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setMethod('show', signature = c("BFmcmc"),
function(object){
show(S3Part(object))
show(object@model)
}
)
setAs("BFmcmc" , "mcmc",
function ( from , to ){
as.mcmc(from)
})
setAs("BFmcmc" , "matrix",
function ( from , to ){
as.matrix(from)
})
setAs("BFmcmc" , "data.frame",
function ( from , to ){
as.data.frame(from)
})
#' @rdname recompute-methods
#' @aliases recompute,BFmcmc-method
setMethod('recompute', signature(x = "BFmcmc", progress="ANY"),
function(x, progress, ...){
posterior(model=x@model, data = x@data, progress = progress, ...)
}
)
setMethod('compare', signature(numerator = "BFmcmc", denominator = "BFmcmc"),
function(numerator, denominator, ...){
compare(numerator = numerator@model, data = numerator@data, ...) /
compare(numerator = denominator@model, data = denominator@data, ...)
}
)
setMethod('compare', signature(numerator = "BFmcmc", denominator = "missing"),
function(numerator, denominator, ...){
compare(numerator = numerator@model, data = numerator@data, ...)
}
)
#' @rdname posterior-methods
#' @aliases posterior,BFmodel,missing,data.frame,missing-method
setMethod("posterior", signature(model="BFmodel", index="missing", data="data.frame", iterations="missing"),
function(model, index, data, iterations, ...)
stop("Iterations must be specified for posterior sampling.")
)
#' @rdname posterior-methods
#' @aliases posterior,BFBayesFactor,missing,missing,missing-method
setMethod("posterior", signature(model="BFBayesFactor", index="missing", data="missing", iterations="missing"),
function(model, index, data, iterations, ...)
stop("Iterations must be specified for posterior sampling.")
)
#' @rdname posterior-methods
#' @aliases posterior,BFBayesFactor,numeric,missing,numeric-method
setMethod('posterior', signature(model = "BFBayesFactor", index = "numeric", data = "missing", iterations = "numeric"),
function(model, index, data, iterations, ...){
if(length(model[index])>1) stop("Index must specify single element.")
posterior(model = model[index], iterations = iterations, ...)
}
)
#' @rdname posterior-methods
#' @aliases posterior,BFBayesFactor,missing,missing,numeric-method
setMethod('posterior', signature(model = "BFBayesFactor", index = "missing", data = "missing", iterations = "numeric"),
function(model, index=NULL, data, iterations, ...){
if(length(model)>1) stop("Index argument required for posterior with multiple numerators.")
posterior(model = model@numerator[[1]], data = model@data, iterations = iterations, ...)
}
)
#' @rdname posterior-methods
#' @aliases posterior,BFlinearModel,missing,data.frame,numeric-method
setMethod('posterior', signature(model = "BFlinearModel", index = "missing", data = "data.frame", iterations = "numeric"),
function(model, index = NULL, data, iterations, ...){
rscaleFixed = rpriorValues("allNways","fixed",model@prior$rscale[['fixed']])
rscaleRandom = rpriorValues("allNways","random",model@prior$rscale[['random']])
rscaleCont = rpriorValues("regression",,model@prior$rscale[['continuous']])
rscaleEffects = model@prior$rscale[['effects']]
formula = formula(model@identifier$formula)
checkFormula(formula, data, analysis = "lm")
factors = fmlaFactors(formula, data)[-1]
nFactors = length(factors)
dataTypes = model@dataTypes
relevantDataTypes = dataTypes[names(dataTypes) %in% factors]
dv = stringFromFormula(formula[[2]])
dv = composeTerm(dv)
if(model@type != "JZS") stop("Unknown model type.")
if( nFactors == 0 ){
stop("Sampling from intercept-only model not implemented.")
}else if(all(relevantDataTypes == "continuous")){
## Regression
X = fullDesignMatrix(formula, data, dataTypes)
chains = linearReg.Gibbs(y = data[[dv]],covariates = X,iterations = iterations, rscale = rscaleCont, ...)
}else if(all(relevantDataTypes != "continuous")){
# ANOVA or t test
chains = nWayFormula(formula=formula, data = data,
dataTypes = dataTypes,
rscaleFixed = rscaleFixed,
rscaleRandom = rscaleRandom,
rscaleEffects = rscaleEffects,
iterations = iterations,
posterior = TRUE, ...)
}else{
# GLM
chains = nWayFormula(formula=formula, data = data,
dataTypes = dataTypes,
rscaleFixed = rscaleFixed,
rscaleRandom = rscaleRandom,
rscaleCont = rscaleCont,
rscaleEffects = rscaleEffects,
iterations = iterations,
posterior = TRUE, ...)
}
return(new("BFmcmc",chains, model = model, data = data))
}
)
#' @rdname posterior-methods
#' @aliases posterior,BFindepSample,missing,data.frame,numeric-method
setMethod('posterior', signature(model = "BFindepSample", index = "missing", data = "data.frame", iterations = "numeric"),
function(model, index = NULL, data, iterations, ...){
formula = formula(model@identifier$formula)
rscale = model@prior$rscale
interval = model@prior$nullInterval
nullModel = ( formula[[3]] == 1 )
chains = ttestIndepSample.Gibbs(formula, data, nullModel, iterations,rscale, interval,...)
new("BFmcmc",chains,model = model, data = data)
})
#' @rdname posterior-methods
#' @aliases posterior,BFcontingencyTable,missing,data.frame,numeric-method
setMethod('posterior', signature(model = "BFcontingencyTable", index = "missing", data = "data.frame", iterations = "numeric"),
function(model, index = NULL, data, iterations, ...){
mod = formula(model@identifier)
type = model@type
prior = model@prior$a
marg = model@prior$fixedMargin
chains = sampleContingency(mod, type, marg, prior, data = data, iterations = iterations, ...)
new("BFmcmc",chains,model = model, data = data)
})
#' @rdname posterior-methods
#' @aliases posterior,BFoneSample,missing,data.frame,numeric-method
setMethod('posterior', signature(model = "BFoneSample", index = "missing", data = "data.frame", iterations = "numeric"),
function(model, index = NULL, data, iterations, ...){
mu = model@prior$mu
rscale = model@prior$rscale
interval = model@prior$nullInterval
nullModel = ( model@identifier$formula == "y ~ 0" )
chains = ttestOneSample.Gibbs(y = data$y, nullModel, iterations = iterations, rscale = rscale,
nullInterval = interval, ...)
new("BFmcmc",chains,model = model, data = data)
})
#' @rdname posterior-methods
#' @aliases posterior,BFmetat,missing,data.frame,numeric-method
setMethod('posterior', signature(model = "BFmetat", index = "missing", data = "data.frame", iterations = "numeric"),
function(model, index = NULL, data, iterations, ...){
rscale = model@prior$rscale
interval = model@prior$nullInterval
nullModel = ( model@identifier$formula == "d = 0" )
chains = meta.t.Metrop(t = data$t, n1 = data$n1, n2 = data$n2, nullModel, iterations = iterations,
rscale = rscale, nullInterval = interval, ...)
new("BFmcmc",chains, model = model, data = data)
})
#' @rdname posterior-methods
#' @aliases posterior,BFproportion,missing,data.frame,numeric-method
setMethod('posterior', signature(model = "BFproportion", index = "missing", data = "data.frame", iterations = "numeric"),
function(model, index = NULL, data, iterations, ...){
rscale = model@prior$rscale
p = model@prior$p0
interval = model@prior$nullInterval
nullModel = ( model@identifier$formula == "p = p0" )
chains = proportion.Metrop(y = data$y, N = data$N, nullModel, iterations = iterations,
nullInterval = interval, p = p, rscale = rscale, ...)
new("BFmcmc",chains, model = model, data = data)
})
#' @rdname posterior-methods
#' @aliases posterior,BFcorrelation,missing,data.frame,numeric-method
setMethod('posterior', signature(model = "BFcorrelation", index = "missing", data = "data.frame", iterations = "numeric"),
function(model, index = NULL, data, iterations, ...){
rscale = model@prior$rscale
interval = model@prior$nullInterval
nullModel = ( model@identifier$formula == "rho = 0" )
chains = correlation.Metrop(y = data$y, x = data$x, nullModel, iterations = iterations,
nullInterval = interval, rscale = rscale, ...)
new("BFmcmc",chains, model = model, data = data)
})
###########
## S3
###########
as.mcmc.BFmcmc <- function(x, ...){
return(S3Part(x))
}
as.matrix.BFmcmc <- function(x,...){
return(as.matrix(S3Part(x)))
}
as.data.frame.BFmcmc <- function(x, row.names=NULL,optional=FALSE,...){
return(as.data.frame(S3Part(x)))
}
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