Keyword Analysis & Research: nagelkerke r2

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Keyword CPC PCC Volume Score Length of keyword
nagelkerke r20.960.329177913
nagelkerke0.830.2377710
r21.50.22092422

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Keyword CPC PCC Volume Score
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nagelkerke pseudo r21.120.632815
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  • nagelkerke function - RDocumentation

    rdocumentation.org

    https://www.rdocumentation.org/packages/rcompanion/versions/2.4.15/topics/nagelkerke

    Feb 04, 2015 · Nagelkerke is also referred to as Cragg and Uhler. Model objects accepted are lm, glm, gls, lme, lmer, lmerTest, nls, clm, clmm, vglm, glmer, negbin, zeroinfl, betareg, and rq. Model objects that require the null model to be defined are nls, lmer, glmer, and clmm. Other objects use the update function to define the null model.

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  • NagelkerkeR2 function - RDocumentation

    rdocumentation.org

    https://www.rdocumentation.org/packages/fmsb/versions/0.7.3/topics/NagelkerkeR2

    The Nagelkerke's R squared means the power of explanation of the model. Usage NagelkerkeR2 (rr) Arguments rr The object with class "glm" and "lm", which would be generated by glm (). Value N The number of observations in which the model were fitted. R2 Nagelkerke's R squared. References

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  • Interpreting Nagelkerke R2 - Google Groups

    google.com

    https://groups.google.com/g/medstats/c/Y5SRiPGdvuU

    Feb 08, 2006 · SR Millis. Unfortunately, there's not an intuitive interpretation for the various pseudo-R^2s that can be derived in logistic regression. Quite literally, these R^2s represent the proportional reduction in the absolute value of the log-likelihood measure---not the amount of variance accounted for, as in the OLS context. When evaluation logistic ...

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  • [Q] Interpreting Nagelkerke R2 : statistics - reddit

    reddit.com

    https://www.reddit.com/r/statistics/comments/f2qexo/q_interpreting_nagelkerke_r2/

    In short, Nagelkerke's R2 is based on the log-likelihood and is a type of scoring rule (a logarithmic one). It can be used as an overall performance measure of the model. This paper by Steyerberg et al. (2010) explains this really well imo. I think it's very difficult to interpret the value of Nagelkerke's R2 itself.

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  • r2_nagelkerke: Nagelkerke's R2 in performance: Assessment of …

    rdrr.io

    https://rdrr.io/cran/performance/man/r2_nagelkerke.html

    Mar 30, 2022 · r2_nagelkerke(model, ...) Arguments Value A named vector with the R2 value. References Nagelkerke, N. J. (1991). Biometrika, 78(3), 691-692. Examples model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial") r2_nagelkerke(model) performance documentationbuilt on March 30, 2022, 9:06 a.m. Related to r2_nagelkerkein performance...

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  • logistic - Nagelkerke $R^2$ interpretation - Cross Validated

    stackexchange.com

    https://stats.stackexchange.com/questions/140509/nagelkerke-r2-interpretation

    Mar 06, 2015 · I used logistic regression and found that my model fits well: Deviance Residuals: Min 1Q Median 3Q Max -1.6434 -1.4623 0.8704 0.9013 1.0066 Coefficients: ...

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  • nagelkerke : Pseudo r-squared measures for various models

    rdrr.io

    https://rdrr.io/cran/rcompanion/man/nagelkerke.html

    Mar 18, 2022 · Produces McFadden, Cox and Snell, and Nagelkerke pseudo r-squared measures, along with p-values, for models. Usage nagelkerke(fit, null = NULL, restrictNobs = FALSE) Arguments. fit: The fitted model object for which to determine pseudo r-squared. null:

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  • Are high nagelkerke R2 values suspicious in a logistic …

    researchgate.net

    https://www.researchgate.net/post/are_high_nagelkerke_R2_values_suspicious_in_a_logistic_regression_model

    High R2 de nagelkelke are not necessarily suspicious of anything. R2 de is a fitness index and should be compared to each other (in various models), the higher the …

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  • R squared in logistic regression – The Stats Geek

    thestatsgeek.com

    https://thestatsgeek.com/2014/02/08/r-squared-in-logistic-regression/

    Feb 08, 2014 · As I understand it, Nagelkerke’s psuedo R2, is an adaption of Cox and Snell’s R2. The latter is defined (in terms of the likelihood function) so that it matches R2 in the case of linear regression, with the idea being that it can be generalized to other types of model. However, once it comes to say logistic regression, as far I know Cox ...

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