
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$R^2$ of Logistic Regression Without Intercept?

I am calibrating a logistic regression for a survey data which comes from a binary stated choice experiment. The stated choice experiment was an unlabeled one, which means that all the variables ...
Gabriel Souza's user avatar
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Will the R-Squared be lower if I run the same model on a dataset with all the data vs a dataset with the quarterly averages?

Let's assume I have a dataset with quarterly data on loans. Let's say I have 100,000 of these loans, spread out in 20 quarters. If I run a regression on all the loans, I will have 100,000 data points. ...
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Logistic regression with high McFadden pseudo-R-squared value and high p-value

I performed a logistic regression of an outcome variable (whether a patient is re-admitted to a hospital within a year) against a continuous index that measures a patient's access to healthcare. I ...
Max's user avatar
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4 votes
2 answers
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Basic R-Squared in Poisson Regression

I have read one cannot/should not calculate the basic R-Squared used in linear regression for a Poisson generalized linear regression model. It is logical to me that one cannot determine the basic R-...
Benykō-Zamurai's user avatar
2 votes
1 answer
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Develop granularity-invariant criteria for comparison of logistic (binomial) models

I have a model with logistic (binomial) likelihood, with number of successes and failures as a response variable. I am comparing various models, which can be of different granularity. Different ...
Tomas's user avatar
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4 votes
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Interpreting pseudo-R² in GLMM

Different approaches to pseudo-R² naturally yield different results. For example, Nagelkerkes pseudo-R² tends to yield higher results than McFaddens pseudo-R². As I am not a statistician, it thus can ...
yenats's user avatar
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Is it possible to calculate a pseudo-R squared for a binomial GLMM with a cauchit link?

I'm modeling some repeated-measures presence-absence data using a binomial GLMM in lme4. I've been using the method suggested by Nakagawa and Schielzeth (2013) to calculate a marginal and conditional ...
yeticrab's user avatar
64 votes
5 answers
134k 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: ...
dfrankow's user avatar
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72 votes
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Which pseudo-$R^2$ measure is the one to report for logistic regression (Cox & Snell or Nagelkerke)?

I have SPSS output for a logistic regression model. The output reports two measures for the model fit, Cox & Snell and ...
Henrik's user avatar
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