forest | R Documentation |

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.

## S3 method for class 'madad' forest(x, type = "sens", log = FALSE, ...) ## S3 method for class '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, ...)

`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>

`madad`

, `madauni`

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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