dissmfacw | R Documentation |

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

dissmfacw(formula, data, R = 1000, gower = FALSE, squared = FALSE, weights = NULL)

`formula` |
A regression-like formula. The left hand side term should be a dissimilarity matrix or a |

`data` |
A data frame from which the variables in |

`R` |
Number of permutations used to assess significance. |

`gower` |
Logical: Is the dissimilarity matrix already a Gower matrix? |

`squared` |
Logical: Should we square the provided dissimilarities? |

`weights` |
Optional numerical vector of case weights. |

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 the list of covariates specified in the `formula`

.
It provides for each covariate the Type-II effect, i.e. the effect measured when removing the covariate from the full model with all variables included.

(The returned F values may slightly differ from those obtained with TraMineR versions older than 1.8-9. Since 1.8-9, the within sum of squares at the denominator is divided by *n-m* instead of *n-m-1*, where *n* is the sample size and *m* the total number of predictors and/or contrasts used to represent categorical factors.)

For a single factor `dissmfacw`

is slower than `dissassoc`

.
Moreover, the latter performs also tests for homogeneity in within-group discrepancies (equality of variances) with a generalization of Levene's and Bartlett's statistics.

Part of the function is based on the Multivariate Matrix Regression with qr decomposition algorithm written in SciPy-Python by Ondrej Libiger and Matt Zapala (See Zapala and Schork, 2006, for a full reference.) The algorithm has been adapted for Type-II effects and extended to account for case weights.

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 |

Matthias Studer (with Gilbert Ritschard for the help page)

Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2011). Discrepancy analysis of state sequences, *Sociological Methods and Research*, Vol. 40(3), 471-510, doi: 10.1177/0049124111415372.

Studer, M., G. Ritschard, A. Gabadinho and N. S. Müller (2010)
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. 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. 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.

`dissvar`

to compute a pseudo variance from dissimilarities and for a basic introduction to concepts of discrepancy analysis.

`dissassoc`

to test association between objects represented by their dissimilarities and a covariate.

`disstree`

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

`disscenter`

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

## Define the state sequence object data(mvad) mvad.seq <- seqdef(mvad[, 17:86]) ## Here, we use only first 100 sequences mvad.seq <- mvad.seq[1:100,] ## Compute dissimilarities (any dissimilarity measure can be used) mvad.ham <- seqdist(mvad.seq, method="HAM") ## And now the multi-factor analysis print(dissmfacw(mvad.ham ~ male + Grammar + funemp + gcse5eq + fmpr + livboth, data=mvad[1:100,], R=10))

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