Contents
How do you read the Hosmer and Lemeshow test?
This test is usually run using technology. The output returns a chi-square value (a Hosmer-Lemeshow chi-squared) and a p-value (e.g. Pr > ChiSq). Small p-values mean that the model is a poor fit. Like most goodness of fit tests, these small p-values (usually under 5%) mean that your model is not a good fit.
What is Hosmer Lemeshow test used for?
The Hosmer–Lemeshow test is a statistical test for goodness of fit for logistic regression models. It is used frequently in risk prediction models. The test assesses whether or not the observed event rates match expected event rates in subgroups of the model population.
How is the Hosmer Lemeshow goodness of fit test calculated?
The Hosmer-Lemeshow test is a statistical test for goodness of fit for the logistic regression model. The data is divided into a number of groups (ten groups is a good way to start). The observed and expected number of cases in each group is calculated and a Chi-squared statistic is calculated as follows:
How is the Hosmer-Lemeshow test used in logistic regression?
The Hosmer-Lemeshow test is used to determine the goodness of fit of the logistic regression model. Essentially it is a chi-square goodness of fit test (as described in Goodness of Fit) for grouped data, usually where the data is divided into 10 equal subgroups.
How are observations grouped in the Hosmer Lemeshow test?
The key concept of the Hosmer-Lemeshow statistic is that, instead of observations being grouped by the values of the x variable (s), the observations are grouped by expected probability. That is, observations with similar expected probability are put into the same group, usually to create approximately 10 groups.
How is the goodness of fit measured in your 2?
R 2 measures by themselves never measure goodness of fit; they measure mainly predictive discrimination. Goodness of fit only comes from comparing R 2 with the R 2 from a richer model The Hosmer-Lemeshow test is for overall calibration error, not for any particular lack of fit such as quadratic effects.