# iid.test {iid.test}

### Description

Test for whether a variable is independent and identically distributed (iid). Used in `daily.station.records`

.

Reference:

Benestad, R.E., 2003: How often can we expect a record-event? Climate Research. 23, 3-13 (pdf)

Benestad, R.E., 2004: Record values, nonstationarity tests and extreme value distributions, Global and Planetary Change, vol 44, p. 11-26

The papers are available in the pdf format from http://regclim.met.no/results_iii_artref.html.

Note, gaps of missing data (NA) can bias the results and produce an under-count. The sign of non-iid behaviour is when the 'forward' analysis indicated higher number of record-events than the confidence region and the backward analysis gives lower than the confidence region.

Version 0.7: Added a test checking for dependencies based on an expected number from a binomial distribution and given the probability p1(n) = 1/n. This test is applied to the parallel series for one respective time (realisation), and is then repeated for all observation times. The check uses `qbinom`

to compute a theoretical 95% confidence interval, and a number outside this range is marked with red in the 'ball diagram' (first plot). `pbinom`

is used to estimate the p-value for the

### Usage

iid.test(Y,plot=TRUE,Monte.Carlo=TRUE,N.test=200,reverse.plot.reverse=TRUE)

### Arguments

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- Y
- A data matrix or a vector.
- plot
- Flag: plot the diagnostics.
- Monte.Carlo
- Flag: for estimating confidence limits.
- N.test
- Number of Monre-Carlo runs.
- reverse.plot.reverse
- TRUE: plots reverse from right to left, else left to right.

### Values

list: 'record.density' and 'record.density.rev' for the reverse analysis. The variables CI.95, p.val, and i.cluster (and their reverse equivalents '.rev') return the estimated 95% conf. int, p-value, and the location of the clusters (binomial).

Documentation reproduced from package iid.test, version 1.13. License: GPL (>= 2)