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Question: I am testing functions in a package that I am developing and would like to know if you can suggest some general guidelines for how to do this. The functions include a large range of statistical modeling, transformations, subsetting, and plotting. Is there a 'standard' or some sufficient test?
An Example: the test that prompted me ask this question,
The function dtheta:
dtheta <- function(x) {
## find the quantile of the mean
q.mean <- mean(mean(x) >= x)
## find the quantiles of ucl and lcl (q.mean +/- 0.15)
q.ucl <- q.mean + 0.15
q.lcl <- q.mean - 0.15
qs <- c(q.lcl, q.mean, q.ucl)
## find the lcl, mean, and ucl of the vector
c(quantile(x,qs), var(x), sqrt(var(x))/mean(x))
}
Step 1: make test data:
set.seed(100) # per Dirk's recommendation
test <- rnorm(100000,10,1)
Step 2: compare the expected output from the function with the actual output from the function:
expected <- quantile(test, c(0.35, 0.65, 0.5))
actual <- dtheta(test)[1:3]
signif(expected,2) %in% signif(actual,2)
Step 3: maybe do another test
test2 <- runif(100000, 0, 100)
expected <- c(35, 50, 65)
actual <- dtheta(test2)
expected %in% signif(actual,2)
Step 4: if true, consider function 'functional'