\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. }
\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.
}
-\author{ Pierre Legendre, Universite de Montreal }
+\author{Pierre Legendre, Universite de Montreal}
\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.
-# 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.
-
-# 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 }
projects / ape.git / blobdiff