flip (version 2.5.0)

flip-package: flip-package

Description

The library is devoted to permutation-based inferential methods.

It implements many univariate and multivariate permutation (and rotation) tests.

The tests comprised are: the one and two samples, ANOVA, linear models, Chi Squared test, rank tests (i.e. Wilcoxon, Mann-Whitney, Kruskal-Wallis), Kolmogorov-Smirnov and Anderson-Darling.

Test on Linear Models are performed also in presence of covariates (i.e. nuisance parameters).

The permutation and the rotation method to get the null distribution of the test statistic(s) are available.

It also implements methods for multiplicity control such as Westfall-Young min-p procedure and Closed Testing (Marcus, 1976).

Package:	flip
Type:	Package
Version:	1.1
Date:	2012-02-05
License:	GPL <=2
LazyLoad:	yes
Depends:	methods, e1071, someMTP, cherry

References

For the general framework of univariate and multivariate permutation tests see: Pesarin, F. (2001) Multivariate Permutation Tests with Applications in Biostatistics. Wiley, New York.

#' @references For the general framework of univariate and multivariate permutation tests see: Pesarin, F. (2001) Multivariate Permutation Tests with Applications in Biostatistics. Wiley, New York.

Livio Finos and Fortunato Pesarin (2018) On zero-inflated permutation testing and some related problems. Statistical Papers.

For analysis of mixed-models see: L. Finos and D. Basso (2014) Permutation Tests for Between-Unit Fixed Effects in Multivariate Generalized Linear Mixed Models. Statistics and Computing. Vo- lume 24, Issue 6, pp 941-952. DOI: 10.1007/s11222-013-9412-6 J. J. Goeman and

D. Basso, L. Finos (2011) Exact Multivariate Permutation Tests for Fixed Effec- ts in Mixed-Models. Communications in Statistics - Theory and Methods. DOI 10.1080/03610926.2011.627103

For Rotation tests see: Langsrud, O. (2005) Rotation tests, Statistics and Computing, 15, 1, 53-60

A. Solari, L. Finos, J.J. Goeman (2014) Rotation-based multiple testing in the multivariate linear model. Biometrics, 70 (4), 954-961.

Examples

# NOT RUN {
Y=data.frame(matrix(rnorm(50),10,5))
names(Y)=LETTERS[1:5]
Y[,1:2]=Y[,1:2]
x=rep(0:1,5)
data=data.frame(x=x, Z=rnorm(10))
res = flip(Y+matrix(x*2,10,5),~x,~Z,data=data)
res

plot(res)

p2=npc(res,"fisher",subsets=list(c1=c("A","B"),c2=names(Y)))
p2

# }