mada (version 0.5.11)

forest: Forest plot for univariate measures

Description

Produce a forest plot. Includes graphical summary of results if applied to output of suitable model-fitting function. forest methods for madad and madauni objects are provided.

Usage

# S3 method for madad
forest(x, type = "sens", log = FALSE, ...)
# S3 method for madauni
forest(x, log = TRUE, ...)
forestmada(x, ci, plotci = TRUE, main = "Forest plot", xlab = NULL,
          digits = 2L,  snames = NULL, subset = NULL, pch = 15, 
          cex = 1, cipoly = NULL, polycol = NA, ...)

Value

Returns and invisible NULL.

Arguments

x

an object for which a forest method exists or (in the case of foresmada) a vector of point estimates.

ci

numeric matrix, each row corresponds to a confidence interval (the first column being the lower bound and the second the upper).

plotci

logical, should the effects sizes and their confidence intervals be added to the plot (as text)?

main

character, heading of plot.

xlab

label of x-axis.

digits

integer, number of digits for axis labels and confidence intervals.

snames

character vector, study names. If NULL, generic study names are generated.

subset

integer vector, allows to study only a subset of studies in the plot. One can also reorder the studies with the help of this argument.

pch

integer, plotting symbol, defaults to a small square. Also see plot.default.

cex

numeric, scaling parameter for study names and confidence intervals.

cipoly

logical vector, which confidence interval should be plotted as a polygon? Useful for summary estimates. If set to NULL, regular confidence intervals will be used.

polycol

color of the polygon(s), passed on to polygon. The default value of NA implies no color.

type

character, one of sens, spec, negLR, posLR or DOR.

log

logical, should the log-transformed values be plotted?

...

arguments to be passed on to forestmada and further on to other plotting functions

Author

Philipp Doebler <[e-mail]>

Details

Produces a forest plot to graphically assess heterogeneity. Note that forestmada is called internally, so that the ... argument can be used to pass on arguments to this function; see the examples.

See Also

madad, madauni

Examples

data(AuditC)

## Forest plot of log DOR with random effects summary estimate
forest(madauni(AuditC))

## Forest plot of negative likelihood ratio (no log transformation)
## color of the polygon: light grey 
## draw the individual estimate as filled circles
forest(madauni(AuditC, type = "negLR"), 
       log = FALSE, polycol = "lightgrey", pch = 19)

## Paired forest plot of sensitivities and specificities
## Might look ugly if device region is too small
old.par <- par()
AuditC.d <- madad(AuditC)

plot.new()
par(fig = c(0, 0.5, 0, 1), new = TRUE)
forest(AuditC.d, type = "sens", xlab = "Sensitivity")
par(fig = c(0.5, 1, 0, 1),  new = TRUE)
forest(AuditC.d, type = "spec", xlab = "Specificity")

par(old.par)

## Including study names
## Using Letters as dummies
forest(AuditC.d, type = "spec",  xlab = "Specificity",
      snames = LETTERS[1:14])