forestplot.nmadasfit: Forest plot

In VNyaga/NMADAS: Arm-Based Network Meta-Analaysis of Diagnostic Accuracy Studies

Description

Produce forest plots for categorical covariates.

Usage

forestplot.nmadasfit(object, vlinecolour = "blue", textsize = 4,
  pointcolour = "grey70", pointsize = 2, dp = 2,
  textlabel = "Mean [95% CI]", dodgewidth = 1, RR = TRUE, ...)

Arguments

object	A nmadasfit object from fit.
textsize	Size of the texts.
pointcolour	A text indicating the colour of the study specific points. Default is "grey70".
pointsize	Size of the study specific points. Default is 2.
dp	An optional positive value to control the number of digits to print when printing numeric values. The default is 2.
textlabel	The text that appear below the plots. By default it is "Mean [95% CI]".
dodgewidth	An optional numeric value to adjust the dogding position. The default is 1. See position_dodge.
RR	Logical which is by default TRUE to draw a forest plot of the relative sensitivity and relative specificity.
...	other stan options.
vlinecolor	A text indication the colour of the line at RR = 1. Default is "blue".
vline	colour of the line at RR = 1. By default it is "blue".

Value

A forestplot by ggplot2.

Author(s)

Victoria N Nyaga <[e-mail]>

References

Watanabe S (2010). Asymptotic Equivalence of Bayes Cross Validation and Widely Applicable Information Criterion in Singular Learning Theory. Journal of Machine Learning Research, 11, 3571-3594.

Vehtari A, Gelman A (2014). WAIC and Cross-validation in Stan. Unpublished, pp. 1-14.

Examples

## Not run: 
data(demodata)

frank <- nmadasmodel()

fit1 <- fit(
        nma.model = frank,
        S.ID='study',
		   T.ID = 'Test',
		   tp = 'TP',
		   tn = 'TN',
		   fp = 'FP',
		   fn = 'FN',
            data = demodata,
            iter = 6000,
            warmup = 2000,
            thin = 5,
            seed = 3)


forestplot(fit1)

## End(Not run)

VNyaga/NMADAS documentation built on May 6, 2019, 11:20 a.m.