R/AllClasses-15-Model-hierarchy.R
In StatisticsNZ/demest: Bayesian Demographic Estimation and Forecasting
## Virtual classes ###########################################################
setClass("NotUseExposure",
contains = "VIRTUAL")
setClass("UseExposure",
contains = "VIRTUAL")
setClass("Model",
slots = c(call = "call"),
contains = c("VIRTUAL",
"MetadataY",
"SlotsToExtract",
"IMethodModel"))
## HAS_TESTS
setClass("Binomial",
contains = c("VIRTUAL",
"UseExposure",
"ScaleThetaMultiplierMixin",
"StrucZeroArrayMixin"),
validity = function(object) {
theta <- object@theta
struc.zero.array <- object@strucZeroArray
is.struc.zero <- struc.zero.array@.Data == 0L
## 'theta' is non-negative
if (any(theta[!is.struc.zero] < 0))
return(gettextf("'%s' has negative values", "theta"))
## 'theta' is less than or equal to 1
if (any(theta[!is.struc.zero] > 1))
return(gettextf("'%s' has values greater than %d",
"theta", 1L))
TRUE
})
## HAS_TESTS
setClass("Normal",
contains = c("VIRTUAL",
"NotUseExposure"))
## HAS_TESTS
setClass("Poisson",
contains = c("VIRTUAL",
"BoxCoxParamMixin",
"ScaleThetaMultiplierMixin",
"StrucZeroArrayMixin"),
validity = function(object) {
theta <- object@theta
## 'theta' is non-negative
if (any(theta[!is.na(theta)] < 0))
return(gettextf("'%s' has negative values", "theta"))
TRUE
})
## NO_TESTS
setClass("CMP",
contains = c("VIRTUAL",
"BoxCoxParamMixin",
"SDLogNuCMPMixin",
"MeanLogNuCMPMixin",
"NuCMPMixin",
"ScaleThetaMultiplierMixin",
"NFailedPropYStarMixin",
"StrucZeroArrayMixin"),
validity = function(object) {
theta <- object@theta
## 'theta' is non-negative
if (any(theta[!is.na(theta)] < 0))
return(gettextf("'%s' has negative values", "theta"))
TRUE
})
## HAS_TESTS
setClass("NormalFixed",
contains = c("Model", "MeanSDMixin", "MeanSDMetadataAllMixin"),
validity = function(object) {
mean <- object@mean@.Data
metadataY <- object@metadataY
## 'metadataY' and 'mean' consistent
if (!identical(length(mean), as.integer(prod(dim(metadataY)))))
return(gettextf("'%s' and '%s' inconsistent",
"mean", "metadataY"))
TRUE
})
## NO_TESTS
setClass("TFixed",
contains = c("VIRTUAL",
"Model",
"MeanSDMixin",
"MeanSDMetadataAllMixin",
"NuMixin"),
validity = function(object) {
mean <- object@mean@.Data
metadataY <- object@metadataY
## 'metadataY' and 'mean' consistent
if (!identical(length(mean), as.integer(prod(dim(metadataY)))))
return(gettextf("'%s' and '%s' inconsistent",
"mean", "metadataY"))
TRUE
})
## Basic Models ##################################################################
## HAS_TESTS
## HAS_UPDATE
setClass("BinomialVarying",
contains = c("Model",
"Binomial",
"Varying"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 9L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
validity = function(object) {
theta <- object@theta
lower <- object@lower
upper <- object@upper
tolerance <- object@tolerance
struc.zero.array <- object@strucZeroArray
is.struc.zero <- struc.zero.array@.Data == 0L
## 'theta' greater than or equal to invlogit(lower)
if (any(theta[!is.struc.zero] < invlogit1(lower) - tolerance))
return(gettextf("'%s' has values that are less than '%s'",
"theta", "lower"))
## 'theta' less than or equal to invlogit(upper)
if (any(theta[!is.struc.zero] > invlogit1(upper) + tolerance))
return(gettextf("'%s' has values that are greater than '%s'",
"theta", "upper"))
TRUE
})
setClass("NormalVarying",
contains = c("VIRTUAL",
"Model",
"Normal",
"Varying",
"WNormalMixin"))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnown",
contains = c("NormalVarying",
"VarsigmaKnown"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 4L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
validity = function(object) {
varsigmaSetToZero <- object@varsigmaSetToZero@.Data
if (varsigmaSetToZero) {
## if varsigma is 0, lower, upper not specified
for (name in c("lower", "upper")) {
value <- methods::slot(object, name)
if (is.finite(value))
return(gettextf("'%s' is %d but '%s' is finite",
"varsigma", 0L, name))
}
}
TRUE
})
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknown",
contains = c("NormalVarying",
"VarsigmaUnknown"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 5L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostVarsigma = methods::new("Parameter", 0),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("PoissonVarying",
contains = c("VIRTUAL",
"Model",
"Poisson",
"Varying"),
validity = function(object) {
theta <- object@theta
lower <- object@lower
upper <- object@upper
tolerance <- object@tolerance
boxCoxParam <- object@boxCoxParam
if (boxCoxParam > 0) {
lower.back.tr <- (boxCoxParam * lower + 1) ^ (1 / boxCoxParam)
upper.back.tr <- (boxCoxParam * upper + 1) ^ (1 / boxCoxParam)
}
else {
lower.back.tr <- exp(lower)
upper.back.tr <- exp(upper)
}
## 'theta' greater than or equal to back-transformed 'lower'
if (any(theta[!is.na(theta)] < lower.back.tr - tolerance))
return(gettextf("'%s' has values that are less than '%s'",
"theta", "lower"))
## 'theta' less than or equal to back-transformed 'upper'
if (any(theta[!is.na(theta)] > upper.back.tr + tolerance))
return(gettextf("'%s' has values that are greater than '%s'",
"theta", "upper"))
TRUE
})
## NO_TESTS
setClass("CMPVarying",
contains = c("VIRTUAL",
"Model",
"CMP",
"Varying"),
validity = function(object) {
theta <- object@theta
lower <- object@lower
upper <- object@upper
tolerance <- object@tolerance
boxCoxParam <- object@boxCoxParam
if (boxCoxParam > 0) {
lower.back.tr <- (boxCoxParam * lower + 1) ^ (1 / boxCoxParam)
upper.back.tr <- (boxCoxParam * upper + 1) ^ (1 / boxCoxParam)
}
else {
lower.back.tr <- exp(lower)
upper.back.tr <- exp(upper)
}
## 'theta' greater than or equal to back-transformed 'lower'
if (any(theta[!is.na(theta)] < lower.back.tr - tolerance))
return(gettextf("'%s' has values that are less than '%s'",
"theta", "lower"))
## 'theta' less than or equal to back-transformed 'upper'
if (any(theta[!is.na(theta)] > upper.back.tr + tolerance))
return(gettextf("'%s' has values that are greater than '%s'",
"theta", "upper"))
TRUE
})
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 6L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("PoissonVarying",
"NotUseExposure"))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 10L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("PoissonVarying",
"UseExposure"))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonBinomialMixture",
contains = c("Model", "Prob", "UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 11L))
## HAS_TESTS
setClass("NormalFixedNotUseExp",
contains = c("NormalFixed",
"NotUseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 30L))
## HAS_TESTS
setClass("NormalFixedUseExp",
contains = c("NormalFixed",
"UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 31L))
## NO_TESTS
setClass("CMPVaryingNotUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nFailedPropYStar",
"nAcceptTheta",
"nuCMP",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 32L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("CMPVarying",
"NotUseExposure"))
## NO_TESTS
setClass("CMPVaryingUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nFailedPropYStar",
"nAcceptTheta",
"nuCMP",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 33L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("CMPVarying",
"UseExposure"))
## HAS_TESTS
setClass("Round3",
contains = c("Model",
"UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 34L))
## HAS_TESTS
setClass("Exact",
contains = c("Model",
"UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 38L))
## HAS_TESTS
setClass("TFixedNotUseExp",
contains = c("TFixed",
"NotUseExposure"),
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 35L))
## HAS_TESTS
setClass("TFixedUseExp",
contains = c("TFixed",
"UseExposure"),
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 36L))
## NO_TESTS
setClass("LN2",
slots = c(add1 = "LogicalFlag",
alphaLN2 = "ParameterVector",
constraintLN2 = "Values",
constraintAllLN2 = "Values",
nCellBeforeLN2 = "integer",
transformLN2 = "CollapseTransformExtra",
updateVarsigmaLN2 = "LogicalFlag",
varsigmaLN2HasHalfT = "LogicalFlag"),
contains = c("Model",
"ASigmaMixin",
"CellInLikMixin",
"MaxAttemptMixin",
"NuSigmaMixin",
"SigmaMaxMixin",
"SigmaMixin",
"StrucZeroArrayMixin",
"UseExposure",
"VarsigmaUnknown"),
prototype = prototype(slotsToExtract = c("alphaLN2",
"varsigma",
"sigma"),
iMethodModel = 37L,
nuVarsigma = methods::new("DegreesFreedom", 7),
nuSigma = methods::new("DegreesFreedom", 7),
logPostSigma = methods::new("Parameter", 0),
logPostVarsigma = methods::new("Parameter", 0)),
validity = function(object) {
alphaLN2 <- object@alphaLN2@.Data
constraintLN2 <- object@constraintLN2
constraintAllLN2 <- object@constraintAllLN2
nCellBeforeLN2 <- object@nCellBeforeLN2
transformLN2 <- object@transformLN2
## 'nCellBeforeLN2' not NA
if (any(is.na(nCellBeforeLN2))) {
return(gettextf("'%s' has missing values",
"nCellBeforeLN2"))
}
## values of 'nCellBeforeLN2' all non-negative
if (any(nCellBeforeLN2 < 0L))
return(gettextf("'%s' has negative values",
"nCellBeforeLN2"))
## 'constraintLN2', 'constraintAllLN2' consist
## entirely of NAs, -1s, 0s, and 1s
for (name in c("constraintLN2", "constraintAllLN2")) {
value <- methods::slot(object, name)
if (!all(value %in% c(NA, -1L, 0L, 1L)))
return(gettextf("'%s' has invalid values",
name))
}
## 'alphaLN2' and 'constraintLN2' have same length
if (!identical(length(alphaLN2), length(constraintLN2)))
return(gettextf("'%s' and '%s' have different lengths",
"alphaLN2", "constraintLN2"))
## length of 'constraintLN2' less than or equal to length of 'restructAllLN2'
if (length(constraintLN2) > length(constraintAllLN2))
return(gettextf("length of '%s' greater than length of '%s'",
"constraintLN2", "constraintAllLN2"))
## 'nCellBeforeLN2' has same length as 'alphaLN2'
if (!identical(length(nCellBeforeLN2), length(alphaLN2)))
return(gettextf("'%s' and '%s' have different lengths",
"nCellBeforeLN2", "alphaLN2"))
## 'alphaLN2' has length implied by 'transformLN2'
if (!identical(length(alphaLN2), as.integer(prod(transformLN2@dimAfter))))
return(gettextf("'%s' and '%s' incompatible",
"alphaLN2", "transformLN2"))
## 'alphaLN2' respects constraints in 'constraint'
for (j in seq_along(constraintLN2)) {
cj <- constraintLN2[j]
aj <- alphaLN2[j]
if (!is.na(cj)) {
if (((cj == -1L) && (aj > 0))
|| ((cj == 0L) && (aj != 0))
|| ((cj == 1L) && (aj < 0)))
return(gettextf("'%s' outside bounds specified in '%s'",
"alphaLN2", "constraintLN2"))
}
}
TRUE
})
## Models With Aggregate ##################################################################
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnownAgCertain",
contains = c("NormalVaryingVarsigmaKnown",
"AgCertain"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 12L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknownAgCertain",
contains = c("NormalVaryingVarsigmaUnknown",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 13L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostVarsigma = methods::new("Parameter", 0),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnownAgNormal",
contains = c("NormalVaryingVarsigmaKnown",
"AgNormal"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 14L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknownAgNormal",
contains = c("NormalVaryingVarsigmaUnknown",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 15L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostVarsigma = methods::new("Parameter", 0),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExpAgCertain",
contains = c("PoissonVaryingNotUseExp",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 16L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExpAgNormal",
contains = c("PoissonVaryingNotUseExp",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 17L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("BinomialVaryingAgCertain",
contains = c("BinomialVarying",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 18L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("BinomialVaryingAgNormal",
contains = c("BinomialVarying",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 19L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExpAgCertain",
contains = c("PoissonVaryingUseExp",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 20L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExpAgNormal",
contains = c("PoissonVaryingUseExp",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 21L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExpAgPoisson",
contains = c("PoissonVaryingNotUseExp",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 22L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExpAgPoisson",
contains = c("PoissonVaryingUseExp",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 23L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnownAgFun",
contains = c("NormalVaryingVarsigmaKnown",
"AgFun"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 24L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknownAgFun",
contains = c("NormalVaryingVarsigmaUnknown",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 25L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpAgFun",
contains = c("PoissonVaryingNotUseExp",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 26L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("BinomialVaryingAgFun",
contains = c("BinomialVarying",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 27L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("PoissonVaryingUseExpAgFun",
contains = c("PoissonVaryingUseExp",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 28L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## NO_TESTS
setClass("PoissonVaryingUseExpAgLife",
contains = c("PoissonVaryingUseExp",
"AgLife"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"mxAg"),
iMethodModel = 29L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## Predicted Models - Basic #############################################################
## iMethodModel for 'Predict' classes equals iMethodModel for
## base class plus 100
## HAS_TESTS
setClass("BinomialVaryingPredict",
prototype = prototype(iMethodModel = 109L),
contains = c("BinomialVarying", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("NormalVaryingVarsigmaKnownPredict",
prototype = prototype(iMethodModel = 104L),
contains = c("NormalVaryingVarsigmaKnown", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("NormalVaryingVarsigmaUnknownPredict",
prototype = prototype(iMethodModel = 105L),
contains = c("NormalVaryingVarsigmaUnknown", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsVarsigma", "OffsetsSigma"))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpPredict",
prototype = prototype(iMethodModel = 106L),
contains = c("PoissonVaryingNotUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("PoissonVaryingUseExpPredict",
prototype = prototype(iMethodModel = 110L),
contains = c("PoissonVaryingUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("PoissonBinomialMixturePredict",
prototype = prototype(iMethodModel = 111L),
contains = "PoissonBinomialMixture")
## HAS_TESTS
setClass("NormalFixedNotUseExpPredict",
prototype = prototype(iMethodModel = 130L),
contains = "NormalFixedNotUseExp")
## HAS_TESTS
setClass("NormalFixedUseExpPredict",
prototype = prototype(iMethodModel = 131L,
slotsToExtract = character()),
contains = "NormalFixedUseExp")
## NO_TESTS
setClass("CMPVaryingNotUseExpPredict",
prototype = prototype(iMethodModel = 132L),
contains = c("CMPVaryingNotUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## NO_TESTS
setClass("CMPVaryingUseExpPredict",
prototype = prototype(iMethodModel = 133L),
contains = c("CMPVaryingUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("Round3Predict",
contains = "Round3",
prototype = prototype(slotsToExtract = character(),
iMethodModel = 134L))
## HAS_TESTS
setClass("ExactPredict",
contains = "Exact",
prototype = prototype(slotsToExtract = character(),
iMethodModel = 138L))
## HAS_TESTS
setClass("TFixedNotUseExpPredict",
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 135L),
contains = "TFixedNotUseExp")
## HAS_TESTS
setClass("TFixedUseExpPredict",
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 136L),
contains = "TFixedUseExp")
## NO_TESTS
setClass("LN2Predict",
prototype = prototype(iMethodModel = 137L),
contains = c("LN2",
"OffsetsAlphaLN2",
"OffsetsVarsigma",
"OffsetsSigma"))
## Predicted Models - Aggregate #############################################################
## MIGHT NEED TO DELETE THESE CLASSES
## HAS_TESTS
setClass("BinomialVaryingPredictAgCertain",
contains = c("BinomialVaryingPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 118L))
## HAS_TESTS
setClass("BinomialVaryingPredictAgNormal",
contains = c("BinomialVaryingPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 119L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaKnownPredictAgCertain",
contains = c("NormalVaryingVarsigmaKnownPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 112L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaUnknownPredictAgCertain",
contains = c("NormalVaryingVarsigmaUnknownPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 113L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaKnownPredictAgNormal",
contains = c("NormalVaryingVarsigmaKnownPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 114L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaUnknownPredictAgNormal",
contains = c("NormalVaryingVarsigmaUnknownPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 115L))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpPredictAgCertain",
contains = c("PoissonVaryingNotUseExpPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 116L))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpPredictAgNormal",
contains = c("PoissonVaryingNotUseExpPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 117L))
## HAS_TESTS
setClass("PoissonVaryingUseExpPredictAgCertain",
contains = c("PoissonVaryingUseExpPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 120L))
## HAS_TESTS
setClass("PoissonVaryingUseExpPredictAgNormal",
contains = c("PoissonVaryingUseExpPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 121L))
## NO_TESTS
setClass("PoissonVaryingNotUseExpPredictAgPoisson",
contains = c("PoissonVaryingNotUseExpPredict",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 122L))
## NO_TESTS
setClass("PoissonVaryingUseExpPredictAgPoisson",
contains = c("PoissonVaryingUseExpPredict",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 123L))
## NO_TESTS
setClass("PoissonVaryingUseExpPredictAgLife",
contains = c("PoissonVaryingUseExp",
"AgLife"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"mxAg"),
iMethodModel = 129L))
StatisticsNZ/demest documentation built on Nov. 2, 2023, 7:56 p.m.
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## Virtual classes ###########################################################
setClass("NotUseExposure",
contains = "VIRTUAL")
setClass("UseExposure",
contains = "VIRTUAL")
setClass("Model",
slots = c(call = "call"),
contains = c("VIRTUAL",
"MetadataY",
"SlotsToExtract",
"IMethodModel"))
## HAS_TESTS
setClass("Binomial",
contains = c("VIRTUAL",
"UseExposure",
"ScaleThetaMultiplierMixin",
"StrucZeroArrayMixin"),
validity = function(object) {
theta <- object@theta
struc.zero.array <- object@strucZeroArray
is.struc.zero <- struc.zero.array@.Data == 0L
## 'theta' is non-negative
if (any(theta[!is.struc.zero] < 0))
return(gettextf("'%s' has negative values", "theta"))
## 'theta' is less than or equal to 1
if (any(theta[!is.struc.zero] > 1))
return(gettextf("'%s' has values greater than %d",
"theta", 1L))
TRUE
})
## HAS_TESTS
setClass("Normal",
contains = c("VIRTUAL",
"NotUseExposure"))
## HAS_TESTS
setClass("Poisson",
contains = c("VIRTUAL",
"BoxCoxParamMixin",
"ScaleThetaMultiplierMixin",
"StrucZeroArrayMixin"),
validity = function(object) {
theta <- object@theta
## 'theta' is non-negative
if (any(theta[!is.na(theta)] < 0))
return(gettextf("'%s' has negative values", "theta"))
TRUE
})
## NO_TESTS
setClass("CMP",
contains = c("VIRTUAL",
"BoxCoxParamMixin",
"SDLogNuCMPMixin",
"MeanLogNuCMPMixin",
"NuCMPMixin",
"ScaleThetaMultiplierMixin",
"NFailedPropYStarMixin",
"StrucZeroArrayMixin"),
validity = function(object) {
theta <- object@theta
## 'theta' is non-negative
if (any(theta[!is.na(theta)] < 0))
return(gettextf("'%s' has negative values", "theta"))
TRUE
})
## HAS_TESTS
setClass("NormalFixed",
contains = c("Model", "MeanSDMixin", "MeanSDMetadataAllMixin"),
validity = function(object) {
mean <- object@mean@.Data
metadataY <- object@metadataY
## 'metadataY' and 'mean' consistent
if (!identical(length(mean), as.integer(prod(dim(metadataY)))))
return(gettextf("'%s' and '%s' inconsistent",
"mean", "metadataY"))
TRUE
})
## NO_TESTS
setClass("TFixed",
contains = c("VIRTUAL",
"Model",
"MeanSDMixin",
"MeanSDMetadataAllMixin",
"NuMixin"),
validity = function(object) {
mean <- object@mean@.Data
metadataY <- object@metadataY
## 'metadataY' and 'mean' consistent
if (!identical(length(mean), as.integer(prod(dim(metadataY)))))
return(gettextf("'%s' and '%s' inconsistent",
"mean", "metadataY"))
TRUE
})
## Basic Models ##################################################################
## HAS_TESTS
## HAS_UPDATE
setClass("BinomialVarying",
contains = c("Model",
"Binomial",
"Varying"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 9L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
validity = function(object) {
theta <- object@theta
lower <- object@lower
upper <- object@upper
tolerance <- object@tolerance
struc.zero.array <- object@strucZeroArray
is.struc.zero <- struc.zero.array@.Data == 0L
## 'theta' greater than or equal to invlogit(lower)
if (any(theta[!is.struc.zero] < invlogit1(lower) - tolerance))
return(gettextf("'%s' has values that are less than '%s'",
"theta", "lower"))
## 'theta' less than or equal to invlogit(upper)
if (any(theta[!is.struc.zero] > invlogit1(upper) + tolerance))
return(gettextf("'%s' has values that are greater than '%s'",
"theta", "upper"))
TRUE
})
setClass("NormalVarying",
contains = c("VIRTUAL",
"Model",
"Normal",
"Varying",
"WNormalMixin"))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnown",
contains = c("NormalVarying",
"VarsigmaKnown"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 4L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
validity = function(object) {
varsigmaSetToZero <- object@varsigmaSetToZero@.Data
if (varsigmaSetToZero) {
## if varsigma is 0, lower, upper not specified
for (name in c("lower", "upper")) {
value <- methods::slot(object, name)
if (is.finite(value))
return(gettextf("'%s' is %d but '%s' is finite",
"varsigma", 0L, name))
}
}
TRUE
})
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknown",
contains = c("NormalVarying",
"VarsigmaUnknown"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 5L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostVarsigma = methods::new("Parameter", 0),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("PoissonVarying",
contains = c("VIRTUAL",
"Model",
"Poisson",
"Varying"),
validity = function(object) {
theta <- object@theta
lower <- object@lower
upper <- object@upper
tolerance <- object@tolerance
boxCoxParam <- object@boxCoxParam
if (boxCoxParam > 0) {
lower.back.tr <- (boxCoxParam * lower + 1) ^ (1 / boxCoxParam)
upper.back.tr <- (boxCoxParam * upper + 1) ^ (1 / boxCoxParam)
}
else {
lower.back.tr <- exp(lower)
upper.back.tr <- exp(upper)
}
## 'theta' greater than or equal to back-transformed 'lower'
if (any(theta[!is.na(theta)] < lower.back.tr - tolerance))
return(gettextf("'%s' has values that are less than '%s'",
"theta", "lower"))
## 'theta' less than or equal to back-transformed 'upper'
if (any(theta[!is.na(theta)] > upper.back.tr + tolerance))
return(gettextf("'%s' has values that are greater than '%s'",
"theta", "upper"))
TRUE
})
## NO_TESTS
setClass("CMPVarying",
contains = c("VIRTUAL",
"Model",
"CMP",
"Varying"),
validity = function(object) {
theta <- object@theta
lower <- object@lower
upper <- object@upper
tolerance <- object@tolerance
boxCoxParam <- object@boxCoxParam
if (boxCoxParam > 0) {
lower.back.tr <- (boxCoxParam * lower + 1) ^ (1 / boxCoxParam)
upper.back.tr <- (boxCoxParam * upper + 1) ^ (1 / boxCoxParam)
}
else {
lower.back.tr <- exp(lower)
upper.back.tr <- exp(upper)
}
## 'theta' greater than or equal to back-transformed 'lower'
if (any(theta[!is.na(theta)] < lower.back.tr - tolerance))
return(gettextf("'%s' has values that are less than '%s'",
"theta", "lower"))
## 'theta' less than or equal to back-transformed 'upper'
if (any(theta[!is.na(theta)] > upper.back.tr + tolerance))
return(gettextf("'%s' has values that are greater than '%s'",
"theta", "upper"))
TRUE
})
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 6L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("PoissonVarying",
"NotUseExposure"))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 10L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("PoissonVarying",
"UseExposure"))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonBinomialMixture",
contains = c("Model", "Prob", "UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 11L))
## HAS_TESTS
setClass("NormalFixedNotUseExp",
contains = c("NormalFixed",
"NotUseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 30L))
## HAS_TESTS
setClass("NormalFixedUseExp",
contains = c("NormalFixed",
"UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 31L))
## NO_TESTS
setClass("CMPVaryingNotUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nFailedPropYStar",
"nAcceptTheta",
"nuCMP",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 32L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("CMPVarying",
"NotUseExposure"))
## NO_TESTS
setClass("CMPVaryingUseExp",
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nFailedPropYStar",
"nAcceptTheta",
"nuCMP",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 33L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)),
contains = c("CMPVarying",
"UseExposure"))
## HAS_TESTS
setClass("Round3",
contains = c("Model",
"UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 34L))
## HAS_TESTS
setClass("Exact",
contains = c("Model",
"UseExposure"),
prototype = prototype(slotsToExtract = character(),
iMethodModel = 38L))
## HAS_TESTS
setClass("TFixedNotUseExp",
contains = c("TFixed",
"NotUseExposure"),
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 35L))
## HAS_TESTS
setClass("TFixedUseExp",
contains = c("TFixed",
"UseExposure"),
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 36L))
## NO_TESTS
setClass("LN2",
slots = c(add1 = "LogicalFlag",
alphaLN2 = "ParameterVector",
constraintLN2 = "Values",
constraintAllLN2 = "Values",
nCellBeforeLN2 = "integer",
transformLN2 = "CollapseTransformExtra",
updateVarsigmaLN2 = "LogicalFlag",
varsigmaLN2HasHalfT = "LogicalFlag"),
contains = c("Model",
"ASigmaMixin",
"CellInLikMixin",
"MaxAttemptMixin",
"NuSigmaMixin",
"SigmaMaxMixin",
"SigmaMixin",
"StrucZeroArrayMixin",
"UseExposure",
"VarsigmaUnknown"),
prototype = prototype(slotsToExtract = c("alphaLN2",
"varsigma",
"sigma"),
iMethodModel = 37L,
nuVarsigma = methods::new("DegreesFreedom", 7),
nuSigma = methods::new("DegreesFreedom", 7),
logPostSigma = methods::new("Parameter", 0),
logPostVarsigma = methods::new("Parameter", 0)),
validity = function(object) {
alphaLN2 <- object@alphaLN2@.Data
constraintLN2 <- object@constraintLN2
constraintAllLN2 <- object@constraintAllLN2
nCellBeforeLN2 <- object@nCellBeforeLN2
transformLN2 <- object@transformLN2
## 'nCellBeforeLN2' not NA
if (any(is.na(nCellBeforeLN2))) {
return(gettextf("'%s' has missing values",
"nCellBeforeLN2"))
}
## values of 'nCellBeforeLN2' all non-negative
if (any(nCellBeforeLN2 < 0L))
return(gettextf("'%s' has negative values",
"nCellBeforeLN2"))
## 'constraintLN2', 'constraintAllLN2' consist
## entirely of NAs, -1s, 0s, and 1s
for (name in c("constraintLN2", "constraintAllLN2")) {
value <- methods::slot(object, name)
if (!all(value %in% c(NA, -1L, 0L, 1L)))
return(gettextf("'%s' has invalid values",
name))
}
## 'alphaLN2' and 'constraintLN2' have same length
if (!identical(length(alphaLN2), length(constraintLN2)))
return(gettextf("'%s' and '%s' have different lengths",
"alphaLN2", "constraintLN2"))
## length of 'constraintLN2' less than or equal to length of 'restructAllLN2'
if (length(constraintLN2) > length(constraintAllLN2))
return(gettextf("length of '%s' greater than length of '%s'",
"constraintLN2", "constraintAllLN2"))
## 'nCellBeforeLN2' has same length as 'alphaLN2'
if (!identical(length(nCellBeforeLN2), length(alphaLN2)))
return(gettextf("'%s' and '%s' have different lengths",
"nCellBeforeLN2", "alphaLN2"))
## 'alphaLN2' has length implied by 'transformLN2'
if (!identical(length(alphaLN2), as.integer(prod(transformLN2@dimAfter))))
return(gettextf("'%s' and '%s' incompatible",
"alphaLN2", "transformLN2"))
## 'alphaLN2' respects constraints in 'constraint'
for (j in seq_along(constraintLN2)) {
cj <- constraintLN2[j]
aj <- alphaLN2[j]
if (!is.na(cj)) {
if (((cj == -1L) && (aj > 0))
|| ((cj == 0L) && (aj != 0))
|| ((cj == 1L) && (aj < 0)))
return(gettextf("'%s' outside bounds specified in '%s'",
"alphaLN2", "constraintLN2"))
}
}
TRUE
})
## Models With Aggregate ##################################################################
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnownAgCertain",
contains = c("NormalVaryingVarsigmaKnown",
"AgCertain"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 12L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknownAgCertain",
contains = c("NormalVaryingVarsigmaUnknown",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 13L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostVarsigma = methods::new("Parameter", 0),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnownAgNormal",
contains = c("NormalVaryingVarsigmaKnown",
"AgNormal"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 14L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknownAgNormal",
contains = c("NormalVaryingVarsigmaUnknown",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 15L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostVarsigma = methods::new("Parameter", 0),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExpAgCertain",
contains = c("PoissonVaryingNotUseExp",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 16L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExpAgNormal",
contains = c("PoissonVaryingNotUseExp",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 17L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("BinomialVaryingAgCertain",
contains = c("BinomialVarying",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 18L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("BinomialVaryingAgNormal",
contains = c("BinomialVarying",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 19L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExpAgCertain",
contains = c("PoissonVaryingUseExp",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 20L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExpAgNormal",
contains = c("PoissonVaryingUseExp",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 21L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingNotUseExpAgPoisson",
contains = c("PoissonVaryingNotUseExp",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 22L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("PoissonVaryingUseExpAgPoisson",
contains = c("PoissonVaryingUseExp",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 23L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaKnownAgFun",
contains = c("NormalVaryingVarsigmaKnown",
"AgFun"),
prototype = prototype(varsigmaSetToZero = methods::new("LogicalFlag", FALSE),
slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 24L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
## HAS_UPDATE
setClass("NormalVaryingVarsigmaUnknownAgFun",
contains = c("NormalVaryingVarsigmaUnknown",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 25L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpAgFun",
contains = c("PoissonVaryingNotUseExp",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 26L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("BinomialVaryingAgFun",
contains = c("BinomialVarying",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 27L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## HAS_TESTS
setClass("PoissonVaryingUseExpAgFun",
contains = c("PoissonVaryingUseExp",
"AgFun"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg"),
iMethodModel = 28L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## NO_TESTS
setClass("PoissonVaryingUseExpAgLife",
contains = c("PoissonVaryingUseExp",
"AgLife"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"mxAg"),
iMethodModel = 29L,
nuSigma = methods::new("DegreesFreedom", 7),
logPostTheta = methods::new("Parameter", 0),
updateTheta = methods::new("LogicalFlag", TRUE),
logPostSigma = methods::new("Parameter", 0),
logPostBetas = methods::new("Parameter", 0),
logPostPriorsBetas = methods::new("Parameter", 0)))
## Predicted Models - Basic #############################################################
## iMethodModel for 'Predict' classes equals iMethodModel for
## base class plus 100
## HAS_TESTS
setClass("BinomialVaryingPredict",
prototype = prototype(iMethodModel = 109L),
contains = c("BinomialVarying", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("NormalVaryingVarsigmaKnownPredict",
prototype = prototype(iMethodModel = 104L),
contains = c("NormalVaryingVarsigmaKnown", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("NormalVaryingVarsigmaUnknownPredict",
prototype = prototype(iMethodModel = 105L),
contains = c("NormalVaryingVarsigmaUnknown", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsVarsigma", "OffsetsSigma"))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpPredict",
prototype = prototype(iMethodModel = 106L),
contains = c("PoissonVaryingNotUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("PoissonVaryingUseExpPredict",
prototype = prototype(iMethodModel = 110L),
contains = c("PoissonVaryingUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("PoissonBinomialMixturePredict",
prototype = prototype(iMethodModel = 111L),
contains = "PoissonBinomialMixture")
## HAS_TESTS
setClass("NormalFixedNotUseExpPredict",
prototype = prototype(iMethodModel = 130L),
contains = "NormalFixedNotUseExp")
## HAS_TESTS
setClass("NormalFixedUseExpPredict",
prototype = prototype(iMethodModel = 131L,
slotsToExtract = character()),
contains = "NormalFixedUseExp")
## NO_TESTS
setClass("CMPVaryingNotUseExpPredict",
prototype = prototype(iMethodModel = 132L),
contains = c("CMPVaryingNotUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## NO_TESTS
setClass("CMPVaryingUseExpPredict",
prototype = prototype(iMethodModel = 133L),
contains = c("CMPVaryingUseExp", "BetaIsPredicted", "OffsetsBetas",
"OffsetsPriorsBetas", "OffsetsSigma"))
## HAS_TESTS
setClass("Round3Predict",
contains = "Round3",
prototype = prototype(slotsToExtract = character(),
iMethodModel = 134L))
## HAS_TESTS
setClass("ExactPredict",
contains = "Exact",
prototype = prototype(slotsToExtract = character(),
iMethodModel = 138L))
## HAS_TESTS
setClass("TFixedNotUseExpPredict",
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 135L),
contains = "TFixedNotUseExp")
## HAS_TESTS
setClass("TFixedUseExpPredict",
prototype = prototype(nu = methods::new("DegreesFreedom", 7),
slotsToExtract = character(),
iMethodModel = 136L),
contains = "TFixedUseExp")
## NO_TESTS
setClass("LN2Predict",
prototype = prototype(iMethodModel = 137L),
contains = c("LN2",
"OffsetsAlphaLN2",
"OffsetsVarsigma",
"OffsetsSigma"))
## Predicted Models - Aggregate #############################################################
## MIGHT NEED TO DELETE THESE CLASSES
## HAS_TESTS
setClass("BinomialVaryingPredictAgCertain",
contains = c("BinomialVaryingPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 118L))
## HAS_TESTS
setClass("BinomialVaryingPredictAgNormal",
contains = c("BinomialVaryingPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 119L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaKnownPredictAgCertain",
contains = c("NormalVaryingVarsigmaKnownPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 112L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaUnknownPredictAgCertain",
contains = c("NormalVaryingVarsigmaUnknownPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 113L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaKnownPredictAgNormal",
contains = c("NormalVaryingVarsigmaKnownPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 114L))
## HAS_TESTS
setClass("NormalVaryingVarsigmaUnknownPredictAgNormal",
contains = c("NormalVaryingVarsigmaUnknownPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"varsigma",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 115L))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpPredictAgCertain",
contains = c("PoissonVaryingNotUseExpPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 116L))
## HAS_TESTS
setClass("PoissonVaryingNotUseExpPredictAgNormal",
contains = c("PoissonVaryingNotUseExpPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 117L))
## HAS_TESTS
setClass("PoissonVaryingUseExpPredictAgCertain",
contains = c("PoissonVaryingUseExpPredict",
"AgCertain"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas"),
iMethodModel = 120L))
## HAS_TESTS
setClass("PoissonVaryingUseExpPredictAgNormal",
contains = c("PoissonVaryingUseExpPredict",
"AgNormal"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 121L))
## NO_TESTS
setClass("PoissonVaryingNotUseExpPredictAgPoisson",
contains = c("PoissonVaryingNotUseExpPredict",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 122L))
## NO_TESTS
setClass("PoissonVaryingUseExpPredictAgPoisson",
contains = c("PoissonVaryingUseExpPredict",
"AgPoisson"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"nFailedPropValueAg",
"nAcceptAg"),
iMethodModel = 123L))
## NO_TESTS
setClass("PoissonVaryingUseExpPredictAgLife",
contains = c("PoissonVaryingUseExp",
"AgLife"),
prototype = prototype(slotsToExtract = c("theta",
"nFailedPropTheta",
"nAcceptTheta",
"betas",
"sigma",
"priorsBetas",
"valueAg",
"mxAg"),
iMethodModel = 129L))
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