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


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
#> # 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