## ----------------------------------------------------------------------------- library(MKclass) ## ----------------------------------------------------------------------------- ## Example from Wikipedia risks(p0 = 0.4, p1 = 0.1) risks(p0 = 0.4, p1 = 0.5) ## ----------------------------------------------------------------------------- or2rr(or = 1.5, p0 = 0.4) or2rr(or = 1/6, p1 = 0.1) ## ----------------------------------------------------------------------------- ## Example from Wikipedia rrCI(a = 15, b = 135, c = 100, d = 150) rrCI(a = 75, b = 75, c = 100, d = 150) ## ----------------------------------------------------------------------------- x <- c(runif(50, max = 0.6), runif(50, min = 0.4)) g <- c(rep(0, 50), rep(1, 50)) AUC(x, group = g) ## ----------------------------------------------------------------------------- g <- c(rep(1, 50), rep(0, 50)) AUC(x, group = g) ## no switching AUC(x, group = g, switchAUC = FALSE) ## ----------------------------------------------------------------------------- g <- c(rep(0, 50), rep(1, 50)) AUC.test(pred1 = x, lab1 = g) ## ----------------------------------------------------------------------------- x2 <- c(runif(50, max = 0.7), runif(50, min = 0.3)) g2 <- c(rep(0, 50), rep(1, 50)) AUC.test(pred1 = x, lab1 = g, pred2 = x2, lab2 = g2) ## ----------------------------------------------------------------------------- x3 <- c(x, x2) g3 <- c(g, c(rep(2, 50), rep(3, 50))) pairwise.auc(x = x3, g = g3) ## ----------------------------------------------------------------------------- ## Example: HIV test ## 1. ELISA screening test (4th generation) predValues(sens = 0.999, spec = 0.998, prev = 0.001) ## 2. Western-Plot confirmation test predValues(sens = 0.998, spec = 0.999996, prev = 1/3) ## ----------------------------------------------------------------------------- ## example from dataset infert fit <- glm(case ~ spontaneous+induced, data = infert, family = binomial()) pred <- predict(fit, type = "response") ## with group numbers perfMeasures(pred, truth = infert$case, namePos = 1) perfScores(pred, truth = infert$case, namePos = 1) ## with group names my.case <- factor(infert$case, labels = c("control", "case")) perfMeasures(pred, truth = my.case, namePos = "case") perfScores(pred, truth = my.case, namePos = "case") ## using weights perfMeasures(pred, truth = infert$case, namePos = 1, weight = 0.3) perfScores(pred, truth = infert$case, namePos = 1, wBS = 0.3) ## ----------------------------------------------------------------------------- ## example from dataset infert fit <- glm(case ~ spontaneous+induced, data = infert, family = binomial()) pred <- predict(fit, type = "response") optCutoff(pred, truth = infert$case, namePos = 1) ## ----------------------------------------------------------------------------- ## Hosmer-Lemeshow goodness of fit tests for C and H statistic HLgof.test(fit = pred, obs = infert$case) ## e Cessie-van Houwelingen-Copas-Hosmer global goodness of fit test HLgof.test(fit = pred, obs = infert$case, X = model.matrix(case ~ spontaneous+induced, data = infert)) ## ----------------------------------------------------------------------------- sessionInfo()