infer_runs_test: Test for Random Order
In inferr: Inferential Statistics

Description Usage Arguments Value Deprecated Function References Examples

runtest tests whether the observations of x are serially independent i.e. whether they occur in a random order, by counting how many runs there are above and below a threshold. By default, the median is used as the threshold. A small number of runs indicates positive serial correlation; a large number indicates negative serial correlation.

infer_runs_test(
  data,
  x,
  drop = FALSE,
  split = FALSE,
  mean = FALSE,
  threshold = NA
)

`data`	a `data.frame` or `tibble`
`x`	numeric; column in `data`
`drop`	logical; if TRUE, values equal to the threshold will be dropped from `x`
`split`	logical; if TRUE, data will be recoded in binary format
`mean`	logical; if TRUE, mean will be used as threshold
`threshold`	threshold to be used for counting runs, specify 0 if data is coded as a binary.

infer_runs_test returns an object of class "infer_runs_test". An object of class "infer_runs_test" is a list containing the following components:

`n`	number of observations
`threshold`	within group sum of squares
`n_below`	number below the threshold
`n_above`	number above the threshold
`mean`	expected number of runs
`var`	variance of the number of runs
`n_runs`	number of runs
`z`	z statistic
`p`	p-value of `z`

runs_test() has been deprecated. Instead use infer_runs_test().

Sheskin, D. J. 2007. Handbook of Parametric and Nonparametric Statistical Procedures, 4th edition. : Chapman & Hall/CRC.

Edgington, E. S. 1961. Probability table for number of runs of signs of first differences in ordered series. Journal of the American Statistical Association 56: 156–159.

Madansky, A. 1988. Prescriptions for Working Statisticians. New York: Springer.

Swed, F. S., and C. Eisenhart. 1943. Tables for testing randomness of grouping in a sequence of alternatives. Annals of Mathematical Statistics 14: 66–87.

infer_runs_test(hsb, read)

infer_runs_test(hsb, read, drop = TRUE)

infer_runs_test(hsb, read, split = TRUE)

infer_runs_test(hsb, read, mean = TRUE)

infer_runs_test(hsb, read, threshold = 0)