counttest {robust2sls}

Count test

Description

counttest() conducts a test whether the number of detected outliers deviates significantly from the expected number of outliers under the null hypothesis that there are no outliers in the sample.

Usage

counttest(
  robust2sls_object,
  alpha,
  iteration,
  one_sided = FALSE,
  tsmethod = c("central", "minlike", "blaker")
)

Arguments

robust2sls_object

An object of class "robust2sls" or a list of such objects.

alpha

A numeric value between 0 and 1 representing the significance level of the test.

iteration

An integer >= 0 or the character "convergence" that determines which iteration is used for the test.

one_sided

A logical value whether a two-sided test (FALSE) should be conducted or a one-sided test (TRUE) that rejects only when the number of detected outliers is above the expected number.

tsmethod

A character specifying the method for calculating two-sided p-values. Ignored for one-sided test.

Details

See outlier_detection() and multi_cutoff() for creating an object of class "robust2sls" or a list thereof.

See exactci::poisson.exact() for the different methods of calculating two-sided p-values.

Value

counttest() returns a data frame with the iteration (m) to be tested, the actual iteration that was tested (generally coincides with the iteration that was specified to be tested but is the convergent iteration if the fixed point is tested), the setting of the probability of exceeding the cut-off (gamma), the number of detected outliers, the expected number of outliers under the null hypothesis that there are no outliers, the type of test (one- or two-sided), the p-value, the significance level alpha, the decision, and which method was used to calculate (two-sided) p-values. The number of rows of the data frame corresponds to the length of the argument robust2sls_object.