gm.screening {gmvalid} | R Documentation |
Preliminary model search procedure for categorial data in three steps, using Goodman's and Kruskal's gamma and chi-squared tests.
gm.screening(data, conf.level = 0.95)
data |
Data frame or a table (array). Variables should have names, data has to be discrete. |
conf.level |
Confidence level for tests on independence. |
The initial screening divides into three parts. In the first part edges are added to the main effects model,
if the marginal gamma coefficient or the chi-squared statistic are significant. In the second part an edge is
added between two variables, if at least one of the conditional gamma
coefficient and the conditional chi-squared statistic is significant
for any possible variable in condition. In the third part all triples of variables that build
a complete subgraph are tested for removal using the conditional gamma coefficient and conditional chi-squared statistic.
An edge is removed if the corresponding p-value is larger than 1 - conf.level
.
mat |
Adjacency matrix of the screened model. |
model |
String of the screened model. |
It is advised to select a model after screening based on a backwards and forwards selection
as implemented in gm.coco
.
Ronja Foraita, Fabian Sobotka
Bremen Institute for Prevention Research and Social Medicine
(BIPS) http://www.bips.uni-bremen.de
Kreiner S and Edwards D (1983) The analysis of contingency tables by graphical models Biometrika, 70(3):553-565
Kreiner S (2008) DIGRAM http://staff.pubhealth.ku.dk/~skm/skm/ last accessed 30.06.2008
Siersma V (2007) Studies in the interactions between disease development and interventions PhD thesis, Faculty of Health Sciences, University of Copenhagen.
backward
, gm.coco
, gm.chi
, gm.gamma
data(wam) model <- gm.screening(wam) gm.coco(wam,recursive=TRUE,criterion="aic",model=model$model)