makeObject: A function used to make a 'meta4diag' object.
In meta4diag: Meta-Analysis for Diagnostic Test Studies

makeObject

R Documentation

A function used to make a `meta4diag` object.

Description

Takes an internal data list, an internal prior setting list and an INLA object produced by makeData(), makePriors() and runModel(), respectively and makes a meta4diag object which contains various informations for later use. This function is used in the main function meta4diag() and can also be used as a separate function.

Usage

  makeObject(model, nsample=FALSE, seed=0L)

Arguments

`model`	An `INLA` object. Get from function `runModel()`.
`nsample`	A numerical value specifying the number of posterior samples, default is FALSE. The posterior samples are used to compute the marginals and estimates values of non-linear functions, such as log ratios and diagnostic odds ratios. If `nsample` is given, `summary.summarized.statistics`, `summary.fitted.LRpos`, `summary.fitted.LRneg`, `summary.fitted.DOR` and samples of `E(se)`, `E(sp)`, `E(1-se)` and `E(1-sp)` will be returned.
`seed`	A numerical value specifying the random seed to control the RNG for generating posterior samples if nsample > 0. If you want reproducible results, you ALSO need to control the seed for the RNG in R by controlling the variable .Random.seed or using the function set.seed.

Value

makeObject returns a meta4diag object with components:

`data`	The provided input data.
`outdata`	The internal data that could be used in INLA from function `makeData()`.
`priors.density`	Prior distributions for the variance components and correlation from function `makePriors()`.
`names.fitted`	Names of the jointly modelled accuracies in the model. For example, se and sp or (1-se) and sp.
`cpu.used`	The cpu time used for running the model.
`call`	The matched call.
`summary.fixed`	Matrix containing the mean and standard deviation (plus, possibly quantiles) of the fixed effects of the model.
`marginals.fixed`	A list containing the posterior marginal densities of the fixed effects of the model.
`summary.expected.(...).accuracy`	Matrix containing the mean and standard deviation (plus, possibly quantiles) of the mean of accuracies transformed with the link function, i.e. E(g(Se)), E(g(Sp)), E(g(1-Se)) and E(g(1-Sp)). Dynamic name for this output. (...) indicates the name of link function used in `runModel()`, i.e. if link function is "logit", the full name of this output is "summary.expected.logit.accuracy".
`marginals.expected.(...).accuracy`	A list containing the posterior marginal densities of the mean of accuracies transformed with the link function, i.e. E(g(Se)), E(g(Sp)), E(g(1-Se)) and E(g(1-Sp)). Dynamic name for this output. (...) indicates the name of link function used in `runModel()`, i.e. if link function is "logit", the full name of this output is "marginals.expected.logit.accuracy".
`summary.expected.accuracy`	Matrix containing the mean and standard deviation (plus, possibly quantiles) of the mean of the accuracies, i.e. E(Se), E(Sp), E(1-Se) and E(1-Sp).
`marginals.expected.accuracy`	A list containing the posterior marginal densities of of the mean of the accuracies, i.e. E(Se), E(Sp), E(1-Se) and E(1-Sp).
`summary.hyperpar`	A matrix containing the mean and sd (plus, possibly quantiles) of the hyperparameters of the model.
`marginals.hyperpar`	A list containing the posterior marginal densities of the hyperparameters of the model.
`correlation.expected.(...).accuracy`	A correlation matrix between the mean of the accuracies transformed with the link function. Dynamic name for this output. (...) indicates the name of link function used in `runModel()`.
`covariance.expected.(...).accuracy`	A covariance matrix between the mean of the accuracies transformed with the link function. Dynamic name for this output. (...) indicates the name of link function used in `runModel()`.
`summary.predictor.(...)`	A matrix containing the mean and sd (plus, possibly quantiles) of the linear predictors one transformed accuracy in the model. The accuracy type depends on the model type. See argument `model.type`. For example, the possible accuracy type could be `g(se)`, `g(sp)`, `(se)` or `(sp)`, where `g()` is the link function.
`marginals.predictor.(...)`	A list containing the posterior marginals of the linear predictors of one transformed accuracy in the model. The accuracy type depends on the model type. See argument `model.type`. For example, the possible accuracy type could be `g(se)`, `g(sp)`, `(se)` or `(sp)`, where `g()` is the link function.
`misc`	Some other settings that maybe useful retruned by meta4diag.
`dic`	The deviance information criteria and effective number of parameters.
`cpo`	A list of three elements: `cpo$cpo` are the values of the conditional predictive ordinate (CPO), `cpo$pit` are the values of the probability integral transform (PIT) and `cpo$failure` indicates whether some assumptions are violated. In short, if cpo$failure[i] > 0 then some assumption is violated, the higher the value (maximum 1) the more seriously.
`waic`	A list of two elements: `waic$waic` is the Watanabe-Akaike information criteria, and `waic$p.eff` is the estimated effective number of parameters.
`mlik`	The log marginal likelihood of the model
`inla.result`	A `INLA` object that from function `runModel()` which implements INLA.
`samples.fixed`	A matrix of the fixed effects samples if `nsample` is given.
`samples.hyperpar`	A matrix of the hyperparameter samples if `nsample` is given.
`samples.overall.Se`	A matrix containing the mean and sd (plus, possibly quantiles) of overall sensitivity samples if `nsample` is given.
`samples.overall.Sp`	A matrix containing the mean and sd (plus, possibly quantiles) of overall specificity samples if `nsample` is given.
`summary.overall.statistics`	A matrix containing the mean and sd (plus, possibly quantiles) of mean positive and negative likelihood ratios and mean diagnostic odds ratios if `nsample` is given.
`samples.study.specific.Se`	A matrix containing the mean and sd (plus, possibly quantiles) of study specific sensitivity samples if `nsample` is given.
`samples.study.specific.Sp`	A matrix containing the mean and sd (plus, possibly quantiles) of study specific specificity samples if `nsample` is given.
`summary.study.specific.LRpos`	A matrix containing the mean and sd (plus, possibly quantiles) of positive likelihood ratios for each study if `nsample` is given.
`summary.study.specific.LRneg`	A matrix containing the mean and sd (plus, possibly quantiles) of negative likelihood ratios for each study if `nsample` is given.
`summary.study.specific.DOR`	A matrix containing the mean and sd (plus, possibly quantiles) of diagnostic odds ratios for each study if `nsample` is given.
`summary.study.specific.RD`	A matrix containing the mean and sd (plus, possibly quantiles) of risk difference for each study if `nsample` is given.
`summary.study.specific.LDOR`	A matrix containing the mean and sd (plus, possibly quantiles) of log diagnostic odds ratios for each study if `nsample` is given.
`summary.study.specific.LLRpos`	A matrix containing the mean and sd (plus, possibly quantiles) of log positive likelihood ratios for each study if `nsample` is given.
`summary.study.specific.LLRneg`	A matrix containing the mean and sd (plus, possibly quantiles) of log negative likelihood ratios for each study if `nsample` is given.

Author(s)

Jingyi Guo

Examples

## Not run: 
if(requireNamespace("INLA", quietly = TRUE)){
  require("INLA", quietly = TRUE)
  data(Catheter)
  outdata = makeData(Catheter)
  outpriors = makePriors()
  model = runModel(outdata=outdata, outpriors=outpriors, link="logit")
  res = makeObject(outdata, outpriors, model, nsample=2000)
}

## End(Not run)

meta4diag documentation built on Nov. 29, 2023, 3:01 a.m.