gconsensus: Generalized Consensus Contructor

In gconsensus: Consensus Value Constructor

Generalized Consensus Contructor

Description

Compute a set of estimates for the generalized consensus value from a set of sources.

Usage

gconsensus(ilab, method = "mean", 
  build.model = NULL, get.samples = NULL,
  config = list(alpha = 0.05, 
    expansion.factor.type = "naive", 
    tau = mad(ilab$data$value),
    unreliable.uncertainties = FALSE, 
    MC_samples = 1e5,
    MC_burn_in = 1000,
    MC_seed = NA, 
    MC_use.HKSJ.adjustment = FALSE,
    filename = "hb_consensus_model.txt"))

Arguments

ilab	ilab object, containing the definition of the participating source/experts/laboratories.
method	string, the current statistical method for estimating the consensus value. The list of current valid methods is: mean [15], grand.mean [15], median [15], h15 for Huber estimator [15], MCM.mean for Monte Carlo mean, MCM.median for Monte Carlo median, GD1 for the original Graybill-Deal estimator [1], GD2 for the modified Graybill-Deal estimator as described by Sinha [13], GD3 for the modified Graybill-Deal estimator as described by Zhang [14], GD4 for the modified Graybill-Deal estimator as described by Zhang [14], DL1 for original DerSimonian-Laid estimator [2], DL2 for the modified DerSimonian-Laird estimator as described by Horn-Horn-Duncan [3], PM for original Paul-Mandel estimator [5], MPM for the modified Paul-Mandel estimator [6], VRMLE for Vangel-Rukhin MLE [7, 8, 9, 10, 11], BOB for the type B on Bias estimator [4], SE for the Schieller-Eberhardt [12], MCM.LP for linear pool, HB for Hierarchical Bayesian
build.model	a function (NULL by default) to build a JAGS model
get.samples	a function (NULL by dafault) to obtain the CODA samples based on the JAGS model
config	list, contains the configuration options used in the computational process. The complete list of options is: alpha for the significance level, default value = 0.05, expansion.factor.type for the type of coverage factor to be used: "naive" which use a coverage factor=2, "large sample" which uses a normal quantile, "small sample" which uses a t quantile, tau initial estimate of between source uncertainty, by default it takes the mad of the reported values, unreliable.uncertainties an indicator for unreliables uncertainties, if TRUE then adjustment is used [15], MC_samples the number of cycles used in the simple Monte Carlo simulation, MC_burn_in the number of initial cycles to be drop from Monte Carlo simulation. The Bayesian algorithm becomes stationary but in the begining it can be noisy, this is fixed by dropping the initial samples. MC_seed the seed used to initializing the random number generator in the Monte Carlo simulation, MC_use.HKSJ.adjustment a flag indicating if small sample adjustments are required [16], filename a string containing the name of the file for communication with JAGS package.

Details

The consensus estimation will vary with the specified method. Display can be customized by using the options mechanism. Available options are: display.signif.digits an integer representing the number of significant digits to be obtained in the adaptative approach of the Monte Carlo simulation, display.order a string ("name", "code", "location", "dispersion") representing the order to be used for displaying the uncertainty contribution, display.shownames a boolean, if true source names are used otherwise source codes are used, display.tab.size an integer representing the column width used in formating data tables, display.orientation a string ("horizontal", "vertical"), plot orientation. display.expandedUncertainty a boolean, plot standard uncertainties or expanded uncertainties.

Value

fit	a data.frame with the value, expanded uncertainty, unit, expansion factor, probability coverage and tau, the between effect uncertainty.
method	statistical method used to estimate the consensus value.
subset	the subset of included values in the consensus value estimation.
ilab	the current participants' information.
config	the current configuration options provided for the consensus value estimation process.
exercise	the exercise the consensus belongs to.
measurand	the measurand the consensus belongs to.

Author(s)

Hugo Gasca-Aragon

Maintainer: Hugo Gasca-Aragon <[e-mail]>

See Also

doe.gconsensus, print.gconsensus, plot.gconsensus

gconsensus documentation built on Nov. 10, 2022, 5:09 p.m.