| Cstat {DescTools} | R Documentation |
Calculate the C statistic, a measure of goodness of fit for binary outcomes in a logistic regression or any other classification model. The C statistic is equivalent to the area under the ROC-curve (Receiver Operating Characteristic).
Cstat(x, ...) ## S3 method for class 'glm' Cstat(x, ...) ## Default S3 method: Cstat(x, resp, ...)
x |
the logistic model for the glm interface or the predicted probabilities of the model for the default. |
resp |
the response variable (coded as c(0, 1)) |
... |
further arguments to be passed to other functions. |
Values for this measure range from 0.5 to 1.0, with higher values indicating better predictive models. A value of 0.5 indicates that the model is no better than chance at making a prediction of membership in a group and a value of 1.0 indicates that the model perfectly identifies those within a group and those not. Models are typically considered reasonable when the C-statistic is higher than 0.7 and strong when C exceeds 0.8.
Confidence intervals for this measure can be calculated by bootstrap.
numeric value
Andri Signorell <andri@signorell.net>
Hosmer D.W., Lemeshow S. (2000) Applied Logistic Regression (2nd Edition). New York, NY: John Wiley & Sons
r.glm <- glm(Survived ~ ., data=Untable(Titanic), family=binomial)
Cstat(r.glm)
# default interface
Cstat(x = predict(r.glm, method="response"),
resp = model.response(model.frame(r.glm)))