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metagen (version 1.0)

Inference in Meta Analysis and Meta Regression

Description

Provides methods for making inference in the random effects meta regression model such as point estimates and confidence intervals for the heterogeneity parameter and the regression coefficients vector. Inference methods are based on different approaches to statistical inference. Methods from three different schools are included: methods based on the method of moments approach, methods based on likelihood, and methods based on generalised inference. The package also includes tools to run extensive simulation studies in parallel on high performance clusters in a modular way. This allows extensive testing of custom inferential methods with all implemented state-of-the-art methods in a standardised way. Tools for evaluating the performance of both point and interval estimates are provided. Also, a large collection of different pre-defined plotting functions is implemented in a ready-to-use fashion.

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Version

Install

install.packages('metagen')

Monthly Downloads

27

Version

1.0

License

GPL-3

Maintainer

Thomas W D Mbius

Last Published

May 27th, 2014

Functions in metagen (1.0)

cbbPalette

Colour palettes for colour blind people
metagenEmpty

Inference: Empty skeleton
makeConfInt

Interval estimates: Generic function
cbgPalette

Colour palettes for colour blind people
formulaR

Regression coefficients: formulaR
hEstimates

Point estimates: For the heterogeneity parameter
dvec

Data generation: Sampling data of clinical trials
performance

Running a computer experiment
lenBoxByType

Plotting performance: Box plot of mean width
experimentY

Running a computer experiment
pivotalStream

Steams of pivotal quantities of the regression coefficient
boxByConfidence

Plotting performance: Box plots for target value confidence-coverage
bcgVaccineData

Example: Setting up the BCG-data set
hConfidence

Inference: Based on methods of moments and maximum likelihood.
yvec

Data generation: Sampling data of clinical trials
designD

Design: Gaussian responses (unknown heteroscedasticity)
render

Render plot: To PDF
collectAllExperiments

Running a computer experiment -- Collect all the results
regressionEstimates

Point estimates: For the regression coefficients
sdsByMethod

Plotting performance: Scatter plot against heteroscedasticity
boxByMethod

Plotting performance: Box plots for target value confidence-coverage
sctVersusC

Plotting performance: Scatter plot against heterogeneity
sctVersusH

Plotting performance: Scatter plot against heterogeneity
plotStudyUnbalance

Example: Plotting study unbalances in group assignments
plotDensityH2

Pivotal distributions: Plot pivot density of the heterogeneity
pfunc

The p_delta(eta) function.
boxByType

Plotting performance: Box plots for target value confidence-coverage
rY

Data generation: Gaussian-Gaussian model
boxSD

Plotting performance: Box plots for standard deviation
collectExperiments

Running a computer experiment -- Collect specific results
performanceConfH

Running a computer experiment: Adding performance measures
experimentD

Running a computer experiment
makeConfInts

Interval estimates: Generic function
lenBoxByMethod

Plotting performance: Box plot of mean width
plotStudyQfuncPfunc

Example: Plotting the q- and p-function from the dissertation
sctBias

Plotting performance: Scatter plots against heterogeneity
renderSVG

Render plot: To SVG
metagenGeneralised

Inference: Based on generalised inference principles.
metareg

Inference: Based on methods of moments and maximum likelihood.
performancePointR

Running a computer experiment: Adding performance measures
setupExperiment

Running a computer experiment in batch mode
plotDensityIntercept

Pivotal distributions: Plot pivotal distribution of regression coefficients
rBinomGauss

Data generation: Sampling data of clinical trials
joinPivotalHeterogeneity

Pivotal distributions: Extract pivots for heterogeneity
formulaL

Regression coefficients: formulaL
designB

Design: Binomial responses
performanceConfR

Running a computer experiment: Adding performance measures
rD

Data generation: Gaussian-Gaussian model
joinPivotalCoefficients

Pivotal distributions: Extract pivots for regression coefficients
performancePointH

Running a computer experiment: Adding performance measures
plotDensitySlope2

Pivotal distributions: Plot pivotal distribution of regression coefficients
qfunc

The q_delta(tau) function.
sctMSE

Plotting performance: Scatter plots against heterogeneity
sdmByMethod

Plotting performance: Scatter plot against heterogeneity
plotIntervalEstimates

Example: Plotting interval estimates
sdsByType

Plotting performance: Scatter plot against heteroscedasticity
plotStudyForest

Example: Plotting a forest plot of a data frame
boxBias

Plotting performance: Box plots for bias
designY

Design: Gaussian responses (known heteroscedasticity)
plotDensityIntercept2

Pivotal distributions: Plot pivotal distribution of regression coefficients
plotStudySizes

Example: Plotting study sizes
intervalEstimates

Interval estimates: For the regression coefficients
rB

Data generation: Log-risk-ration of a binomial-Gaussian model
plotDensityH

Pivotal distributions: Plot pivotal distribution of heterogeneity
sdmByType

Plotting performance: Scatter plot against heterogeneity
metagen

Inference: Analysis of the data set
plotCoefficientInterval

Plot pivots: Interval estimates of the heterogeneity
sctSD

Plotting performance: Scatter plots against heterogeneity
plotHeterogeneityInterval

Plot pivots: Interval estimates of the heterogeneity
boxMSE

Plotting performance: Box plots for mean squared error
lenDenByMethod

Plotting performance: Density estimate of mean width
lenDenByType

Plotting performance: Density estimate of mean width
plotDensitySlope

Pivotal distributions: Plot pivotal distribution of regression coefficients