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.

1 2 3 4 5 6 7 |

`x` |
an object for which a |

`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 |

`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 |

`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 |

`polycol` |
color of the polygon(s), passed on to |

`type` |
character, one of |

`log` |
logical, should the log-transformed values be plotted? |

`...` |
arguments to be passed on to |

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.

Returns and invisible `NULL`

.

Philipp Doebler <philipp.doebler@googlemail.com>

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 | ```
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])
``` |

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