showTable {CoCoRaw} | R Documentation |
Print on standard output the observed probabilities, counts, means, covariance matrix, canonical parameters, fitted values, residuals, etc. of marginal tables.
showTable(type = "observed", set = "*", model = FALSE, random = FALSE, log.transformed = FALSE, complete = FALSE, discrete.ordered = TRUE, table = FALSE, matrix = TRUE, mixed = FALSE, output.form = "table", data = NULL, object = .object.of.model(model, data = data, ...), ...)
type |
A character string.
The argument type for discrete variables:
"counts" ,
"probabilities" ,
"expected" ,
"unadjusted" ,
"absolute" ,
"f-res" ,
"r-f" ,
"g-res" ,
"r-g" ,
"adjusted" ,
"c-res" ,
"m-res" ,
"standardized" ,
"standard" ,
"x-res" ,
"l-res" ,
"freeman-tukey" ,
"sqrt" ,
"power" ,
"index" ,
"zero" ,
"error" .
The argument type for both discrete and
continuous variables:
"leverage" ,
"canonical" ,
"gs" ,
"hs" ,
"ks" ,
"moment" ,
"means" ,
"covariance" ,
"raw" ,
"total" ,
"ss" ,
"ssds" ,
"determinants" ,
"mk" ,
"ms" .
|
set |
A character string with the set of variables. |
model |
See returnModel . |
random |
Logical, if random then a random table
with counts in the sufficient marginal tables
as in the observed table is generated,
and the values are are computed for this random table. |
log.transformed |
Logical, if TRUE then the values are
log.transformed before printed. |
complete |
Logical, if complete is TRUE then
no value is returned for cells to be zero
by structure. |
discrete.ordered |
Logical, if discrete.ordered then
the variables are ordered as specified
by the call. |
table |
Logical, for CoCoCg objects.
If table is FALSE then for each configuration
in the cross classification of the discrete variables
the means and covariances of the continuous variables
are printed according to the argument matrix .
If table is TRUE then for each mean
and for each covariance a table formed by the
cross classification of the discrete variables
is printed with the quantity. |
matrix |
Logical, for CoCoCg objects.
If matrix is TRUE then the means and covariances
are printed in a matrix with the continuous variables
as headings, else all the quantities for the continuous
variables are printed on a single line for each configuration
in the cross classification of the discrete variables. |
mixed |
Logical, if mixed is TRUE then mixed
quantities are printed.. |
output.form |
A character string.
The argument output.form is only
used for only discrete variables:
"table" for the table of the value selected by
the argument type ,
"sparse.table" for a list of counts in cells
with count different for zero,
"case.list" for a case list,
or, "list.all.values" for a list of all the
values for discrete variables. |
data |
See exportCoCo . |
object |
See exportCoCo . |
... |
Additional arguments to generate the CoCo object
from the data argument. See propertyModel . |
"counts"
)"absolute"
)"m-res"
,
"x-res"
, or
"standard"
) "l-res"
or
"-2log"
)"2n-m"
)"sigma"
) 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.
library(CoCoCg); library(MASS) data(crabs); crabsCoCo <- makeCoCoCg(); result <- enterDataFrame(crabs[,-3], object = crabsCoCo); enterModel("*;", object = crabsCoCo); showTable("observed", "[:sp:sex]", object = crabsCoCo); showTable("canonical", "[:sp:sex:FL:RW:CL:CW:BD]", matrix = TRUE, object = crabsCoCo); showTable("canonical", "[:sp:sex:FL:RW:CL:CW:BD]", matrix = TRUE); showTable("moment", "[:sp:sex:FL:RW:CL:CW:BD]", matrix = TRUE, object = crabsCoCo); returnTable("canonical", "[:sp:sex:FL:RW:CL:CW:BD]"); returnTable("mk", "[:sp:sex:FL:RW:CL:CW:BD]"); # backward(recursive = TRUE, headlong = TRUE, coherent = TRUE, follow = TRUE, # object = crabsCoCo); m5 <- "[[:sp:sex]] / [[:sp:sex:FL][:sp:sex:RW][:sex:CL][:sp:sex:BD][:sp:sex:CW]] / [[:sex:FL:CL][:sp:sex:FL:CW][:sp:sex:FL:BD][:sp:sex:RW:CW][:sex:CL:CW][:sex:CL:BD][:sp:sex:CW:BD]]" m4 <- "[[:sp:sex]] / [[:sp:sex:RW][:sp:sex:BD][:sp:sex:CW][:sp:sex:FL][:sex:CL]] / [[:sp:sex:CW:BD][:sex:CL:BD][:sex:CL:CW][:sp:sex:RW:BD][:sp:sex:RW:CW][:sp:sex:FL:BD][:sp:sex:FL:CW][:sex:FL:CL]]" m3 <- "[[:sp:sex]] / [[:sp:sex:FL][:sp:sex:RW][:sex:CL][:sp:sex:BD][:sp:sex:CW]] / [[:sp:sex:FL:RW][:sex:FL:CL][:sp:sex:FL:CW][:sp:sex:FL:BD][:sp:sex:RW:CW][:sp:sex:RW:BD][:sex:CL:CW][:sex:CL:BD][:sp:sex:CW:BD]]" m2 <- "[[:sp:sex]] / [[:sp:sex:BD][:sp:sex:CW][:sex:CL][:sp:sex:RW][:sp:sex:FL]] / [[:sp:sex:CW:BD][:sex:CL:BD][:sex:CL:CW][:sp:sex:RW:BD][:sp:sex:RW:CW][:sex:RW:CL][:sp:sex:FL:BD][:sp:sex:FL:CW][:sex:FL:CL][:sp:sex:FL:RW]]" m1 <- "[[:sp:sex]] / [[:sp:sex:BD][:sp:sex:CW][:sp:sex:CL][:sp:sex:RW][:sp:sex:FL]] / [[:sp:sex:CW:BD][:sp:sex:CL:BD][:sp:sex:CL:CW][:sp:sex:RW:BD][:sp:sex:RW:CW][:sp:sex:RW:CL][:sp:sex:FL:BD][:sp:sex:FL:CW][:sp:sex:FL:CL][:sp:sex:FL:RW]]" enterModel(m1, object = crabsCoCo); enterModel(m2, object = crabsCoCo); enterModel(m3, object = crabsCoCo); enterModel(m4, object = crabsCoCo); enterModel(m5, object = crabsCoCo); makeCurrent("last", object = crabsCoCo) returnTable("mk", "[:sp:sex:FL:RW:CL:CW:BD]"); Rfirst <- returnTable("mk", "[:sp:sex:FL:RW:CL:CW:BD]", model = 1); Rlast <- returnTable("mk", "[:sp:sex:FL:RW:CL:CW:BD]", model = 6); Sfirst <- returnTable("ms", "[:sp:sex:FL:RW:CL:CW:BD]", model = 1); Slast <- returnTable("ms", "[:sp:sex:FL:RW:CL:CW:BD]", model = 6); Rfirst$h-Rlast$h Rfirst$K-Rlast$K Sfirst$Mean-Slast$Mean endCoCo(object = crabsCoCo);