TraMineR (version 1.6-2)

dissmfac: 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.

Value

• 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

encoding

latin1

Details

This method is, in some way, a generalization of dissassoc to account for several explanatory variables. The function computes the part of discrepancy explained by a list of covariates. It provides for each covariate Type-II effect, i.e. the effect of removing the variable from the full model with all variables included. For a single factor dissmfac is slower than dissassoc. Moreover, the latter performs also tests of homogeneity in within group discrepancy (equality of variances) using generalization of Bratlett statistics. The function is based on the program written for scipy (Python) by Ondrej Libiger and Matt Zapala. See Zapala and Schork (2006) for a full reference.

References

Studer, M., G. Ritschard, A. Gabadinho and N. S. M�ller (2009) Discrepancy analysis of complex objects using dissimilarities. In F. Guillet, G. Ritschard, D. A. Zighed and H. Briand (Eds.), Advances in Knowledge Discovery and Management, Studies in Computational Intelligence, Volume 292, pp. 3-19. Berlin: Springer. Studer, M., G. Ritschard, A. Gabadinho and N. S. M�ller (2009). Analyse de dissimilarit�s par arbre d'induction. In EGC 2009, Revue des Nouvelles Technologies de l'Information, Vol. E-15, pp. 7-18. Anderson, M. J. (2001). A new method for non-parametric multivariate analysis of variance. Austral Ecology 26, 32-46. McArdle, B. H. and 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. and 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 association between objects represented by their dissimilarities and a covariate. 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 pairwise 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))