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DTAplots (version 1.0.2.5)

Creates Plots Accompanying Bayesian Diagnostic Test Accuracy Meta-Analyses

Description

Function to create forest plots. Functions to use posterior samples from Bayesian bivariate meta-analysis model, Bayesian hierarchical summary receiver operating characteristic (HSROC) meta-analysis model or Bayesian latent class (LC) meta-analysis model to create Summary Receiver Operating Characteristic (SROC) plots using methods described by Harbord et al (2007).

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Install

install.packages('DTAplots')

Monthly Downloads

264

Version

1.0.2.5

License

GPL (>= 2)

Maintainer

Ian Schiller

Last Published

October 15th, 2021

Functions in DTAplots (1.0.2.5)

SROC_rjags

A function to create a summary plot in Receiver Operating Characteristic (ROC) space
posterior_samples_HSROC dataset

Samples of the posterior distributions obtained from a Bayesian HSROC meta-analysis model
Anti_CCP

Anti-CCP dataset
posterior_samples_Bivariate dataset

Samples of the posterior distributions obtained from a Bayesian bivariate meta-analysis model
Xpert

Xpert dataset
posterior_samples_LC dataset

Samples of the posterior distributions obtained from a Bayesian latent class (LC) meta-analysis model
Forest

Forest plot for sensitivity and specificity