dissreg {TraMineR} | R Documentation |
Perform a regression analysis of a dissimilarity matrix.
dissreg(formula, data, R = 1000, gower = FALSE, squared = TRUE, permutation = "dissmatrix")
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 |
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.
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 |
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.
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
## 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))