gm.screening {gmvalid}R Documentation

Model Screening

Description

Preliminary model search procedure for categorial data in three steps, using Goodman's and Kruskal's gamma and chi-squared tests.

Usage

gm.screening(data, conf.level = 0.95)

Arguments

data Data frame or a table (array). Variables should have names, data has to be discrete.
conf.level Confidence level for tests on independence.

Details

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.

Value

mat Adjacency matrix of the screened model.
model String of the screened model.

Note

It is advised to select a model after screening based on a backwards and forwards selection as implemented in gm.coco.

Author(s)

Ronja Foraita, Fabian Sobotka
Bremen Institute for Prevention Research and Social Medicine
(BIPS) http://www.bips.uni-bremen.de

References

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.

See Also

backward, gm.coco, gm.chi, gm.gamma

Examples

  data(wam)
  model <- gm.screening(wam)
  gm.coco(wam,recursive=TRUE,criterion="aic",model=model$model)

[Package gmvalid version 1.1 Index]