Multinomial goodness-of-fit tests for logistic regression models

2008 ◽  
Vol 27 (21) ◽  
pp. 4238-4253 ◽  
Author(s):  
Morten W. Fagerland ◽  
David W. Hosmer ◽  
Anna M. Bofin
Keyword(s):  
Logistic Regression ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Logistic Regression Models ◽  
Goodness Of Fit Tests

On Graphically Checking Goodness-of-fit of Binary Logistic Regression Models

2009 ◽  
Vol 48 (03) ◽  
pp. 306-310 ◽  
Author(s):  
C. E. Minder ◽  
G. Gillmann
Keyword(s):  
Logistic Regression ◽  
Regression Analysis ◽  
Regression Models ◽  
Goodness Of Fit ◽  
Graphical Method ◽  
Logistic Models ◽  
Binary Logistic Regression ◽  
Logistic Regression Models ◽  
Logistic Analysis ◽  
Viable Solution

Summary Objectives: This paper is concerned with checking goodness-of-fit of binary logistic regression models. For the practitioners of data analysis, the broad classes of procedures for checking goodness-of-fit available in the literature are described. The challenges of model checking in the context of binary logistic regression are reviewed. As a viable solution, a simple graphical procedure for checking goodness-of-fit is proposed. Methods: The graphical procedure proposed relies on pieces of information available from any logistic analysis; the focus is on combining and presenting these in an informative way. Results: The information gained using this approach is presented with three examples. In the discussion, the proposed method is put into context and compared with other graphical procedures for checking goodness-of-fit of binary logistic models available in the literature. Conclusion: A simple graphical method can significantly improve the understanding of any logistic regression analysis and help to prevent faulty conclusions.

scholarly journals How to Test for Goodness of Fit in Ordinal Logistic Regression Models

2017 ◽  
Vol 17 (3) ◽  
pp. 668-686 ◽  
Author(s):  
Morten W. Fagerland ◽  
David W. Hosmer
Keyword(s):  
Regression Models ◽  
Goodness Of Fit ◽  
Logistic Models ◽  
Ratio Test ◽  
Response Variable ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Goodness Of Fit Tests ◽  
Interaction Terms ◽  
Different Types

Ordinal regression models are used to describe the relationship between an ordered categorical response variable and one or more explanatory variables. Several ordinal logistic models are available in Stata, such as the proportional odds, adjacent-category, and constrained continuation-ratio models. In this article, we present a command (ologitgof) that calculates four goodness-of-fit tests for assessing the overall adequacy of these models. These tests include an ordinal version of the Hosmer–Lemeshow test, the Pulkstenis–Robinson chi-squared and deviance tests, and the Lipsitz likelihood-ratio test. Together, these tests can detect several different types of lack of fit, including wrongly specified continuous terms, omission of different types of interaction terms, and an unordered response variable.

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