For logistic regression models, performs a Chi-squared goodness-of-fit-test.
chisq_gof(x, prob = NULL, weights = NULL)
A numeric vector or a
Vector of probabilities (indicating the population probabilities)
of the same length as
Vector with weights, used to weight
For vectors, returns the object of the computed
glm-objects, an object of class
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
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 #>