## Hosmer-lemeshow goodness-of-fit statistics for `blm`

and `lexpit`

objects.

### Description

Computes the deviance and Pearson chi-squared statistics for the fit from a `blm`

or `lexpit`

model. These tests are appropriate when all predictors are categorical and there are many replicates within each covariate class.

### Value

Returns a list with `table`

, with expected `E`

and observed `O`

, and the chi-square test `chisq`

and p-value (`p.value`

) for the Pearson goodness-of-fit test. The observed and expected count are listed in the order of the unique levels formed by the design matrix.

When sample weights are present, the goodness-of-fit test is a modified F-test as suggested by Archer et al. (2007).

### usage

gof(object)

### arguments

- object
instance of `blm`

or `lexpit`

### Author(s)

Stephanie Kovalchik s.a.kovalchik@gmail.com

### References

Archer KJ, Lemeshow S, Hosmer DW. Goodness-of-fit tests for logistic regression models when data are collected using a complex sampling design. *Computational Statistics & Data Analysis*. 2007;51:4450–4464.

### See Also

`blm`

, `lexpit`

### Examples

```
data(ccdata)
ccdata$packyear <- ccdata$packyear+runif(nrow(ccdata))
# UNWEIGHTED GOF
fit <- blm(y~female+packyear,data = ccdata)
gof(fit)
# WEIGHTED GOF
fit <- blm(y~female+packyear,data = ccdata, weight = ccdata$w)
gof(fit)
```

[Package

*blm* version 2022.0.0.1

Index]