RSiena (version 1.4.7)

print.sienaMeta: Methods for processing sienaMeta objects

Description

print, summary, and plot methods for sienaMeta objects; and a function to write a LaTeX table.

Usage

# S3 method for sienaMeta
print(x, file=FALSE, reportEstimates=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, ..., which = 1:length(x$theta),
                         useBound=TRUE, layout = c(2,2))
meta.table(x, d=3, option=2,
    filename=paste(deparse(substitute(x)),'_global.tex',sep=""), align=TRUE)

Value

The function print.sienaMeta prints details of the merged estimates of the meta-analysis carried out by siena08, with test statistics. See the help page for siena08

for what is produced by this function.

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.txt where projname is the value of the argument to siena08.

The function meta.table writes a combined table of results for all parameters to a LaTeX file in (as default) the current working directory. This table is a more compact version of the results presented by print.sienaMeta.

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.

Another funnel plot, not using siena08, is available as funnelPlot.

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.txt. If FALSE, output is to the terminal.

reportEstimates

Boolean: whether to report all estimates and standard errors.

extra

Boolean: if TRUE, prints more information.

which

Set of effects contained in the plot (given by sequence numbers).

useBound

Boolean: whether to restict plotted symbols to the bound used in the call of sienaMeta.

layout

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

d

Number of decimals to be used in table.

option

1: results without normality assumptions; 2: results with normality assumptions, with confidence intervals; 3: results with normality assumptions, with standard errors.

filename

filename for output; if "", printed to the console.

align

Whether to align numbers at the decimal point.

...

For extra arguments (none used at present).

Author

Ruth Ripley, Tom Snijders

References

Snijders, T.A.B, and Baerveldt, C. (2003), A Multilevel Network Study of the Effects of Delinquent Behavior on Friendship Evolution. Journal of Mathematical Sociology 27, 123--151.

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

See Also

siena08

Examples

if (FALSE) {
# 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, transRecTrip, fix=TRUE, test=TRUE)
effects.3 <- includeEffects(effects.3, transTrip)
effects.3 <- setEffect(effects.3, transRecTrip, fix=TRUE, test=TRUE)
effects.4 <- includeEffects(effects.4, transTrip)
effects.4 <- setEffect(effects.4, transRecTrip, 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)
print(meta, reportEstimates=FALSE)
print(meta)
summary(meta)
# For specifically presenting the Fisher combinations:
# First determine the number of estimated effects:
(neff <- sum(sapply(meta, function(x){ifelse(is.list(x),
        !is.null(x$cjplus),FALSE)})))
Fishers <- t(sapply(1:neff,
        function(i){c(meta[[i]]$cjplus, meta[[i]]$cjminus,
                        meta[[i]]$cjplusp, meta[[i]]$cjminusp, 2*meta[[i]]$n1 )}))
Fishers <- as.data.frame(Fishers, row.names=names(meta)[1:neff])
names(Fishers) <- c('Fplus', 'Fminus', 'pplus', 'pminus', 'df')
Fishers
# For plotting:
plo <- plot(meta, layout = c(3,1))
plo
plo[3]
# Show effects of bound (bounding at 0.4 is not reasonable, just for example)
meta <- siena08(ans.1, ans.3, ans.4, bound=0.4)
plot(meta, which=c(2,3), layout=c(2,1))
plot(meta, which=c(2,3), layout=c(2,1), useBound=FALSE)
meta.table(meta, option=3, file='')
}