Nothing
setMethod("coef",
signature(object = "IdtSNDE"),
function(object,selmodel=BestModel(object),ParType=c("Centr","Direct","All"),...)
{
ParType <- match.arg(ParType)
if (ParType=="Centr") {
return( list(mu=object@CovConfCases[[selmodel]]$muE,Sigma=object@CovConfCases[[selmodel]]$SigmaE,
gamma1=object@CovConfCases[[selmodel]]$gamma1E) )
} else if (ParType=="Direct") {
return( list(ksi=object@CovConfCases[[selmodel]]$ksiE,Omega=object@CovConfCases[[selmodel]]$OmegaE,
alpha=object@CovConfCases[[selmodel]]$alphaE) )
} else if (ParType=="All") {
return( list(mu=object@CovConfCases[[selmodel]]$muE,Sigma=object@CovConfCases[[selmodel]]$SigmaE,
gamma1=object@CovConfCases[[selmodel]]$gamma1E,ksi=object@CovConfCases[[selmodel]]$ksiE,
Omega=object@CovConfCases[[selmodel]]$OmegaE,alpha=object@CovConfCases[[selmodel]]$alphaE) )
}
}
)
setMethod("stdEr",
signature(x = "IdtSNDE"),
function(x,selmodel=BestModel(x),...)
{
modres <- x@CovConfCases[[selmodel]]
if (modres$status!="Regular")
{
if (modres$status=="Invalid") {
warning("Standard errors were not computed for model ",x@ModelNames[selmodel],"\n",
"because the computation of the observed Information Matrix was not successful.\n")
# } else if (modres$status=="SingInf") {
# warning("Standard errors were not computed for model ",x@ModelNames[selmodel],"\n",
# "because the mle estimates were too close to the singularity point where all gamma1 equal 0.\n")
# } else if (modres$status=="InstInf") {
# warning("Standard errors were not computed for model ",x@ModelNames[selmodel],"\n",
# "because the mle estimates resulted in a numerically unstable information matrix.\n")
} else if (modres$status=="Onborder") {
warning("Standard errors were not computed for model ",x@ModelNames[selmodel]," because the mle estimates are too close \n",
"to the frontier of the parameter space, where classical maximum likeklihood theory does not apply.\n")
# } else if (modres$status=="PositiveScore") {
# warning("Standard errors were not computed for model ",x@ModelNames[selmodel]," because at the current estimate the score\n",
# "function (i.e., the derivative of the log-likelihhod) is stricty positive\n")
# } else if (modres$status=="SingOmega") {
# warning("Standard errors were not computed for model ",x@ModelNames[selmodel],"\n",
# "because the mle estimates resulted in a singular Omega matrix.\n")
}
return(NULL)
}
list(mu=modres$muEse,Sigma=modres$SigmaEse,gamma1=modres$gamma1Ese)
}
)
setMethod("vcov",
signature(object = "IdtSNDE"),
function(object,selmodel=BestModel(object),...)
{
modres <- object@CovConfCases[[selmodel]]
if (modres$status!="Regular")
{
if (modres$status=="Invalid") {
warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],"\n",
"because the computation of the observed Information Matrix was not successful.\n")
# } else if (modres$status=="SingInf") {
# warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],"\n",
# "because the mle estimates were too close to the singularity point where all gamma1 equal 0.\n")
# } else if (modres$status=="InstInf") {
# warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],"\n",
# "because the mle estimates resulted in a numerically unstable information matrix.\n")
} else if (modres$status=="Onborder") {
warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],
" because the mle estimates are too close\nto the frontier of the parameter space, where",
" classical maximum likeklihood theory does not apply.\n")
} else if (modres$status=="PositiveScore") {
warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],
" because at the current estimate the score\n",
"function (i.e., the derivative of the log-likelihhod) is stricty positive\n")
} else if (modres$status=="SingOmega") {
warning("Standard errors were not computed for model ",object@ModelNames[selmodel],"\n",
"because the mle estimates resulted in a singular Omega matrix.\n")
}
return(NULL)
}
object@CovConfCases[[selmodel]]$mleCPvcov
}
)
setMethod("vcov",
signature(object = "IdtMxSNDE"),
function(object,selmodel=BestModel(object),group=NULL,...)
{
modres <- object@CovConfCases[[selmodel]]
if (modres$status!="Regular")
{
if (modres$status=="Invalid") {
warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],"\n",
"because the computation of the observed Information Matrix was not successful.\n")
# } else if (modres$status=="SingInf") {
# warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],"\n",
# "because the mle estimates were too close to the singularity point where all gamma1 equal 0.\n")
# } else if (modres$status=="InstInf") {
# warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],"\n",
# "because the mle estimates resulted in a numerically unstable information matrix.\n")
} else if (modres$status=="Onborder") {
warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],
" because the mle estimates are too close\nto the frontier of the parameter space, where",
" classical maximum likeklihood theory does not apply.\n")
} else if (modres$status=="PositiveScore") {
warning("The asymptotic covariance matrix was not computed for model ",object@ModelNames[selmodel],
" because at the current estimate the score\n",
"function (i.e., the derivative of the log-likelihhod) is stricty positive\n")
} else if (modres$status=="SingOmega") {
warning("Standard errors were not computed for model ",object@ModelNames[selmodel],"\n",
"because the mle estimates resulted in a singular Omega matrix.\n")
}
return(NULL)
}
if (object@Hmcdt) {
object@CovConfCases[[selmodel]]$mleCPvcov
} else {
if (is.null(group))
{
warning(paste("vcov returned a three-dimensional array with a different var-cov matrix for each group,\n",
"which was identified by the level of the third array dimension\n"))
object@CovConfCases[[selmodel]]$mleCPvcov
} else {
object@CovConfCases[[selmodel]]$mleCPvcov[[,,group]]
}
}
}
)
setMethod("mean", signature(x = "IdtSNDE"), function(x) { coef(x)$mu } )
setMethod("var", signature(x ="IdtSNDE"), function(x) { coef(x)$Sigma } )
setMethod("cor",
signature(x ="IdtSNDE"),
function(x)
{
Sig <- coef(x)$Sigma
if (length(dim(Sig))==2) {
return(cov2cor(Sig))
} else if (length(dim(Sig))==3) {
return(array(apply(Sig,3,cov2cor),dim=dim(Sig),dimnames=dimnames(Sig)))
}
}
)
setMethod("coef",
signature(object = "IdtNandSNDE"),
function(object,selmodel=BestModel(object),ParType=c("Centr","Direct","All"),...)
{
ParType <- match.arg(ParType)
if (object@ModelType[selmodel]=="Normal") {
return(coef(object@NMod))
} else if (object@ModelType[selmodel]=="SkewNormal") {
return(coef(object@SNMod,ParType=ParType))
}
}
)
setMethod("stdEr",
signature(x = "IdtNandSNDE"),
function(x,selmodel=BestModel(x),...)
{
if (x@ModelType[selmodel]=="Normal") {
stdEr(x@NMod,selmodel=selmodel)
} else if (x@ModelType[selmodel]=="SkewNormal") {
stdEr(x@SNMod,selmodel=selmodel-length(x@NMod@ModelConfig))
}
}
)
setMethod("vcov",
signature(object = "IdtNandSNDE"),
function(object,selmodel=BestModel(object),...)
{
if (object@ModelType[selmodel]=="Normal") {
return(vcov(object@NMod,selmodel=selmodel))
} else if (object@ModelType[selmodel]=="SkewNormal") {
return(vcov(object@SNMod,selmodel=selmodel-length(object@NMod@ModelConfig)))
}
}
)
setMethod("mean", signature(x = "IdtNandSNDE"), function(x) { coef(x)$mu } )
setMethod("var", signature(x ="IdtNandSNDE"), function(x) { coef(x)$Sigma } )
setMethod("cor",
signature(x ="IdtNandSNDE"),
function(x)
{
Sig <- coef(x)$Sigma
if (length(dim(Sig))==2) {
return(cov2cor(Sig))
} else if (length(dim(Sig))==3) {
return(array(apply(Sig,3,cov2cor),dim=dim(Sig),dimnames=dimnames(Sig)))
}
}
)
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