gmvalid-package {gmvalid} | R Documentation |
This package provides functions among others that can be used to analyse graphical models. This includes e.g. the possibility to simulate data sets given a dependence model, to analyze discrete graphical models utilizing the MIM program or the CoCo package and to quantify associations or interactions.
Furthermore, a selected graphical model can be validated using the bootstrap and the best prediction model can be evaluated for a dichotomous outcome variable and several discrete influences using cross validation.
Package: | gmvalid |
Type: | Package |
Version: | 1.0 |
Date: | 2007-11-07 |
License: | GPL (version 2 or later) |
mimR
.
This work has been supported by the German Research Foundation
(DFG: http://www.dfg.de)
under grant scheme PI 345/2-1.
Ronja Foraita, Fabian Sobotka
Bremen Institute for Prevention Research and Social Medicine
(BIPS) http://www.bips.uni-bremen.de
> MIM (http://www.hypergraph.dk/)
Edwards D (2002)
An Introduction to Graphical Modelling.
Springer
> mimR (http://genetics.agrsci.dk/~sorenh/mimR/index.html)
Højsgaard S (2004)
The mimR package for graphical modelling in R.
Journal of Statistical Software, 11(6).
> CoCo (http://www.badsberg.eu)
Badsberg JH (2001)
A guide to CoCo.
Journal of Statistical Software, 6(4).
> CSI
Foraita R (2007)
A conditional synergy index to assess biological interaction.
Working Paper. Please send an e-mail to foraita@bips.uni-bremen.de.
### Generates a data frame given a dependence model gm.a <- gm.modelsim(1000,"ABC,CDE") ### Modelselection with graphical output gm.analysis(gm.a) ### Model validation using the bootstrap gm.boot.coco(100,gm.a,recursive=TRUE,follow=TRUE) ### Model prediction using cross validation gm.cv(3,data=gm.a,strategy="f",options="b") ### Testing interaction on the penetrance scale ### using the conditional synergy index (CSI) gm.csi(1,2,3,data=gm.a) ### Testing interaction on a additivity scale ### using the synergy index (S) gm.si(1,2,3,data=gm.a) ### Gamma Coefficient B indpendent D given C gm.gamma(2,4,data=gm.a,conditions=3)