Is there any explanation for strong, adequate or low value of it? Thank you
It is a measure of goodness of fit in logistic regression analysis. It is a modification of the Cox and Snell R Square, which is derived from the likelihood ratio test statistic.
Nagelkerke R Square ranges from 0 to 1, with values closer to 1 indicating a better fit of the model. However, unlike in linear regression analysis, where R Square can be interpreted as the proportion of variance explained by the model, Nagelkerke R Square cannot be interpreted as easily.
A common rule of thumb is to interpret it, a value of 0.2 or less indicates a weak relationship between the predictors and the outcome.
A value of 0.2 to 0.4 indicates a moderate relationship.
A value of 0.4 or higher indicates a strong relationship.
However, it's important to note that the interpretation of Nagelkerke R Square should be taken with caution and should be supplemented by other measures of model fit, such as the Hosmer-Lemeshow test, AIC, or BIC. Additionally, it's essential to consider the practical significance of the relationship between the predictors and the outcome, rather than solely relying on statistical significance or goodness of fit measures.
hi Prastiwi Wahyu Dyah
I found these for you: please check these out:
1) https://www.researchgate.net/post/What_is_the_minimum_acceptable_limit_of_Nagelkerke_R_Square_value_in_binary_logit_regression_model
2)https://www.researchgate.net/post/are_high_nagelkerke_R2_values_suspicious_in_a_logistic_regression_model
3)https://statisticalhorizons.com/r2logistic/
4)https://web.pdx.edu/~newsomj/cdaclass/ho_logistic.pdf
5)https://support.sas.com/resources/papers/proceedings14/1485-2014.pdf
and a good article i found is :
6)moz-extension://86a141b6-b608-4b85-9280-accf4d91c039/enhanced-reader.html?openApp&pdf=https%3A%2F%2Farxiv.org%2Fpdf%2F1701.07745.pdf
maybe these help you to understand it well.
Good luck
This might be helpful:
Chapter Pseudo-R2 and related measures. Online Supplement 4 to Serio...
(Note that in general these measures are not very useful)
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