# critical: Compute the critical value of generalized Kolmogorov-Smirnov... In Jiefei-Wang/exceedance: Multiple Hypothesis Testing While Controlling the Exceedance Probability of the False Discovery Proportion

 GKSCritical R Documentation

## Compute the critical value of generalized Kolmogorov-Smirnov test statistics

### Description

Compute the critical value of generalized Kolmogorov-Smirnov test statistics, see details on how to use the function.

### Usage

```GKSCritical(
n,
alpha,
index = NULL,
indexL = NULL,
indexU = NULL,
statName = c("KS", "KS+", "KS-", "BJ", "BJ+", "BJ-", "HC", "HC+", "HC-", "Simes")
)
```

### Arguments

 `n` Integer, the sample size of the data. `alpha` numeric, the type I error rate for the critical value. Please do not be confused with `alpha0`. `index` Integer, controlling which ordered samples will be used in the statistics, see details. `indexL` Integer, controlling which ordered samples will be used in the statistics, see details. `indexU` Integer, controlling which ordered samples will be used in the statistics, see details. `statName` Character, the name of the statistic that will be computed. The default is "KS".

### Details

statistics definitions

The function compute the test statistics which aggregate the significant signal from the order statistics of the samples, that is, if `T` is a statistic and `X_1`,`X_2`,...,`X_n` are the samples, the value of `T` is purely based on the value of `X_(1)`,`X_(2)`,...,`X_(n)`, where `X_(i)` is the ith ascending sorted samples of `X1`,`X2`,...,`Xn`. Moreover, the rejection region of the statistic `T` can be written as a set of rejection regions of the ordered samples `X_(1)`,`X_(2)`,...,`X_(n)`. In other words, there exist two sequences `{l_i}` and `{u_i}` for `i=1,...,n` and the statistic `T` is rejected if and only if there exist one `i` such that `X_(i) < l_i` or `X_(i) > u_i`.

The most well-known statistic which takes this form is the Kolmogorov-Smirnov statistic. Other statistics like Berk-Jones or the higher criticism also have similar formulas but define different sets of `{l_i}` and `{u_i}`.

alpha0, index, indexL and indexU

As mentioned previouly, the rejection of a test can be determined by the sequences of `{l_i}` and `{u_i}`. Therefore, the parameter `alpha0`, `index` `indexL` and `indexU`. provide a way to control which `l_i` and `u_i` will be considered in the test procedure. If no argument is provided, all `l_i`s and `u_i`s will be compared with their corresponding sorted sample `X_(i)`. This yields the traditional test statistics. If `alpha0` is used, only the data `X_(1),...X_(k)` will be used in the test where `k` is the nearest integer of `alpha0*n`. If `index` is provided, only `X_(i)` for `i` in `index` will be considered in the test. If `indexL` and/or `indexU` is not `NULL`, only `l_i` for `i` in `indexL` and `u_i` for `i` in `indexU` will be used as the rejection boundary for the test. These can be used to generate an one-sided version of the test statistic. For example, if `indexL` is from `1` to the length of `x` and `indexU` is `NULL`, this will yield a test specifically sensitive to smaller samples. The test statistics like `KS+`, `HC+` and `BJ+` are implemented by calling `GKSStat(..., indexU = NULL)`, where `indexU` is always `NULL`.

A critical value

### Examples

```## Compute the critical value of the KS test
## of sample size 10
GKSCritical(alpha = 0.05, n = 10, statName = "KS")

## The critical value for the test that
## only considers the first 3 ordered samples
## All gives the same result.
GKSCritical(alpha = 0.05, n = 10, alpha0 = 0.3, statName = "KS")
GKSCritical(alpha = 0.05, n = 10, index = 1:3, statName = "KS")
GKSCritical(alpha = 0.05, n = 10, indexL = 1:3, indexU = 1:3, statName = "KS")

```

Jiefei-Wang/exceedance documentation built on May 11, 2022, 1:43 a.m.