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Calculate prevalence, sensitivity, specificity, and positive and negative predictive values
diagtest( table, positive = 2, exact = FALSE, p0 = NA, confint = c("logit", "arcsin", "pseudoscore", "exact"), ... )
Table or (matrix/data.frame with two columns)
Switch reference
If TRUE exact binomial proportions CI/test will be used
Optional null hypothesis (test prevalenc, sensitivity, ...)
Type of confidence limits
Additional arguments to lower level functions
Klaus Holst
Table should be in the format with outcome in columns and test in rows. Data.frame should be with test in the first column and outcome in the second column.
M <- as.table(matrix(c(42,12, 35,28),ncol=2,byrow=TRUE, dimnames=list(rater=c("no","yes"),gold=c("no","yes")))) diagtest(M,exact=TRUE)
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