showTest {CoCoRaw} | R Documentation |
Print on standard output for discrete data the Pearson $chi^2$, the power divergence, and the deviance, and for tests of two ordinal variable conditional independent given discrete also the Goodman and Kruskal's Gamma coefficient, and for mixed data the deviance and the F-test statistic, all with p-values. For discrete data also exact p-values can be printed.
showTest(model.1 = "current", model.2 = "base", exact.test = NULL, break.down = "", set = ";", only.if.one.edge = FALSE, data = NULL, object = .object.of.models(model.1, model.2, data = data, ...), ...)
model.1 |
See isSubmodel . |
model.2 |
See isSubmodel . |
exact.test |
Logical, if exact.test is TRUE then exact p-values
are computed by Monte Carlo simulation. |
break.down |
Text-string:
"edges" : Break down the test in a sequence of tests,
each test a test between two models differing by one edge.
The order of the edges are controlled by the argument set .
"interactions" : Break down the test in a sequence of tests, each test
a test between two models differing by one interaction term.
The order of the terms are controlled by
the argument set .
"components" : Find sets that are complete separators for both models,
and show tests collapsed to each component.
"show.common.decompositions" : Do not perform a test, show
separators which are complete in both models.
"decompose.models" : Do not perform a test, but decompose both
models with respect to the argument set .
|
set |
Text string with set of variables,
see the argument break.down . |
only.if.one.edge |
Logical, if only.if.one.edge is set to TRUE
then the test is only performed
if the models differs with one edge and only
one edge. |
data |
See exportCoCo . |
object |
See exportCoCo . |
... |
Additional arguments to generate the CoCo object
from the data argument. See propertyModel . |
TRUE
Jens Henrik Badsberg
Badsberg, J.H.: A guide to CoCo, JSS, 2001 ( http://www.jstatsoft.org/v06/i04/ ) and Badsberg, J.H.: Xlisp+CoCo, Aalborg University, 1996.
enterModel
, makeModel
,
showModel
, makeCurrent
,
returnModel
, returnModelNumber
,
partialAssociations
, optionsCoCo
,
showDeviance
,
returnTest
, returnDeviance
,
backward
and forward
.
# data(HairEyeColor); # library(CoCo); # showTest(":H:S,:E:S", "*", data = HairEyeColor, exact.test = "on"); # endCoCo(); library(MASS); data(cabbages); library(CoCoCg); data(Rats); CoCoObject <- makeCoCoCg(); enterDataFrame(cabbages, object = CoCoObject); fullModel <- makeModel(enterModel("*", object = CoCoObject)); # Generate some models, here by "backward": backward(recursive = TRUE, headlong = TRUE, coherent = TRUE, follow = TRUE, object = CoCoObject); makeCurrent("last", object = CoCoObject); showTest("last", object = CoCoObject); returnTable("moment", ":Cult:Date:HeadWt:VitC", object = CoCoObject); returnTable("moment", ":Cult:Date:HeadWt:VitC", matrix = TRUE, object = CoCoObject); returnTable("mk", ":Cult:Date:HeadWt:VitC", matrix = TRUE, object = CoCoObject); showTable("mk", ":Cult:Date:HeadWt:VitC", matrix = TRUE, object = CoCoObject); showTable("mk", ":Cult:Date:HeadWt:VitC", object = CoCoObject); showTable("ms", ":Cult:Date:HeadWt:VitC", object = CoCoObject); endCoCo(CoCoObject);