jmuOutlier-package: Permutation Tests for Nonparametric Statistics

In jmuOutlier: Permutation Tests for Nonparametric Statistics

Description

Performs a permutation test on the difference between two location parameters, a permutation correlation test, a permutation F-test, the Siegel-Tukey test, a ratio mean deviance test. Also performs some graphing techniques, such as for confidence intervals, vector addition, and Fourier analysis; and includes functions related to the Laplace (double exponential) and triangular distributions. Performs power calculations for the binomial test.

Details

(I) Permutation tests

• perm.cor.test performs a permutation test based on Pearson and Spearman correlations.

• perm.f.test performs a permutation F-test and a one-way analysis of variance F-test.

• perm.test performs one-sample and two-sample permutation tests on vectors of data.

• rmd.test performs a permutation test based on the estimated RMD, the ratio of the mean of the absolute value of the deviances, using two datasets.

• siegel.test performs the Siegel-Tukey test using two datasets.

(II) Confidence intervals

• CI.t.test produces two-sided confidence intervals on population mean, allowing for a finite population correction.

• quantileCI produces exact confidence intervals on quantiles corresponding to the stated probabilities, based on the binomial test.

(III) Graphs

• coin.toss illustrates the Law of Large Numbers for proportions.

• fourier determines the Fourier approximation for any function on domain (0, 2π) and then graphs both the function and the approximation.

• lineGraph constructs a line graph on a vector of numerical observations.

• plotCI plots multiple confidence intervals on the same graph, and determines the proportion of confidence intervals containing the true population mean.

• plotEcdf graphs one or two empirical cumulative distribution functions on the same plot.

• plotVector plots one or two 2-dimensional vectors along with their vector sum.

• truncHist produces a truncated histogram, which may be useful if data contain some extreme outliers.

(IV) Laplace (double exponential) and symmetric triangular distributions

• dlaplace, plaplace, qlaplace, and rlaplace give the density, the distribution function, the quantile function, and random deviates, respectively, of the Laplace distribution.

• dtriang, ptriang, qtriang, and rtriang give the density, the distribution function, the quantile function, and random deviates, respectively, of the triangular distribution.

(V) Reading datasets

• read.table2 reads table of data from author's website.

• scan2 scans data from author's website.

(VI) Additional functions

• abbreviation determines if one character variable is an abbreviation among a selection of other character variables.

• latin generates a Latin square.

• power.binom.test computes the power of the binomial test of a simple null hypothesis about a population median.

• score generates van der Waerden scores (i.e., normal quantiles) and exponential (similar to Savage) scores.

Author(s)

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

References

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

See Also

R-package coin for additional permutation tests, and R-package fastGraph.

Examples

print( x <- rtriang(20,50) ) 

perm.test( x, mu=25, stat=median )

quantileCI( x, c(0.25, 0.5, 0.75)  )

power.binom.test( 20, 0.05, "less", 47, plaplace, 45.2, 3.7 )

fourier (function(x){ (x-pi)^3 }, 4 )

Example output

 [1] 16.331527 11.073387 33.669940 43.412116 30.667683 44.094618 26.317435
 [8] 26.208683 27.969395 13.543866 39.246650  4.471731 25.494942 24.359593
[15] 33.829072 11.887257 39.855904 27.766518 21.204405 32.210546
[[1]]
[1] "One-sample permutation test was performed."

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

$alternative
[1] "two.sided"

$mu
[1] 25

$p.value
[1] 0.36035

        lower    upper
0.25 11.07339 26.31744
0.5  21.20441 33.66994
0.75 27.76652 43.41212
[1] 0.6119434
$constant
[1] 0

$cosine.coefficients
[1] 0 0 0 0

$sine.coefficients
[1] -7.739209 -8.369604 -6.135292 -4.747302

$approximation
[1] "g(x) = - 7.74 sin x - 8.37 sin 2x - 6.14 sin 3x - 4.75 sin 4x"

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