Methods for processing sienaMeta objects
print, summary, and plot methods for
sienaMeta objects; and a function to write a LaTeX table.
## S3 method for class 'sienaMeta'
print(x, file=FALSE, reportEstimates=FALSE, ...)
## S3 method for class 'sienaMeta'
summary(object, file=FALSE, extra=TRUE, ...)
## S3 method for class 'summary.sienaMeta'
print(x, file=FALSE, extra=TRUE, ...)
## S3 method for class '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)object |
An object of class |
x |
An object of class |
file |
Boolean: if TRUE, sends output to file named
|
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 |
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). |
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.
Ruth Ripley, Tom Snijders
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/
## 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, 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='')
## End(Not run)Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.