X-Git-Url: https://git.donarmstrong.com/?a=blobdiff_plain;f=man%2FCADM.global.Rd;h=007918bd6259767d6cc734fce467280ae2dcb0d0;hb=da67dccb93d35408baa48b141fcda921772c8b9c;hp=efd57297b5c5a5983c4d3b91262497542d470f24;hpb=bea9a4387337eaa1990123745315eb04a1759fc9;p=ape.git diff --git a/man/CADM.global.Rd b/man/CADM.global.Rd index efd5729..007918b 100644 --- a/man/CADM.global.Rd +++ b/man/CADM.global.Rd @@ -1,4 +1,5 @@ \name{CADM.global} +\alias{CADM} \alias{CADM.global} \alias{CADM.post} \title{ Congruence among distance matrices } @@ -10,8 +11,10 @@ Function \code{\link{CADM.post}} carries out a posteriori permutation tests of t Use in phylogenetic analysis: to identify congruence among distance matrices (D) representing different genes or different types of data. Congruent D matrices correspond to data tables that can be used together in a combined phylogenetic or other type of multivariate analysis. } \usage{ -CADM.global(Dmat, nmat, n, nperm=99, make.sym=TRUE, weights=NULL, silent=FALSE) -CADM.post (Dmat, nmat, n, nperm=99, make.sym=TRUE, weights=NULL, mult="holm", mantel=FALSE, silent=FALSE) +CADM.global(Dmat, nmat, n, nperm=99, make.sym=TRUE, weights=NULL, + silent=FALSE) +CADM.post (Dmat, nmat, n, nperm=99, make.sym=TRUE, weights=NULL, + mult="holm", mantel=FALSE, silent=FALSE) } \arguments{ @@ -34,26 +37,26 @@ The corrections used for multiple testing are applied to the list of P-values (P The Holm correction is computed after ordering the P-values in a list with the smallest value to the left. Compute adjusted P-values as: -\eqn{P_corr = (k-i+1)*P} +\deqn{P_{corr} = (k-i+1)*P}{P_corr = (k-i+1)*P} -where i is the position in the ordered list. Final step: from left to right, if an adjusted P_corr in the ordered list is smaller than the one occurring at its left, make the smallest one equal to the largest one. +where i is the position in the ordered list. Final step: from left to right, if an adjusted \eqn{P_{corr}}{P_corr} in the ordered list is smaller than the one occurring at its left, make the smallest one equal to the largest one. The Sidak correction is: -\eqn{P_corr = 1 - (1 - P)^k} +\deqn{P_{corr} = 1 - (1 - P)^k}{P_corr = 1 - (1 - P)^k} The Bonferonni correction is: -\eqn{P_corr = k*P} +\deqn{P_{corr} = k*P}{P_corr = k*P} } \value{ -\code{CADM.global} produces a small table containing the W, Chi2, and Prob.perm statistics described in the following list. -\code{CADM.post} produces a table stored in element $A_posteriori_tests, containing Mantel.mean, Prob, and Corrected.prob statistics in rows; the columns correspond to the k distance matrices under study, labeled Dmat.1 to Dmat.k. +\code{CADM.global} produces a small table containing the W, Chi2, and Prob.perm statistics described in the following list. +\code{CADM.post} produces a table stored in element \code{A_posteriori_tests}, containing Mantel.mean, Prob, and Corrected.prob statistics in rows; the columns correspond to the k distance matrices under study, labeled Dmat.1 to Dmat.k. If parameter \code{mantel} is TRUE, tables of Mantel statistics and P-values are computed among the matrices. - \item{W }{Kendall's coefficient of concordance, W (Kendall and Babington Smith 1939). } + \item{W }{Kendall's coefficient of concordance, W (Kendall and Babington Smith 1939; see also Legendre 2010). } \item{Chi2 }{Friedman's chi-square statistic (Friedman 1937) used in the permutation test of W. } \item{Prob.perm }{Permutational probability. } @@ -65,56 +68,68 @@ If parameter \code{mantel} is TRUE, tables of Mantel statistics and P-values are \item{Mantel.prob }{One-tailed P-values associated with the Mantel correlations of the previous table. The probabilities are computed in the right-hand tail. H0 is tested against the alternative one-tailed hypothesis that the Mantel correlation under test is positive. No correction is made for multiple testing. } } -\references{ -Campbell, V., P. Legendre and F.-J. Lapointe. 2009. Assessing congruence among ultrametric distance matrices. Journal of Classification (In press). +\references{ +Campbell, V., Legendre, P. and Lapointe, F.-J. (2009) Assessing congruence among ultrametric distance matrices. \emph{Journal of Classification}, \bold{26}, 103--117. -Campbell, V., P. Legendre and F.-J. Lapointe. Performance of the congruence test among distance matrices in phylogenetic analysis. (Submitted MS). +Campbell, V., Legendre, P. and Lapointe, F.-J. (2011) The performance of the Congruence Among Distance Matrices (CADM) test in phylogenetic analysis. \emph{BMC Evolutionary Biology}, \bold{11}, 64. \url{http://www.biomedcentral.com/1471-2148/11/64}. -Friedman, M. 1937. The use of ranks to avoid the assumption of normality implicit in the analysis of variance. Journal of the American Statistical Association 32: 675-701. +Friedman, M. (1937) The use of ranks to avoid the assumption of normality implicit in the analysis of variance. \emph{Journal of the American Statistical Association}, \bold{32}, 675--701. -Kendall, M. G. and B. Babington Smith. 1939. The problem of m rankings. Annals of Mathematical Statistics 10: 275-287. +Kendall, M. G. and Babington Smith, B. (1939) The problem of m rankings. \emph{Annals of Mathematical Statistics}, \bold{10}, 275--287. -Lapointe, F.-J., J. A. W. Kirsch and J. M. Hutcheon. 1999. Total evidence, consensus, and bat phylogeny: a distance-based approach. Molecular Phylogenetics and Evolution 11: 55-66. +Lapointe, F.-J., Kirsch, J. A. W. and Hutcheon, J. M. (1999) Total evidence, consensus, and bat phylogeny: a distance-based approach. \emph{Molecular Phylogenetics and Evolution}, \bold{11}, 55--66. -Legendre, P. 2008. Coefficient of concordance. In: Encyclopedia of Research Design. SAGE Publications (in press). +Legendre, P. (2010) Coefficient of concordance. Pp. 164-169 in: Encyclopedia of Research Design, Vol. 1. N. J. Salkind, ed. SAGE Publications, Inc., Los Angeles. -Legendre, P. and F.-J. Lapointe. 2004. Assessing congruence among distance matrices: single malt Scotch whiskies revisited. Australian and New Zealand Journal of Statistics 46: 615-629. +Legendre, P. and Lapointe, F.-J. (2004) Assessing congruence among distance matrices: single malt Scotch whiskies +revisited. \emph{Australian and New Zealand Journal of Statistics}, \bold{46}, 615--629. -Legendre, P. et F.-J. Lapointe. 2005. Congruence entre matrices de distance. P. 178-181 in: Makarenkov, V., G. Cucumel et F.-J. Lapointe [eds] Comptes rendus des 12emes Rencontres de la Societe Francophone de Classification, Montreal, 30 mai - 1er juin 2005. +Legendre, P. and Lapointe, F.-J. (2005) Congruence entre matrices de distance. P. 178-181 in: Makarenkov, V., G. Cucumel et F.-J. Lapointe [eds] Comptes rendus des 12emes Rencontres de la Societe Francophone de Classification, Montreal, 30 mai - 1er juin 2005. -Siegel, S. and N. J. Castellan, Jr. 1988. Nonparametric statistics for the behavioral sciences. 2nd edition. McGraw-Hill, New York. +Siegel, S. and Castellan, N. J., Jr. (1988) \emph{Nonparametric statistics for the behavioral sciences. 2nd edition}. New York: McGraw-Hill. } -\seealso{ \code{\link{kendall.W}}, \code{\link[ape:ape-package]{ape}} } -\author{ Pierre Legendre, Universite de Montreal } -\examples{ +\author{Pierre Legendre, Universite de Montreal} -# Examples 1 and 2: 5 genetic distance matrices computed from simulated DNA -# sequences representing 50 taxa having evolved along additive trees with -# identical evolutionary parameters (GTR+ Gamma + I). Distance matrices were -# computed from the DNA sequence matrices using a p distance corrected with the -# same parameters as those used to simulate the DNA sequences. See Campbell et -# al. (submitted) for details. +\examples{ +# Examples 1 and 2: 5 genetic distance matrices computed from simulated DNA +# sequences representing 50 taxa having evolved along additive trees with +# identical evolutionary parameters (GTR+ Gamma + I). Distance matrices were +# computed from the DNA sequence matrices using a p distance corrected with the +# same parameters as those used to simulate the DNA sequences. See Campbell et +# al. (2009) for details. -# First example: five independent additive trees. Data provided by V. Campbell. +# Example 1: five independent additive trees. Data provided by V. Campbell. data(mat5Mrand) res.global <- CADM.global(mat5Mrand, 5, 50) -# Second example: three partly similar trees, two independent trees. +# Example 2: three partly similar trees, two independent trees. # Data provided by V. Campbell. data(mat5M3ID) res.global <- CADM.global(mat5M3ID, 5, 50) res.post <- CADM.post(mat5M3ID, 5, 50, mantel=TRUE) -# Third example: three matrices respectively representing Serological -# (asymmetric), DNA hybridization (asymmetric) and Anatomical (symmetric) +# Example 3: three matrices respectively representing Serological +# (asymmetric), DNA hybridization (asymmetric) and Anatomical (symmetric) # distances among 9 families. Data from Lapointe et al. (1999). data(mat3) res.global <- CADM.global(mat3, 3, 9, nperm=999) res.post <- CADM.post(mat3, 3, 9, nperm=999, mantel=TRUE) + +# Example 4, showing how to bind two D matrices (cophenetic matrices +# in this example) into a file using rbind(), then run the global test. + +a <- rtree(5) +b <- rtree(5) +A <- cophenetic(a) +B <- cophenetic(b) +x <- rownames(A) +B <- B[x, x] +M <- rbind(A, B) +CADM.global(M, 2, 5) } \keyword{ multivariate }