RSiena (version 1.1-232)

print.sienaMeta: Methods for processing sienaMeta objects

Description

print, summary, and plot methods for sienaMeta objects.

Usage

# S3 method for sienaMeta
print(x, file=FALSE, ...)
# S3 method for sienaMeta
summary(object, file=FALSE, extra=TRUE, ...)
# S3 method for summary.sienaMeta
print(x, file=FALSE, extra=TRUE, ...)
# S3 method for sienaMeta
plot(x,  ..., layout=c(2,2))

Arguments

object

An object of class sienaMeta

x

An object of class sienaMeta, or summary.sienaMeta as appropriate

file

Boolean: if TRUE, sends output to file named x$projname.out. If FALSE, output is to the terminal.

extra

Boolean: if TRUE, prints more information

layout

the vector giving number of rows and columns in the arrangement of the several panels in a rectangular array, possibly spanning multiple pages

…

For extra arguments (none used at present)

Value

The function print.sienaMeta prints details of the merged estimates of the meta-analysis, with test statistics.

The function summary.sienaMeta prints details as for the print method, but also details of the sienaFit objects included.

Output from either can be directed to a file by using the argument file. It will be appended to any existing file of the same name: projname.out where projname is the value of the argument to siena08.

The function plot.sienaMeta plots estimates against standard errors for each effect, with reference lines added at the two-sided significance threshold 0.05. It returns an object of class trellis, of the lattice.package. Effects for which a score test was requested are not plotted.

References

T. A. B. Snijders and Chris Baerveldt. "Multilevel network study of the effects of delinquent behavior on friendship evolution". Journal of Mathematical Sociology, 27: 123--151, 2003.

See also the Siena manual and http://www.stats.ox.ac.uk/~snijders/siena/

Examples

# NOT RUN {
# A meta-analysis for three groups does not make much sense
# for generalizing to a population of networks,
# but it the Fisher combinations of p-values are meaningful.
# But using three groups shows the idea.

Group1 <- sienaDependent(array(c(N3401, HN3401), dim=c(45, 45, 2)))
Group3 <- sienaDependent(array(c(N3403, HN3403), dim=c(37, 37, 2)))
Group4 <- sienaDependent(array(c(N3404, HN3404), dim=c(33, 33, 2)))
dataset.1 <- sienaDataCreate(Friends = Group1)
dataset.3 <- sienaDataCreate(Friends = Group3)
dataset.4 <- sienaDataCreate(Friends = Group4)
OneAlgorithm <- sienaAlgorithmCreate(projname = 'SingleGroups')
effects.1 <- getEffects(dataset.1)
effects.3 <- getEffects(dataset.3)
effects.4 <- getEffects(dataset.4)
effects.1 <- includeEffects(effects.1, transTrip)
effects.1 <- setEffect(effects.1, cycle3, fix=TRUE, test=TRUE)
effects.3 <- includeEffects(effects.3, transTrip)
effects.3 <- setEffect(effects.3, cycle3, fix=TRUE, test=TRUE)
effects.4 <- includeEffects(effects.4, transTrip)
effects.4 <- setEffect(effects.4, cycle3, fix=TRUE, test=TRUE)
ans.1 <- siena07(OneAlgorithm, data=dataset.1, effects=effects.1, batch=TRUE)
ans.3 <- siena07(OneAlgorithm, data=dataset.3, effects=effects.3, batch=TRUE)
ans.4 <- siena07(OneAlgorithm, data=dataset.4, effects=effects.4, batch=TRUE)
ans.1
ans.3
ans.4
(meta <- siena08(ans.1, ans.3, ans.4))
summary(meta)
plo <- plot(meta, layout = c(3,1))
plo
plo[3]
# }