Multivariate Analysis of Variance based on Distances and Bootstrap
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)
Distance |
A matrix of distances. |
groups |
A factor containing the groups to compare. |
C |
A matrix of contrasts (if null the identity is used). |
Effects |
A vector of effects. |
nB |
Number of Bootstrap replicates. |
seed |
Seed for the random numbers. |
CoordPrinc |
Should Principal Coordinates be calculated. |
dimens |
Dimension of the solution. |
PCoA |
Type of Principal Coordinates to calulate. |
ProjectInd |
Should the individuals be projected onto the graph. |
tol |
Tolerance for convergence of the algorithms. |
DatosIni |
Should the initial data be included in the results. |
Multivariate Analysis of Variance based on distances and Bootstrap.
call |
Function |
Title |
Title of the study |
Type |
BootMANOVA |
Distances |
A matrix containing the distances between individuals. |
C |
Contrasts Matrix. |
Initial |
Containing two matrices: * Global -> Global contrast. * Contrastes ->Contrasts for groups. |
DistMuestral |
Sample distribution of F-exp from permutations. |
pvalue |
Estimate p-valor for PERMANOVA. |
ExplainedVariance |
Explained variance by Principal Coordinates selected. |
Inertias |
Own value, Explained variance, Cumulative explained variance. |
MeanCoordinates |
Mean Coordinates by groups for the dimensions obtained in the Principal Coordinates Analysis. |
Qualities |
Qualities representation by groups for the dimensions of PCoA. |
CumulativeQualities |
Cumulative qualities representation. |
ClusterType |
Cluster type selected. |
Clusters |
Clusters created. |
ClusterNames |
Names of clusters |
ClusterColors |
Colors of clusters, color name and HTML code. |
Laura Vicente-Gonzalez, Jose Luis Vicente-Villardon
Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral ecology, 26(1):32–46.
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 ## End(Not run)
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