Multivariate Analysis of variance based on distances and Bootstrap.
BootDisMANOVA(Distance, groups, C = NULL, Effects = NULL, nB = 1000, seed = NULL,
CoordPrinc = FALSE, dimens = 2, PCoA = "Standard", ProjectInd = TRUE, tol = 1e-04,
DatosIni = TRUE)A matrix of distances.
A factor containing the groups to compare.
A matrix of contrasts (if null the identity is used).
A vector of effects.
Number of Bootstrap replicates.
Seed for the random numbers.
Should Principal Coordinates be calculated.
Dimension of the solution.
Type of Principal Coordinates to calulate.
Should the individuals be projected onto the graph.
Tolerance for convergence of the algorithms.
Should the initial data be included in the results.
Function
Title of the study
BootMANOVA
A matrix containing the distances between individuals.
Contrasts Matrix.
Containing two matrices:
* Global -> Global contrast.
* Contrastes ->Contrasts for groups.
Sample distribution of F-exp from permutations.
Estimate p-valor for PERMANOVA.
Explained variance by Principal Coordinates selected.
Eigenvalue, Explained variance, Cumulative explained variance.
Mean Coordinates by groups for the dimensions obtained in the Principal Coordinates Analysis.
Qualities representation by groups for the dimensions of PCoA.
Cummulative qualities representation.
Cluster type selected.
Clusters created.
Names of clusters
Colors of clusters, color name and HTML code.
Multivariate Analysis of Variance based on distances and Bootstrap.
Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral ecology, 26(1):32<U+2013>46.
Anderson, M. J. (2005). Permanova: a fortran computer program for permutational multivariate analysis of variance. Department of Statistics, University of Auckland, New Zealand, 24.
# NOT RUN {
data(wine)
X = wine[,4:21]
D = DistContinuous (X)
bootwine=BootDisMANOVA(D, wine$Group)
bootwine
# }
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