dissmfac {TraMineR} | R Documentation |
Perform a multi-factor analysis of variance from a dissimilarity matrix.
dissmfac(formula, data, R = 1000, gower = FALSE, squared = TRUE, permutation = "dissmatrix")
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. |
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 Zapala and Schork (2006) for a full reference.
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 |
Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2009) Discrepancy analysis of complex objects using dissimilarities. In H. Briand, F. Guillet, G. Ritschard, and D. A. Zighed (Eds.), Advances in Knowledge Discovery and Management, Studies in Computational Intelligence. Berlin: Springer.
Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2009). Analyse de dissimilarités 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. 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.
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.
## 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))