Statistical Analysis Stack Exchange
Statistical Analysis Stack Exchange

Questions tagged [pseudo-r-squared]

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

Pseudo R2 is a measure of how well variables of the model explain some phenomenon. If we catch with our variables more than 0,5 we can form our expectation for the model, but there are other unexplained issues and then try to find other factors that can explain and test our thesis.

Psuedo r-squared is a legitimate thing that can be reported. However, you want to find out from the software documentation which pseudo-r-square it's reporting. (Some common ones are listed here: .

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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: ced.logr <- glm(ced.del ~ cat + follows + factor(class), family=binomial) Some output: > summary(ced.logr) Call: glm(formula = ced.del ~ cat +…
dfrankow
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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-Squared for a logistic regression, since the…
Benkyozamurai
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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 be somewhat difficult to interpret pseudo-R². In…
yenats
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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 the predictor variables. For the diverse pseudo-R²…
yenats
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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 describing the two alternatives are generic (time and…
Gabriel Souza
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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 y2that is continuous. I therefore fitted two models (I work in R environment): lm(y1 ~ x) glm(y2 ~ x, family =…
Kryštof Chytrý
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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 are two ways to specify the model: as binary data…
cdalitz
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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 factorials (planting and monitor) and two covariates…
Roni Gafni
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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 granularity means that the binomial observations can be…
Tomas
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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 that looks like that : FitGlmm <-…
Thomas_9210
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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 variation", or better, "captured variation". However, I…
A4-paper
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Why do I get an R² Nagelkerke even though my response is numerical?

I just fitted a GLM with a Gamma distribution and somehow my output gives me a pseudo R². (Nagelkerke). As far as I know a pseudo R² is only used when the response is ordinal or nominal, but my response is numerical. Why do I get a pseudo R²? Below…
Effigy
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R2 for mixed-effects Conway-Maxwell Poisson using package glmmTMB

After running a mixed-effects Conway-Maxwell Poisson model using glmmTMB, I've printed the results using sjPlot's tab_model() but I don't know what R2 calculation is being used. Is it Nakagawa's R2 since it is a mixed-effects model? Also, I've come…
Leandra
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Nagelkerke pseudo R^2 interpretation in spatial regression

Could anyone explain to me the interpretation of Nagelkerke pseudo R^2 in spatial regression models? Definitions say it is computed using some conditonal probability, but I didn't quite get what they mean by that.
MaryBerryCherry
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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 climate effect most strongly affects my variable.…
lvh
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