Nothing

```
library(QCApro)
context("ambiguity")
# dataset
data(d.tumorscreen)
# error messages
test_that("ambiguity analyzes design variations", {
expect_error(ambiguity(d.tumorscreen, outcome = ""),
"No outcome/s is/are specified")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", neg.out = c(TRUE, FALSE, TRUE)),
"The argument 'neg.out' must be")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", neg.out = c(TRUE, TRUE)),
"The argument 'neg.out' must be")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", neg.out = c(TRUE, "F")),
"The argument 'neg.out' must be")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", tuples = c()),
"At least one tuple")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", exo.facs = c("CA","AR","RR","DR","DC","PR"),
tuples = 1:5),
"The minimum tuple size is two")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", exo.facs = c("CA","AR","RR","DR","DC","PR"),
tuples = 3:7),
"The minimum tuple size is two")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", exo.facs = c("CA","AR","RR","DR","DC","PR"),
tuples = 3:5, incl.cut1 = c(1), incl.cut0 = c(1,0.8)),
"The vectors of inclusion cut-offs")
expect_error(ambiguity(d.tumorscreen, outcome = "HPF", exo.facs = c("CA","AR","RR","DR","DC","PR"),
tuples = 3:5, row.dom = c(TRUE), min.dis = c(TRUE, FALSE)),
"The vectors of the arguments")
})
```

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