gmcpic.test: Function implementing the Generalized Monte Carlo plug-in...
In StrainRanking: Ranking of Pathogen Strains

Description Usage Arguments Details Value Author(s) References Examples

The GMCPIC test is a procedure to test the equality of the vectors of probabilities of two multinomial draws. The test statistics that is used is the multinomial-density statistic.

1	gmcpic.test(x, B, M, weights, threshold)

`x`	[2-column matrix] Column 1 (resp. 2) contains the vector of observed frequencies in population 1 (resp. 2).
`B`	[Integer] Number of Monte Carlo simulations.
`M`	[Integer] Number of repetitions for the calibration.
`weights`	[Numeric] Vector of weights in [0,1] that are tried for the calibration.
`threshold`	[Numeric] Targeted risk level of the test; value in [0,1].

The GMCPIC test was developed to test the similarity of two pathogen compositions based on small samples and sparse data.

list with INPUT arguments (x, B, M, weights and threshold) and the following items:

`calibrated.weight`	Weight selected by the calibration procedure.
`p.value`	Test p-value.
`reject.null.hypothesis`	Logical indicating whether the null hypothesis is rejected or not at the risk level specified by `threshold`.
`Message`	Details about the p-value interpretation.

Samuel Soubeyrand <samuel.soubeyrand@inra.fr>

Vincent Garreta

Maintainer: Jean-Francois Rey

Soubeyrand S, Garreta V, Monteil C, Suffert F, Goyeau H, Berder J, Moinard J, Fournier E, Tharreau D, Morris C, Sache I (2017). Testing differences between pathogen compositions with small samples and sparse data. Phytopathology 107: 1199-1208. http://doi.org/10.1094/PHYTO-02-17-0070-FI

## Load Pathogen Compositions of M. oryzae collected in Madagascar
data(PathogenCompositionMoryzaeMadagascar)
x=t(PathogenCompositionMoryzaeMadagascar)

## Apply the GMCPIC test (use B=10^3, M=10^4 to get a robust result)
testMada=gmcpic.test(x, B=10^2, M=10^3, weights=seq(0.5,0.99,by=0.01),threshold=0.05)
testMada

## Apply the Chi-squared test
chisq.test(x, simulate.p.value = TRUE, B = 10000)