C Statistic (Area Under the ROC Curve)
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
d.titanic = Untable(Titanic) r.glm <- glm(Survived ~ ., data=d.titanic, family=binomial) Cstat(r.glm) # default interface Cstat(x = predict(r.glm, method="response"), resp = model.response(model.frame(r.glm))) # calculating bootstrap confidence intervals FUN <- function(d.set, i) { r.glm <- glm(Survived ~ ., data=d.set[i,], family=binomial) Cstat(r.glm) } ## Not run: library(boot) boot.res <- boot(d.titanic, FUN, R=999) # the percentile confidence intervals boot.ci(boot.res, type="perc") ## BOOTSTRAP CONFIDENCE INTERVAL CALCULATIONS ## Based on 999 bootstrap replicates ## ## CALL : ## boot.ci(boot.out = res, type = "perc") ## ## Intervals : ## Level Percentile ## 95% ( 0.7308, 0.7808 ) ## Calculations and Intervals on Original Scale ## End(Not run)
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