dissmfac {TraMineR}R Documentation

Multi-factor ANOVA from a dissimilarity matrix

Description

Perform a multi-factor analysis of variance from a dissimilarity matrix.

Usage

dissmfac(formula, data, R = 1000, gower = FALSE, squared = TRUE,
 permutation = "dissmatrix")

Arguments

formula A regression-like formula. The left hand side should be a dissimilarity matrix or a dist object.
data data to search for variables in formula
R Number of permutations to assess significance
gower Logical: Is the dissimilarity matrix already a Gower matrix?
squared Logical: should we square the dissimilarity matrix?
permutation if equal to dissmatrix, permutations are done on the dissimilarity matrix, else if equal to "model" permutations are done on the variable matrix. Depending on the number of observation, "model" can be quicker.

Details

This method is, in some way, a generalization of dissassoc that can account for several explanatory variables. This function compute the part of variance explained by a list of covariates using a decomposition of the discrepancy (variance) explained. This function is slower than dissassoc for one factor. More on that, the latter also perform a test of discrepancy homogeneity (equality of variance) using a generalization of the T statistic.

The function is based on the program written for scipy (Python) by Ondrej Libiger and Matt Zapala. See references below for a full reference.

Value

Return a dissmultifactor object with the following components:

mfac The part of variance explained by each variable (comparing full model to model without the specified variable) and its significance using permutation test
call Function call
perms Permutation values as a boot object
perm_method Permutation method used to compute significance

References

Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2009). Analyse de dissimilarités par arbre d'induction. Revue des Nouvelles Technologies de l'Information, EGC'2009.

Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology 26, 32-46.

McArdle, B. H. et M. J. Anderson (2001). Fitting multivariate models to community data: A comment on distance-based redundancy analysis. Ecology 82(1), 290-297.

Zapala, M. A. et N. J. Schork (2006). Multivariate regression analysis of distance matrices for testing associations between gene expression patterns and related variables. Proceedings of the National Academy of Sciences of the United States of America 103(51), 19430-19435.

See Also

dissvar to compute the pseudo variance from dissimilarities and for a basic introduction to concepts of pseudo variance analysis

dissassoc to test the association of objects characterized by a dissimilarity matrix with a single explanatory variable

disstree for an induction tree analyse of objects characterized by a dissimilarity matrix.

disscenter to compute the distance of each object to its group center from dissimilarities.

dissmfac to perform multi-factor analysis of variance using dissimilarities.

Examples

## Defining a state sequence object
data(mvad)
mvad.seq <- seqdef(mvad[, 17:86])

## Building dissimilarities
mvad.lcs <- seqdist(mvad.seq, method="LCS")
print(dissmfac(mvad.lcs ~ male + Grammar + funemp +
        gcse5eq + fmpr + livboth, data=mvad, R=10))

[Package TraMineR version 1.2-1 Index]