For logistic regression models, performs a Chi-squared goodness-of-fit-test.
chisq_gof(x, prob = NULL, weights = NULL)A numeric vector or a glm-object.
Vector of probabilities (indicating the population probabilities)
of the same length as x's amount of categories / factor levels.
Use nrow(table(x)) to determine the amount of necessary values
for prob. Only used, when x is a vector, and not a
glm-object.
Vector with weights, used to weight x.
For vectors, returns the object of the computed chisq.test.
For glm-objects, an object of class chisq_gof with
following values: p.value, the p-value for the goodness-of-fit test;
z.score, the standardized z-score for the goodness-of-fit test;
rss, the residual sums of squares term and chisq, the pearson
chi-squared statistic.
For vectors, this function is a convenient function for the
chisq.test(), performing goodness-of-fit test. For
glm-objects, this function performs a goodness-of-fit test.
A well-fitting model shows no significant difference between the
model and the observed data, i.e. the reported p-values should be
greater than 0.05.
Hosmer, D. W., & Lemeshow, S. (2000). Applied Logistic Regression. Hoboken, NJ, USA: John Wiley & Sons, Inc. doi:10.1002/0471722146
data(efc)
efc$neg_c_7d <- ifelse(efc$neg_c_7 < median(efc$neg_c_7, na.rm = TRUE), 0, 1)
m <- glm(
neg_c_7d ~ c161sex + barthtot + c172code,
data = efc,
family = binomial(link = "logit")
)
# goodness-of-fit test for logistic regression
chisq_gof(m)
#>
#> # Chi-squared Goodness-of-Fit Test
#>
#> Chi-squared: 852.765
#> z-score: 1.025
#> p-value: 0.305
#>
#> Summary: model seems to fit well.
# goodness-of-fit test for vectors against probabilities
# differing from population
chisq_gof(efc$e42dep, c(0.3,0.2,0.22,0.28))
#>
#> Chi-squared test for given probabilities
#>
#> data: dummy
#> X-squared = 234.76, df = 3, p-value < 2.2e-16
#>
# equal to population
chisq_gof(efc$e42dep, prop.table(table(efc$e42dep)))
#>
#> Chi-squared test for given probabilities
#>
#> data: dummy
#> X-squared = 0, df = 3, p-value = 1
#>