mcmc_array-class: Working with 3D MCMC arrays

In multinma: Bayesian Network Meta-Analysis of Individual and Aggregate Data

Working with 3D MCMC arrays

Description

3D MCMC arrays (Iterations, Chains, Parameters) are produced by as.array() methods applied to stan_nma or nma_summary objects.

Usage

## S3 method for class 'mcmc_array'
summary(object, ..., probs = c(0.025, 0.25, 0.5, 0.75, 0.975))

## S3 method for class 'mcmc_array'
print(x, ...)

## S3 method for class 'mcmc_array'
names(x)

## S3 replacement method for class 'mcmc_array'
names(x) <- value

Arguments

...	Further arguments passed to other methods
probs	Numeric vector of quantiles of interest
x, object	A 3D MCMC array of class mcmc_array
value	Character vector of replacement parameter names

Value

The summary() method returns a nma_summary object, the print() method returns x invisibly. The names() method returns a character vector of parameter names, and ⁠names()<-⁠ returns the object with updated parameter names.

Examples

## Smoking cessation

# Run smoking RE NMA example if not already available
if (!exists("smk_fit_RE")) example("example_smk_re", run.donttest = TRUE)


# Working with arrays of posterior draws (as mcmc_array objects) is
# convenient when transforming parameters

# Transforming log odds ratios to odds ratios
LOR_array <- as.array(relative_effects(smk_fit_RE))
OR_array <- exp(LOR_array)

# mcmc_array objects can be summarised to produce a nma_summary object
smk_OR_RE <- summary(OR_array)

# This can then be printed or plotted
smk_OR_RE
plot(smk_OR_RE, ref_line = 1)

# Transforming heterogeneity SD to variance
tau_array <- as.array(smk_fit_RE, pars = "tau")
tausq_array <- tau_array^2

# Correct parameter names
names(tausq_array) <- "tausq"

# Summarise
summary(tausq_array)

multinma documentation built on May 31, 2023, 5:46 p.m.