plot.multipletables {mmeta} | R Documentation |
multipletables
objectsProduces a variety of plots for multiple tables analysis
## S3 method for class 'multipletables' plot(x,type=NULL,select=NULL,file=NULL, xlim=NULL,ylim=NULL, xlabel=NULL,mar=NULL,xlog=TRUE, addline=NULL,xlab=NULL,ylab=NULL,ciShow=TRUE,...)
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 x-axis label in the plot. Default is the name of the measure of association |
ylab |
a character string specifying the x-axis 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 tick-marks are to
be drawn. By default (when |
addline |
a numeric value specifying the x-value for a vertical
reference line at |
xlog |
a logical value indicating whether a logarithmic scale
should be used for x-axis. 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
study-specific measure are displayed side by side in 4-panel plots
with study names.
If type="overlap"
, the posterior distributions of all
study-specific 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 study-specific 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 Meta-Analysis. Journal of Statistical Software, 56(11), 1-26.
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):3518-3523" . Statistics in Medicine, 30(15), 1913-1915.
Chen, Y., Chu, H., Luo, S., Nie, L., and Chen, S. (2014a). Bayesian analysis on meta-analysis of case-control studies accounting for within-study 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 meta-analysis 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 meta-analysis of adverse events in clinical trials. Statistics in Biopharmaceutical Research, 5(2), 142-155.
multipletables
summary.multipletables
#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 study-specific 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 #side-by-side 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))