TraMineR (version 1.1)

dissreg: Regression analysis of dissimilarity matrix

Description

Perform a regression analysis of a dissimilarity matrix.

Usage

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

Arguments

formula

A formula for the regression. The left hand side should be a dissimilarity matrix or a dist object.

data

data to search for variable in formula

R

Number of permutation to assess significance

gower

Is the dissimilarity matrix already a gower matrix ?

squared

should we square the dissimilarity matrix ?

permutation

if equal to dissmatrix, permutations are done on the dissimilarity matrix, else if equal to "model" permutation are done on variable matrix

Value

• Return a dissregression object with the following componant:
• mreg
  The part of variance explained by each coefficient (comparing full model to model without the specified variable) and its significativity using permutation test
• call
  Function call
• perms
  Permutations 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 in order to several explicative variables. The function is based on the program written for scipy (Python) by Ondrej Libiger and Matt Zapala. See references below for a full reference. This function compute the part of variance explained by a list of covariates using a decomposition of the variance explained.

References

Studer, M., G. Ritschard, A. Gabadinho and N. S. M�ller (2009). Analyse de dissimilarit�s 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 pseudo variance using dissimilarities and for a basic introduction to concepts of pseudo variance analysis dissassoc to test association between dissimilarity and another variable disstree to analyse dissimilarities using induction trees disscenter to compute the distance of each object to its center of group 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(dissreg(mvad.lcs ~ male + Grammar + funemp + 
	gcse5eq + fmpr + livboth,  data=mvad, R=10))