gm.gms {gmvalid} | R Documentation |
Selection strategy for a graphical model. Searches forwards or backwards in one or more steps using the
conditional gamma coefficient impelemented in gm.gamma
.
gm.gms(data, strategy = c("backwards", "forwards", "combined"), model = FALSE, onestep = FALSE, headlong = FALSE, conf.level = 0.95)
data |
Data frame or array. Variables need to be discrete and should have names. |
strategy |
Type of model selection. "backwards" eliminates not significant edges, starting from the saturated model as default.
"forwards" adds significant edges, starting from the main effects model.
The "combined" strategy is a 3 step procedure: gm.screening , "backwards" and then "forwards".
The default strategy is "backwards". Selections may be abbreviated.
|
model |
Character string to specify a start model for "backwards" or "forwards" selection procedures.
For "combined" the model cannot be given, a start model
will be specified by gm.screening .
The model formula has to start with the first lowercase letters of the alphabet, e.g. "abc,cde".
Variable names cannot be given.
|
onestep |
If TRUE all edges associated with a p-value < 1 - conf.level / p-value > 1 - conf.level will be added / removed in one step. If FALSE only one edge will be added / removed in each step. |
headlong |
If TRUE edges are visited in random order and the first (in)significant is added / eliminated.
If FALSE in every step the edge with the highest / lowest p-value is eliminated / added.
Only working for onestep FALSE.
|
conf.level |
See gm.gamma . |
For every two-variable association the conditional gamma coefficient, the standard error and the p-value is calculated from the data. In the one-step procedure all (in-) significant edges are added / deleted at once, where the basis is the main effects / saturated model while when doing more steps the base model is always the selected model from the previous step.
measure |
A list of matrices with the output of gm.gamma for all cliques in the selected model. |
model |
Srting of selected model. |
The function is more time consuming than comparable functions.
Ronja Foraita, Fabian Sobotka
Bremen Institute for Prevention Research and Social Medicine
(BIPS) http://www.bips.uni-bremen.de
Davis JA (1967) A partial coefficient for Goodman and Kruskal's gamma. Journal of the American Statistical Association, 62:189-193.
Olszak M, Ritschard G (1995) The behaviour of nominal and ordinal partial association measures. The Statistician, 44(2):195-212.
data(wam) gm.gms(wam) gm.gms(wam,onestep=TRUE)