It plots the models from either the lavaan model or
meta, wls, and osmasem objects.
# S3 method for meta
plot(x, effect.sizes, add.margin = 0.1, interval = 0.95,
main= "Effect Sizes and their Confidence Ellipses",
axis.labels= paste("Effect size ", effect.sizes, sep = ""),
study.col = "black", study.pch = 19, study.min.cex = 0.8,
study.weight.plot = FALSE, study.ellipse.plot = TRUE,
study.ellipse.col = "black", study.ellipse.lty = 2,
study.ellipse.lwd = 0.5, estimate.col = "blue",
estimate.pch = 18, estimate.cex = 2,
estimate.ellipse.plot = TRUE, estimate.ellipse.col = "red",
estimate.ellipse.lty = 1, estimate.ellipse.lwd = 2,
randeff.ellipse.plot = TRUE, randeff.ellipse.col = "green",
randeff.ellipse.lty = 1, randeff.ellipse.lwd = 2,
univariate.plot = TRUE, univariate.lines.col = "gray",
univariate.lines.lty = 3, univariate.lines.lwd = 1,
univariate.polygon.width = 0.02,
univariate.polygon.col = "red",
univariate.arrows.col = "green", univariate.arrows.lwd = 2,
diag.panel = FALSE, xlim=NULL, ylim=NULL, ...)
# S3 method for character
plot(x, fixed.x=FALSE, nCharNodes=0, nCharEdges=0,
layout=c("tree", "circle", "spring", "tree2", "circle2"),
sizeMan=8, sizeLat=8, edge.label.cex=1.3, color="white", ...)
# S3 method for wls
plot(x, manNames=NULL, latNames=NULL, labels=c("labels", "RAM"),
what="est", nCharNodes=0, nCharEdges=0,
layout=c("tree", "circle", "spring", "tree2", "circle2"),
sizeMan=8, sizeLat=8, edge.label.cex=1.3, color="white",
weighted=FALSE, ...)
# S3 method for osmasem
plot(x, manNames=NULL, latNames=NULL, labels=c("labels", "RAM"),
what="est", nCharNodes=0, nCharEdges=0,
layout=c("tree", "circle", "spring", "tree2", "circle2"),
sizeMan=8, sizeLat=8, edge.label.cex=1.3, color="white",
weighted=FALSE, ...)
# S3 method for mxRAMmodel
plot(x, manNames=NULL, latNames=NULL, labels=c("labels", "RAM"),
what="est", nCharNodes=0, nCharEdges=0,
layout=c("tree", "circle", "spring", "tree2", "circle2"),
sizeMan=8, sizeLat=8, edge.label.cex=1.3, color="white",
weighted=FALSE, ...)
An object returned from either a lavaan model class
character, osmasem, osmasem3L, wls or meta
Numeric values indicating which effect sizes to
be plotted. At least two effect sizes are required. To plot the effect sizes of \(y_1\) and
\(y_2\), one may use effect.sizes=c(1,2). If it is missing, all effect sizes will be plotted in a
pairwise way.
Value for additional margins on the left and bottom margins.
Interval for the confidence ellipses.
Main title of each plot. If there are multiple plots, a vector of character titles may be used.
Labels for the effect sizes.
The color for individual studies. See col in par.
Plotting character of individual studies. See pch in points.
The minimum value of cex for individual
studies. See cex in par.
Logical. If TRUE, the plotting size of individual
studies (cex) will be proportional to one over the square root of the
determinant of the sampling covariance matrix of the study.
Logical. If TRUE, the confidence
ellipses of individual studies are plotted.
The color of the confidence ellipses of
individual studies. See col in par.
The line type of the confidence ellipse of
individual studies. See lty in par.
The line width of the confidence ellipse of
individual studies. See lwd in par.
The color of the estimated effect size. See col in par.
Plotting character of the estimated effect sizes. See pch in points.
The amount of plotting of the estimated effect sizes. See cex in par.
Logical. If TRUE, the confidence
ellipse of the estimated effect sizes will be plotted.
The color of the confidence
ellipse of the estimated effect sizes. See col in par.
The line type of the confidence
ellipse of the estimated effect sizes. See lty in par.
The line width of the confidence
ellipse of the estimated effect sizes. See lwd in par.
Logical. If TRUE, the confidence
ellipses of the random effects will be plotted.
Color of the confidence
ellipses of the random effects. See col in par.
The line type of the confidence
ellipses of the random effects. See lty in par.
The line width of the confidence
ellipses of the random effects. See lwd in par.
Logical. If TRUE, the estimated univariate effect
sizes will be plotted.
The color of the estimated univariate effect
sizes. See col in par.
The line type of the estimated univariate effect
sizes. See lty in par.
The line width of the estimated univariate effect
sizes. See lwd in par.
The width of the polygon of the estimated univariate effect sizes.
The color of the polygon of the estimated univariate effect sizes.
The color of the arrows of the estimated univariate effect sizes.
The line width of the arrows of the estimated univariate effect sizes.
Logical. If TRUE, diagonal panels will be
created. They can then be used for forrest plots for univariate meta-analysis.
NULL or a numeric vector of length 2; if it is NULL, it provides defaults estimated from the data.
NULL or a numeric vector of length 2; if it is NULL, it provides defaults estimated from the data.
Argument passed to
semPlotModel.
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Argument passed to semPaths
Further arguments passed to the methods.
Mike W.-L. Cheung <mikewlcheung@nus.edu.sg>
Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20, 429-454.
Berkey98, wvs94a meta2semPlot semPaths
# \donttest{
## lavaan model
model <- "y ~ m + x
m ~ x"
plot(model)
# }
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