perm.test: Permutation Test In jmuOutlier: Permutation Tests for Nonparametric Statistics

Description

Performs one-sample and two-sample permutation tests on vectors of data.

Usage

 ```1 2``` ```perm.test(x, y = NULL, alternative = c("two.sided", "less", "greater"), mu = 0, paired = FALSE, all.perms = TRUE, num.sim = 20000, plot = FALSE, stat = mean, ...) ```

Arguments

 `x` A (non-empty) numeric vector of data values. `y` An optional numeric vector data values. `alternative` A character string specifying the alternative hypothesis, and must be one of `"two.sided"` (default), `"greater"` or `"less"`. Only the initial letter needs to be specified. `mu` A number indicating the null value of the location parameter (or the difference in location parameters if performing a two-sample test). `paired` Logical, indicating whether or not a two-sample test should be paired, and is ignored for a one-sample test. `all.perms` Logical. The exact p-value is attempted when `all.perms` (i.e., all permutations) is `TRUE` (default), and is simulated when `all.perms` is `FALSE` or when computing an exact p-value requires more than `num.sim` calculations. `num.sim` The upper limit on the number of permutations generated. `plot` Logical. If `TRUE`, then plot the histogram of the permutation distribution; otherwise, list the p-value. `stat` Function, naming the test statistic, such as `mean` and `median`. `...` Optional arguments to `stat`; and is the second argument to `stat` when unspecified. For example, if `stat` equals `mean`, then the second argument `trim` denotes the fraction (0 to 0.5) of observations to be trimmed from each end of `x` and `y` before the mean is computed.

Details

A paired test using data `x` and nonNULL `y` is equivalent to a one-sample test using data `x-y`. The output states more details about the permutation test, such as one-sample or two-sample, and whether or not the `p.value` calculated was based on all permutations.

Value

 `alternative` Same as the input. `mu` Same as the input. `p.value` The p-value of the permutation test.

Note

The formulas computed within `perm.test` are based on the textbook by Higgins (2004).

Author(s)

Steven T. Garren, James Madison University, Harrisonburg, Virginia, USA

References

Higgins, J. J. (2004) Introduction to Modern Nonparametric Statistics.

Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11``` ```# One-sample test print( x <- rnorm(10,0.5) ) perm.test( x, stat=median ) # Two-sample unpaired test print( y <- rnorm(13,1) ) perm.test( x, y ) ```

Example output

``` [1]  1.6013242  1.5846410 -0.5714917  0.3988142  0.5416978  1.3851507
[7] -0.2418826  1.3553764  0.5802484 -0.4113348
[[1]]
[1] "One-sample permutation test was performed."

[[2]]
[1] "p-value was calculated based on all permutations."

\$alternative
[1] "two.sided"

\$mu
[1] 0

\$p.value
[1] 0.078125

[1]  1.5735208  1.5312516  0.4463142  1.2528721 -0.5620620  2.3429980
[7]  0.7756172  1.6497653  0.7889176  0.5107436  0.7542697  1.4606170
[13]  0.4759810
[[1]]
[1] "Unpaired two-sample permutation test was performed."

[[2]]
[1] "p-value was estimated based on 20000 simulations."

\$alternative
[1] "two.sided"

\$mu
[1] 0

\$p.value
[1] 0.2584
```

jmuOutlier documentation built on Aug. 6, 2019, 1:03 a.m.