metaSEM (version 0.9.8)

plot: Plot method for meta objects

Description

It plots the pooled and the individual effect sizes and their confidence ellipses for meta objects.

Usage

"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, ...)

Arguments

x

An object of class meta.

effect.sizes

Numeric values indicating which effect sizes to be plotted. At least two effect sizes are required. To plot the effect sizes of $y1$ and $y2$, one may use effect.sizes=c(1,2). If it is missing, all effect sizes will be plotted in a pairwise way.

add.margin

Value for additional margins on the left and bottom margins.

interval

Interval for the confidence ellipses.

main

Main title of each plot. If there are multiple plots, a vector of character titles may be used.

axis.labels

Labels for the effect sizes.

study.col

Color of individual studies. See col in par.

study.pch

Plotting character of individual studies. See pch in points.

study.min.cex

Minimum value of cex for individual studies. See cex in par.

study.weight.plot

Logical. If TRUE, the ploting 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.

study.ellipse.plot

Logical. If TRUE, the confidence ellipses of individual studies are plotted.

study.ellipse.col

Color of the confidence ellipses of individual studies. See col in par.

study.ellipse.lty

Line type of the confidence ellipse of individual studies. See lty in par.

study.ellipse.lwd

Line width of the confidence ellipse of individual studies. See lwd in par.

estimate.col

Color of the estimated effect size. See col in par.

estimate.pch

Plotting character of the estimated effect sizes. See pch in points.

estimate.cex

Amount of plotting of the estimated effect sizes. See cex in par.

estimate.ellipse.plot

Logical. If TRUE, the confidence ellipse of the estimated effect sizes will be plotted.

estimate.ellipse.col

Color of the confidence ellipse of the estimated effect sizes. See col in par.

estimate.ellipse.lty

Line type of the confidence ellipse of the estimated effect sizes. See lty in par.

estimate.ellipse.lwd

Line width of the confidence ellipse of the estimated effect sizes. See lwd in par.

randeff.ellipse.plot

Logical. If TRUE, the confidence ellipses of the random effects will be plotted.

randeff.ellipse.col

Color of the confidence ellipses of the random effects. See col in par.

randeff.ellipse.lty

Line type of the confidence ellipses of the random effects. See lty in par.

randeff.ellipse.lwd

Line width of the confidence ellipses of the random effects. See lwd in par.

univariate.plot

Logical. If TRUE, the estimated univariate effect sizes will be plotted.

univariate.lines.col

Color of the estimated univariate effect sizes. See col in par.

univariate.lines.lty

Line type of the estimated univariate effect sizes. See lty in par.

univariate.lines.lwd

Line width of the estimated univariate effect sizes. See lwd in par.

univariate.polygon.width

Width of the polygon of the estimated univariate effect sizes.

univariate.polygon.col

Color of the polygon of the estimated univariate effect sizes.

univariate.arrows.col

Color of the arrows of the estimated univariate effect sizes.

univariate.arrows.lwd

Line width of the arrows of the estimated univariate effect sizes.

diag.panel

Logical. If TRUE, diagonal panels will be created. They can then be used for forrest plots for univariate meta-analysis.

xlim

NULL or a numeric vector of length 2; if it is NULL, it provides defaults estimated from the data.

ylim

NULL or a numeric vector of length 2; if it is NULL, it provides defaults estimated from the data.

...

Further arguments to be passed to the plot function.

References

Cheung, M. W.-L. (2013). Multivariate meta-analysis as structural equation models. Structural Equation Modeling, 20, 429-454.

Examples

## Not run: 
# ## Multivariate meta-analysis
# x <- meta(y=cbind(PD, AL), v=cbind(var_PD, cov_PD_AL, var_AL), data=Berkey98)
# plot(x)
# 
# ## Plot individual studies proportional to the weights
# plot(x, study.weight.plot=TRUE)
# 
# ## Include forest plot from the metafor package
# library(metafor)
# plot(x, diag.panel=TRUE, main="Multivariate meta-analysis",
# axis.label=c("PD", "AL"))
# forest( rma(yi=PD, vi=var_PD, data=Berkey98) )
# title("Forest plot of PD")
# forest( rma(yi=AL, vi=var_AL, data=Berkey98) )
# title("Forest plot of AL")
# ## End(Not run)