Description Usage Arguments Details Author(s) References See Also Examples
Produces a variety of plots for multiple tables analysis
1 2 3 4 
x 
an object inheriting from class 
type 
a chracter string specifying the type of plots to
produce. Options are 
select 
a numeric value or vector specifying which studies to
be plotted. By default (when 
xlab 
a character string specifying the xaxis label in the plot. Default is the name of the measure of association 
ylab 
a character string specifying the xaxis label in the plot. Default is "Density" 
file 
a character string specifying the filename as which the plots
are saved. By default (when 
xlim, ylim 
a numeric vectors of length 2 specifying the lower
and upper limits of the axes. By default (when 
xlabel 
a numeric vector specifying at which tickmarks are to
be drawn. By default (when 
addline 
a numeric value specifying the xvalue for a vertical
reference line at 
xlog 
a logical value indicating whether a logarithmic scale
should be used for xaxis. Default is 
mar 
A numerical vector of 4 values which control the space (in the number of lines)
between the axes and the border of the graph of the form

ciShow 
a logical value; if 
... 
Other arguments can be passed to plot function 
If type="sidebyside"
, the posterior distributions of all
studyspecific measure are displayed side by side in 4panel plots
with study names.
If type="overlap"
, the posterior distributions of all
studyspecific measure are displayed in one graph. To clarity, it
is advisable to specify a few studies by select
argument.
If type="forest")
, a forest plot of all studyspecific and
overall measure with 95% credible/confidence intervals are
plotted.
If file=NULL
, the plots will be displayed on screen. Or
else, the plots will be saved as "./mmeta/codefile.pdf", where
"./" denotes current working directory.
Xiao Su <Xiao.Su@uth.tmc.edu>
Luo, S., Chen, Y., Su, X., Chu, H., (2014). mmeta: An R Package for Multivariate MetaAnalysis. Journal of Statistical Software, 56(11), 126.
Chen, Y., Luo, S., (2011a). A Few Remarks on "Statistical Distribution of the Difference of Two Proportions' by Nadarajah and Kotz, Statistics in Medicine 2007; 26(18):35183523" . Statistics in Medicine, 30(15), 19131915.
Chen, Y., Chu, H., Luo, S., Nie, L., and Chen, S. (2014a). Bayesian analysis on metaanalysis of casecontrol studies accounting for withinstudy correlation. Statistical Methods in Medical Research, doi: 10.1177/0962280211430889. In press.
Chen, Y., Luo, S., Chu, H., Su, X., and Nie, L. (2014b). An empirical Bayes method for multivariate metaanalysis with an application in clinical trials. Communication in Statistics: Theory and Methods. In press.
Chen, Y., Luo, S., Chu, H., Wei, P. (2013). Bayesian inference on risk differences: an application to multivariate metaanalysis of adverse events in clinical trials. Statistics in Biopharmaceutical Research, 5(2), 142155.
multipletables
summary.multipletables
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31  #library(mmeta)
# Analyze the dataset colorectal to conduct exact inference of the odds ratios
#data(colorectal)
#multiple.OR < multipletables(data=colorectal, measure="OR", model="Sarmanov", method="exact")
# Generate the forest plot with 95% CIs of studyspecific odds ratios
#and 95% CI of overall odds ratio
#plot(multiple.OR, type="forest", addline=1)
# Plot the posterior density functions of some target studies in an overlaying manner
#plot(multiple.OR, type="overlap", select=c(4,14,16,20))
# Plot the posterior density functions of some target studies in a
#sidebyside manner
#plot(multiple.OR, type="sidebyside", select=c(4,14,16,20), ylim=c(0,2.7), xlim=c(0.5,1.5))
# Analyze the dataset withdrawal to conduct inference of the relative risks
#data(withdrawal)
#multiple.RR < multipletables(data=withdrawal, measure="RR",model="Sarmanov")
#plot(multiple.RR, type="forest", addline=1)
#plot(multiple.RR, type="overlap", select=c(3,8,14,16))
#plot(multiple.RR, type="sidebyside", select=c(3,8,14,16), ylim=c(0,1.2),
#xlim=c(0.4,3))
# Analyze the dataset withdrawal to conduct inference of the risk differences
#data(withdrawal)
#multiple.RD < multipletables(data=withdrawal, measure="RD", model="Sarmanov")
#summary(multiple.RD)
#plot(multiple.RD, type="forest", addline=0)
#plot(multiple.RD, type="overlap", select=c(3,8,14,16))
#plot(multiple.RD, type="sidebyside", select=c(3,8,14,16))
#plot(multiple.RD, type="sidebyside", select=c(3,8,14,16),
# ylim=c(0,6), xlim=c(0.2,0.4))

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