Nothing
R/110a_gev.R
In fitdistcp: Distribution Fitting with Calibrating Priors for Commonly Used Distributions
Defines functions
tgev_cp
pgev_cp
dgev_cp
rgev_cp
qgev_cp
Documented in
dgev_cp
pgev_cp
qgev_cp
rgev_cp
tgev_cp
#' Generalized Extreme Value Distribution, Predictions Based on a Calibrating Prior
#'
#' @inherit man description author references seealso return
#' @inheritParams man
#'
#' @inheritSection man Optional Return Values
#' @inheritSection man Optional Return Values (EVT models only)
#' @inheritSection man Optional Return Values (some EVT models only)
# #' @inheritSection man Details (homogeneous models)
#' @inheritSection man Details (non-homogeneous models)
# #' @inheritSection man Details (analytic integration)
#' @inheritSection man Details (DMGS integration)
#' @inheritSection man Details (RUST)
#'
#' @section Details of the Model:
#' The GEV distribution has distribution function
#' \deqn{F(x;\mu,\sigma,\xi)=\exp{(-t(x;\mu,\sigma,\xi))}}
#' where
#' \deqn{t(x;\mu,\sigma,\xi) =
#' \begin{cases}
#' {\left[1+\xi\left(\frac{x-\mu}{\sigma}\right)\right]}^{-1/\xi} & \text{if $\xi \ne 0$}\\
#' \exp{\left(-\frac{x-\mu}{\sigma}\right)} & \text{if $\xi=0$}
#' \end{cases}}
#' where
#' \eqn{x} is the random variable and
#' \eqn{\mu,\sigma>0,\xi} are the parameters.
#'
#' The calibrating prior we use is given by
#' \deqn{\pi(\mu,\sigma,\xi) \propto \frac{1}{\sigma}}
#' as given in Jewson et al. (2025).
#'
#' The code will stop with an error if the
#' input data gives a maximum likelihood
#' value for the shape parameter that lies outside the range \code{(minxi,maxxi)},
#' since outside this range there may be numerical problems.
#' Such values seldom occur
#' in real observed data for maxima.
#'
#' @example man/examples/example_110_gev.R
#'
#' @name gev_cp
NULL
#' @rdname gev_cp
#' @inheritParams man
#' @export
#'
qgev_cp=function(x,p=seq(0.1,0.9,0.1),ics=c(0,0,0),
d1=0.01,fd2=0.01,d3=0.01,fdalpha=0.01,
minxi=-1,maxxi=999,
means=FALSE,waicscores=FALSE,extramodels=FALSE,
pdf=FALSE,customprior=0,
dmgs=TRUE,rust=FALSE,nrust=100000,
pwm=FALSE,debug=FALSE,aderivs=TRUE){
#
# 1 intro
#
stopifnot(is.finite(x),!is.na(x),is.finite(p),!is.na(p),p>0,p<1,length(ics)==3,fdalpha<1)
alpha=1-p
nx=length(x)
nalpha=length(alpha)
if(pdf){
dalpha=pmin(fdalpha*alpha,fdalpha*(1-alpha))
alpham=alpha-dalpha
alphap=alpha+dalpha
}
#
# 2 ml param estimate
#
ics=gev_setics(x,ics)
opt1=optim(ics,gev_loglik,x=x,control=list(fnscale=-1))
v1hat=opt1$par[1]
v2hat=opt1$par[2]
v3hat=opt1$par[3]
ml_params=c(v1hat,v2hat,v3hat)
# gev_checkmle(ml_params,minxi,maxxi)
if(debug)message(" v1hat,v2hat,v3hat=",v1hat,v2hat,v3hat,"")
pw_params="pwm not selected"
if(pwm)pw_params=gev_pwm_params(x)
if(abs(v3hat)>=1){revert2ml=TRUE}else{revert2ml=FALSE}
# 3 aic
#
ml_value=opt1$val
maic=make_maic(ml_value,nparams=3)
#
# 4 calc ml quantiles and densities (vectorized over alpha)
#
ml_quantiles=qgev((1-alpha),mu=v1hat,sigma=v2hat,xi=v3hat)
if(v3hat<0){
ml_max=v1hat-v2hat/v3hat
} else {
ml_max=Inf
}
fhat=dgev(ml_quantiles,mu=v1hat,sigma=v2hat,xi=v3hat)
pw_quantiles="pwm not selected"
if(pwm)pw_quantiles=qgev((1-alpha),mu=pw_params[1],sigma=pw_params[2],xi=pw_params[3])
#
# dmgs
#
standard_errors="dmgs not selected"
flat_quantiles="dmgs not selected"
rh_ml_quantiles="dmgs not selected"
rh_flat_quantiles="dmgs not selected"
cp_quantiles="dmgs not selected"
jp_quantiles="dmgs not selected"
ru_quantiles="dmgs not selected"
custom_quantiles="dmgs not selected"
ml_pdf="dmgs not selected"
rh_flat_pdf="dmgs not selected"
cp_pdf="dmgs not selected"
waic1="dmgs not selected"
waic2="dmgs not selected"
ml_mean="dmgs not selected"
flat_mean="dmgs not selected"
rh_mean="dmgs not selected"
rh_flat_mean="dmgs not selected"
rh_ml_mean="dmgs not selected"
cp_mean="dmgs not selected"
cp_method="dmgs not selected"
jp_mean="dmgs not selected"
custom_mean="dmgs not selected"
#
# 5 alpha pdf stuff
#
if((dmgs)&&(!revert2ml)){
if(debug)message(" ml_quantiles=",ml_quantiles,"")
if(pdf){
ml_quantilesm=qgev((1-alpham),mu=v1hat,sigma=v2hat,xi=v3hat)
ml_quantilesp=qgev((1-alphap),mu=v1hat,sigma=v2hat,xi=v3hat)
fhatm=dgev(ml_quantilesm,mu=v1hat,sigma=v2hat,xi=v3hat)
fhatp=dgev(ml_quantilesp,mu=v1hat,sigma=v2hat,xi=v3hat)
}
#
# 6 expected information matrix and related (for Jeffreys prior)
#
if(debug)message(" call gev.infomat")
if(extramodels|means){
gg=gev.infomat(c(v1hat,v2hat,v3hat),dat=c(1),method=c("exp")) #faster than num (seems to fail when v3hat=0.4 though)
ggi=solve(gg)
detg=det(gg)
ggd=gev_ggd_mev(v1hat,d1,v2hat,fd2,v3hat,d3) #is faster than num
}
#
# 7 ldd (two versions)
#
if(debug)message(" calculate ldd")
if(aderivs) ldd=gev_ldda(x,v1hat,v2hat,v3hat)
if(!aderivs)ldd=gev_ldd(x,v1hat,d1,v2hat,fd2,v3hat,d3)
lddi=solve(ldd)
standard_errors=make_se(nx,lddi)
if(extramodels|means){
if(aderivs) ldd_k3=gev_k3_ldda(x,v1hat,v2hat,v3hat)
if(!aderivs)ldd_k3=gev_k3_ldd(x,v1hat,d1,v2hat,fd2,v3hat)
lddi_k3=solve(ldd_k3)
}
#
# 8 lddd (two versions)
#
if(debug)message(" calculate lddd")
if(aderivs) lddd=gev_lddda(x,v1hat,v2hat,v3hat)
if(!aderivs)lddd=gev_lddd(x,v1hat,d1,v2hat,fd2,v3hat,d3)
if(extramodels|means){
if(aderivs) lddd_k3=gev_k3_lddda(x,v1hat,v2hat,v3hat)
if(!aderivs)lddd_k3=gev_k3_lddd(x,v1hat,d1,v2hat,fd2,v3hat)
}
#
# 9 mu1 (two versions)
#
if(aderivs) mu1=gev_mu1fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu1=gev_mu1f(alpha,v1hat,d1,v2hat,fd2,v3hat,d3)
if(extramodels|means){
if(aderivs) mu1_k3=gev_k3_mu1fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu1_k3=gev_k3_mu1f(alpha,v1hat,d1,v2hat,fd2,v3hat)
}
if(pdf){
if(aderivs){
mu1m=gev_mu1fa(alpham,v1hat,v2hat,v3hat)
mu1p=gev_mu1fa(alphap,v1hat,v2hat,v3hat)
}else{
mu1m=gev_mu1f(alpham,v1hat,d1,v2hat,fd2,v3hat,d3)
mu1p=gev_mu1f(alphap,v1hat,d1,v2hat,fd2,v3hat,d3)
}
}
#
# 10 mu2 (two versions)
#
if(aderivs) mu2=gev_mu2fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu2=gev_mu2f(alpha,v1hat,d1,v2hat,fd2,v3hat,d3)
if(extramodels|means){
if(aderivs) mu2_k3=gev_k3_mu2fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu2_k3=gev_k3_mu2f(alpha,v1hat,d1,v2hat,fd2,v3hat)
}
if(pdf){
if(aderivs){
mu2m=gev_mu2fa(alpham,v1hat,v2hat,v3hat)
mu2p=gev_mu2fa(alphap,v1hat,v2hat,v3hat)
} else {
mu2m=gev_mu2f(alpham,v1hat,d1,v2hat,fd2,v3hat,d3)
mu2p=gev_mu2f(alphap,v1hat,d1,v2hat,fd2,v3hat,d3)
}
}
#
# 11 model 2: flat prior
#
if(extramodels|means){
lambdad_flat=matrix(0,3)
dq=dmgs(lddi,lddd,mu1,lambdad_flat,mu2,dim=3)
flat_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
flat_quantiles="extramodels not selected"
}
#
# 12 model 3: rh_ML (needs to use 2d version of Bayesian code, and ldd_k3,lddd_k3,mu1_k3,mu2_k3)
#
if(extramodels|means){
lambdad_rh_mle=c(0,-1/v2hat)
dq=dmgs(lddi_k3,lddd_k3,mu1_k3,lambdad_rh_mle,mu2_k3,dim=2)
rh_ml_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
rh_ml_quantiles="extramodels not selected"
}
#
# 13 model 4: rh_Flat with flat prior on shape (needs to use 3d version of Bayesian code)
#
lambdad_rh_flat=c(0,-1/v2hat,0)
dq=dmgs(lddi,lddd,mu1,lambdad_rh_flat,mu2,dim=3)
rh_flat_quantiles=ml_quantiles+dq/(nx*fhat)
if(pdf){
dqm=dmgs(lddi,lddd,mu1m,lambdad_rh_flat,mu2m,dim=3)
dqp=dmgs(lddi,lddd,mu1p,lambdad_rh_flat,mu2p,dim=3)
quantilesm=ml_quantilesm+dqm/(nx*fhatm)
quantilesp=ml_quantilesp+dqp/(nx*fhatp)
ml_pdf=fhat
rh_flat_pdf=-(alphap-alpham)/(quantilesp-quantilesm)
} else{
ml_pdf=fhat
rh_flat_pdf="pdf not selected"
}
#
# 14 model 5: JP, calculated from g, using Jacobi's formula, in a function in the generic library
#
if(extramodels|means){
lambdad_jp=jpf3p(ggd,detg,ggi) #this is jp
dq=dmgs(lddi,lddd,mu1,lambdad_jp,mu2,dim=3)
jp_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
jp_quantiles="extramodels not selected"
}
#
# 15 model 6: custom prior on shape parameter
#
if(extramodels|means){
lambdad_custom=c(0,-1/v2hat,customprior)
dq=dmgs(lddi,lddd,mu1,lambdad_custom,mu2,dim=3)
custom_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
custom_quantiles="extramodels not selected"
}
#
# 16 means
#
means=gev_means(means,ml_params,lddi,lddi_k3,lddd,lddd_k3,
lambdad_flat,lambdad_rh_mle,
lambdad_rh_flat,lambdad_jp,lambdad_custom,
nx,dim=3)
ml_mean =means$ml_mean
flat_mean =means$flat_mean
rh_ml_mean =means$rh_ml_mean
rh_flat_mean =means$rh_flat_mean
jp_mean =means$jp_mean
custom_mean =means$custom_mean
#
# 17 waicscores
#
waic=gev_waic(waicscores,x,v1hat,d1,v2hat,fd2,v3hat,d3,lddi,lddd,
lambdad_rh_flat,aderivs)
waic1=waic$waic1
waic2=waic$waic2
#
# 19 rust
#
ru_quantiles="rust not selected"
if(rust){
rustsim=rgev_cp(nrust,x,rust=TRUE,mlcp=FALSE)
ru_quantiles=makeq(rustsim$ru_deviates,p)
}
#end of if(dmgs)
} else {
flat_quantiles=ml_quantiles
rh_ml_quantiles=ml_quantiles
rh_flat_quantiles=ml_quantiles
ru_quantiles=ml_quantiles
jp_quantiles=ml_quantiles
pw_quantiles=ml_quantiles
custom_quantiles=ml_quantiles
rh_flat_pdf=ml_pdf
flat_mean=ml_mean
rh_ml_mean=ml_mean
rh_flat_mean=ml_mean
jp_mean=ml_mean
custom_mean=ml_mean
}
list( ml_params=ml_params,
pw_params=pw_params,
ml_value=ml_value,
# ldd=ldd,
# lddi=lddi,
standard_errors=standard_errors,
ml_quantiles=ml_quantiles,
ml_max=ml_max,
revert2ml=revert2ml,
flat_quantiles=flat_quantiles,
rh_ml_quantiles=rh_ml_quantiles,
cp_quantiles=rh_flat_quantiles,
ru_quantiles=ru_quantiles,
jp_quantiles=jp_quantiles,
pw_quantiles=pw_quantiles,
custom_quantiles=custom_quantiles,
ml_pdf=ml_pdf,
cp_pdf=rh_flat_pdf,
maic=maic,
waic1=waic1,
waic2=waic2,
ml_mean=ml_mean,
flat_mean=flat_mean,
rh_ml_mean=rh_ml_mean,
cp_mean=rh_flat_mean,
jp_mean=jp_mean,
custom_mean=custom_mean,
cp_method=crhpflat_dmgs_cpmethod())
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
rgev_cp=function(n,x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,
minxi=-0.45,maxxi=0.45,
extramodels=FALSE,rust=FALSE,mlcp=TRUE,debug=FALSE,aderivs=TRUE){
# stopifnot(is.finite(n),!is.na(n),is.finite(x),!is.na(x),length(ics)==3)
stopifnot(is.finite(x),!is.na(x),length(ics)==3)
ml_params="mlcp not selected"
ml_deviates="mlcp not selected"
flat_deviates="mlcp not selected"
rh_ml_deviates="mlcp not selected"
jp_deviates="mlcp not selected"
cp_deviates="mlcp not selected"
ru_deviates="rust not selected"
if(mlcp){
q=qgev_cp(x,runif(n),ics=ics,d1=d1,fd2=fd2,d3=d3,extramodels=extramodels,
aderivs=aderivs)
ml_params=q$ml_params
ml_deviates=q$ml_quantiles
flat_deviates=q$flat_quantiles
rh_ml_deviates=q$rh_ml_quantiles
ru_deviates=q$ru_quantiles
cp_deviates=q$cp_quantiles
jp_deviates=q$jp_quantiles
}
if(rust){
th=tgev_cp(n,x)$theta_samples
ru_deviates=numeric(0)
for (i in 1:n){
ru_deviates[i]=rgev(1,mu=th[i,1],sigma=th[i,2],xi=th[i,3])
}
}
op=list(ml_params=ml_params,
ml_deviates=ml_deviates,
flat_deviates=flat_deviates,
rh_ml_deviates=rh_ml_deviates,
cp_deviates=cp_deviates,
ru_deviates=ru_deviates,
jp_deviates=jp_deviates,
cp_method=crhpflat_dmgs_cpmethod())
return(op)
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
dgev_cp=function(x,y=x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,
minxi=-0.45,maxxi=0.45,extramodels=FALSE,
rust=FALSE,nrust=1000,debug=FALSE,aderivs=TRUE){
stopifnot(is.finite(x),!is.na(x),is.finite(y),!is.na(y),length(ics)==3)
ics=gev_setics(x,ics)
opt1=optim(ics,gev_loglik,x=x,control=list(fnscale=-1))
v1hat=opt1$par[1]
v2hat=opt1$par[2]
v3hat=opt1$par[3]
if(v3hat<=(-1)){revert2ml=TRUE}else{revert2ml=FALSE}
ml_params=c(v1hat,v2hat,v3hat)
# gev_checkmle(ml_params,minxi,maxxi)
dd=dgevsub(x=x,y=y,ics=ics,d1=d1,fd2=fd2,d3=d3,customprior=0,
minxi=minxi,maxxi=maxxi,extramodels=extramodels,aderivs=aderivs)
ru_pdf="rust not selected"
if(rust&&(!revert2ml)){
th=tgev_cp(nrust,x)$theta_samples
ru_pdf=numeric(length(y))
for (ir in 1:nrust){
ru_pdf=ru_pdf+dgev(y,mu=th[ir,1],sigma=th[ir,2],xi=th[ir,3])
}
ru_pdf=ru_pdf/nrust
} else {
ru_pdf=dd$ml_pdf
}
op=list(
ml_params=dd$ml_params,
ml_pdf=dd$ml_pdf,
revert2ml=revert2ml,
ru_pdf=ru_pdf,
cp_method=nopdfcdfmsg())
return(op)
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
pgev_cp=function(x,y=x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,
minxi=-0.45,maxxi=0.45,extramodels=FALSE,
rust=FALSE,nrust=1000,debug=FALSE,aderivs=TRUE){
stopifnot(is.finite(x),!is.na(x),is.finite(y),!is.na(y),length(ics)==3)
ics=gev_setics(x,ics)
opt1=optim(ics,gev_loglik,x=x,control=list(fnscale=-1))
v1hat=opt1$par[1]
v2hat=opt1$par[2]
v3hat=opt1$par[3]
if(v3hat<=(-1)){revert2ml=TRUE}else{revert2ml=FALSE}
ml_params=c(v1hat,v2hat,v3hat)
# gev_checkmle(ml_params,minxi,maxxi)
dd=dgevsub(x=x,y=y,ics=ics,d1=d1,fd2=fd2,d3=d3,customprior=0,
minxi=minxi,maxxi=maxxi,extramodels=extramodels,aderivs=aderivs)
ru_cdf="rust not selected"
if(rust&&(!revert2ml)){
th=tgev_cp(nrust,x)$theta_samples
ru_cdf=numeric(length(y))
for (ir in 1:nrust){
ru_cdf=ru_cdf+pgev(y,mu=th[ir,1],sigma=th[ir,2],xi=th[ir,3])
}
ru_cdf=ru_cdf/nrust
} else {
ru_pdf=dd$ml_pdf
}
op=list(
ml_params=dd$ml_params,
ml_cdf=dd$ml_cdf,
revert2ml=revert2ml,
ru_cdf=ru_cdf,
cp_method=nopdfcdfmsg())
return(op)
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
tgev_cp=function(n,x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,extramodels=FALSE,debug=FALSE){
# stopifnot(is.finite(n),!is.na(n),is.finite(x),!is.na(x),length(ics)==3)
stopifnot(is.finite(x),!is.na(x),length(ics)==3)
ics=gev_setics(x,ics)
# t=ru(gev_logf,x=x,n=n,d=3,init=c(0,1,0))
t=ru(gev_logf,x=x,n=n,d=3,init=ics)
list(theta_samples=t$sim_vals)
}
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Defines functions tgev_cp pgev_cp dgev_cp rgev_cp qgev_cp
Documented in dgev_cp pgev_cp qgev_cp rgev_cp tgev_cp
#' Generalized Extreme Value Distribution, Predictions Based on a Calibrating Prior
#'
#' @inherit man description author references seealso return
#' @inheritParams man
#'
#' @inheritSection man Optional Return Values
#' @inheritSection man Optional Return Values (EVT models only)
#' @inheritSection man Optional Return Values (some EVT models only)
# #' @inheritSection man Details (homogeneous models)
#' @inheritSection man Details (non-homogeneous models)
# #' @inheritSection man Details (analytic integration)
#' @inheritSection man Details (DMGS integration)
#' @inheritSection man Details (RUST)
#'
#' @section Details of the Model:
#' The GEV distribution has distribution function
#' \deqn{F(x;\mu,\sigma,\xi)=\exp{(-t(x;\mu,\sigma,\xi))}}
#' where
#' \deqn{t(x;\mu,\sigma,\xi) =
#' \begin{cases}
#' {\left[1+\xi\left(\frac{x-\mu}{\sigma}\right)\right]}^{-1/\xi} & \text{if $\xi \ne 0$}\\
#' \exp{\left(-\frac{x-\mu}{\sigma}\right)} & \text{if $\xi=0$}
#' \end{cases}}
#' where
#' \eqn{x} is the random variable and
#' \eqn{\mu,\sigma>0,\xi} are the parameters.
#'
#' The calibrating prior we use is given by
#' \deqn{\pi(\mu,\sigma,\xi) \propto \frac{1}{\sigma}}
#' as given in Jewson et al. (2025).
#'
#' The code will stop with an error if the
#' input data gives a maximum likelihood
#' value for the shape parameter that lies outside the range \code{(minxi,maxxi)},
#' since outside this range there may be numerical problems.
#' Such values seldom occur
#' in real observed data for maxima.
#'
#' @example man/examples/example_110_gev.R
#'
#' @name gev_cp
NULL
#' @rdname gev_cp
#' @inheritParams man
#' @export
#'
qgev_cp=function(x,p=seq(0.1,0.9,0.1),ics=c(0,0,0),
d1=0.01,fd2=0.01,d3=0.01,fdalpha=0.01,
minxi=-1,maxxi=999,
means=FALSE,waicscores=FALSE,extramodels=FALSE,
pdf=FALSE,customprior=0,
dmgs=TRUE,rust=FALSE,nrust=100000,
pwm=FALSE,debug=FALSE,aderivs=TRUE){
#
# 1 intro
#
stopifnot(is.finite(x),!is.na(x),is.finite(p),!is.na(p),p>0,p<1,length(ics)==3,fdalpha<1)
alpha=1-p
nx=length(x)
nalpha=length(alpha)
if(pdf){
dalpha=pmin(fdalpha*alpha,fdalpha*(1-alpha))
alpham=alpha-dalpha
alphap=alpha+dalpha
}
#
# 2 ml param estimate
#
ics=gev_setics(x,ics)
opt1=optim(ics,gev_loglik,x=x,control=list(fnscale=-1))
v1hat=opt1$par[1]
v2hat=opt1$par[2]
v3hat=opt1$par[3]
ml_params=c(v1hat,v2hat,v3hat)
# gev_checkmle(ml_params,minxi,maxxi)
if(debug)message(" v1hat,v2hat,v3hat=",v1hat,v2hat,v3hat,"")
pw_params="pwm not selected"
if(pwm)pw_params=gev_pwm_params(x)
if(abs(v3hat)>=1){revert2ml=TRUE}else{revert2ml=FALSE}
# 3 aic
#
ml_value=opt1$val
maic=make_maic(ml_value,nparams=3)
#
# 4 calc ml quantiles and densities (vectorized over alpha)
#
ml_quantiles=qgev((1-alpha),mu=v1hat,sigma=v2hat,xi=v3hat)
if(v3hat<0){
ml_max=v1hat-v2hat/v3hat
} else {
ml_max=Inf
}
fhat=dgev(ml_quantiles,mu=v1hat,sigma=v2hat,xi=v3hat)
pw_quantiles="pwm not selected"
if(pwm)pw_quantiles=qgev((1-alpha),mu=pw_params[1],sigma=pw_params[2],xi=pw_params[3])
#
# dmgs
#
standard_errors="dmgs not selected"
flat_quantiles="dmgs not selected"
rh_ml_quantiles="dmgs not selected"
rh_flat_quantiles="dmgs not selected"
cp_quantiles="dmgs not selected"
jp_quantiles="dmgs not selected"
ru_quantiles="dmgs not selected"
custom_quantiles="dmgs not selected"
ml_pdf="dmgs not selected"
rh_flat_pdf="dmgs not selected"
cp_pdf="dmgs not selected"
waic1="dmgs not selected"
waic2="dmgs not selected"
ml_mean="dmgs not selected"
flat_mean="dmgs not selected"
rh_mean="dmgs not selected"
rh_flat_mean="dmgs not selected"
rh_ml_mean="dmgs not selected"
cp_mean="dmgs not selected"
cp_method="dmgs not selected"
jp_mean="dmgs not selected"
custom_mean="dmgs not selected"
#
# 5 alpha pdf stuff
#
if((dmgs)&&(!revert2ml)){
if(debug)message(" ml_quantiles=",ml_quantiles,"")
if(pdf){
ml_quantilesm=qgev((1-alpham),mu=v1hat,sigma=v2hat,xi=v3hat)
ml_quantilesp=qgev((1-alphap),mu=v1hat,sigma=v2hat,xi=v3hat)
fhatm=dgev(ml_quantilesm,mu=v1hat,sigma=v2hat,xi=v3hat)
fhatp=dgev(ml_quantilesp,mu=v1hat,sigma=v2hat,xi=v3hat)
}
#
# 6 expected information matrix and related (for Jeffreys prior)
#
if(debug)message(" call gev.infomat")
if(extramodels|means){
gg=gev.infomat(c(v1hat,v2hat,v3hat),dat=c(1),method=c("exp")) #faster than num (seems to fail when v3hat=0.4 though)
ggi=solve(gg)
detg=det(gg)
ggd=gev_ggd_mev(v1hat,d1,v2hat,fd2,v3hat,d3) #is faster than num
}
#
# 7 ldd (two versions)
#
if(debug)message(" calculate ldd")
if(aderivs) ldd=gev_ldda(x,v1hat,v2hat,v3hat)
if(!aderivs)ldd=gev_ldd(x,v1hat,d1,v2hat,fd2,v3hat,d3)
lddi=solve(ldd)
standard_errors=make_se(nx,lddi)
if(extramodels|means){
if(aderivs) ldd_k3=gev_k3_ldda(x,v1hat,v2hat,v3hat)
if(!aderivs)ldd_k3=gev_k3_ldd(x,v1hat,d1,v2hat,fd2,v3hat)
lddi_k3=solve(ldd_k3)
}
#
# 8 lddd (two versions)
#
if(debug)message(" calculate lddd")
if(aderivs) lddd=gev_lddda(x,v1hat,v2hat,v3hat)
if(!aderivs)lddd=gev_lddd(x,v1hat,d1,v2hat,fd2,v3hat,d3)
if(extramodels|means){
if(aderivs) lddd_k3=gev_k3_lddda(x,v1hat,v2hat,v3hat)
if(!aderivs)lddd_k3=gev_k3_lddd(x,v1hat,d1,v2hat,fd2,v3hat)
}
#
# 9 mu1 (two versions)
#
if(aderivs) mu1=gev_mu1fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu1=gev_mu1f(alpha,v1hat,d1,v2hat,fd2,v3hat,d3)
if(extramodels|means){
if(aderivs) mu1_k3=gev_k3_mu1fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu1_k3=gev_k3_mu1f(alpha,v1hat,d1,v2hat,fd2,v3hat)
}
if(pdf){
if(aderivs){
mu1m=gev_mu1fa(alpham,v1hat,v2hat,v3hat)
mu1p=gev_mu1fa(alphap,v1hat,v2hat,v3hat)
}else{
mu1m=gev_mu1f(alpham,v1hat,d1,v2hat,fd2,v3hat,d3)
mu1p=gev_mu1f(alphap,v1hat,d1,v2hat,fd2,v3hat,d3)
}
}
#
# 10 mu2 (two versions)
#
if(aderivs) mu2=gev_mu2fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu2=gev_mu2f(alpha,v1hat,d1,v2hat,fd2,v3hat,d3)
if(extramodels|means){
if(aderivs) mu2_k3=gev_k3_mu2fa(alpha,v1hat,v2hat,v3hat)
if(!aderivs)mu2_k3=gev_k3_mu2f(alpha,v1hat,d1,v2hat,fd2,v3hat)
}
if(pdf){
if(aderivs){
mu2m=gev_mu2fa(alpham,v1hat,v2hat,v3hat)
mu2p=gev_mu2fa(alphap,v1hat,v2hat,v3hat)
} else {
mu2m=gev_mu2f(alpham,v1hat,d1,v2hat,fd2,v3hat,d3)
mu2p=gev_mu2f(alphap,v1hat,d1,v2hat,fd2,v3hat,d3)
}
}
#
# 11 model 2: flat prior
#
if(extramodels|means){
lambdad_flat=matrix(0,3)
dq=dmgs(lddi,lddd,mu1,lambdad_flat,mu2,dim=3)
flat_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
flat_quantiles="extramodels not selected"
}
#
# 12 model 3: rh_ML (needs to use 2d version of Bayesian code, and ldd_k3,lddd_k3,mu1_k3,mu2_k3)
#
if(extramodels|means){
lambdad_rh_mle=c(0,-1/v2hat)
dq=dmgs(lddi_k3,lddd_k3,mu1_k3,lambdad_rh_mle,mu2_k3,dim=2)
rh_ml_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
rh_ml_quantiles="extramodels not selected"
}
#
# 13 model 4: rh_Flat with flat prior on shape (needs to use 3d version of Bayesian code)
#
lambdad_rh_flat=c(0,-1/v2hat,0)
dq=dmgs(lddi,lddd,mu1,lambdad_rh_flat,mu2,dim=3)
rh_flat_quantiles=ml_quantiles+dq/(nx*fhat)
if(pdf){
dqm=dmgs(lddi,lddd,mu1m,lambdad_rh_flat,mu2m,dim=3)
dqp=dmgs(lddi,lddd,mu1p,lambdad_rh_flat,mu2p,dim=3)
quantilesm=ml_quantilesm+dqm/(nx*fhatm)
quantilesp=ml_quantilesp+dqp/(nx*fhatp)
ml_pdf=fhat
rh_flat_pdf=-(alphap-alpham)/(quantilesp-quantilesm)
} else{
ml_pdf=fhat
rh_flat_pdf="pdf not selected"
}
#
# 14 model 5: JP, calculated from g, using Jacobi's formula, in a function in the generic library
#
if(extramodels|means){
lambdad_jp=jpf3p(ggd,detg,ggi) #this is jp
dq=dmgs(lddi,lddd,mu1,lambdad_jp,mu2,dim=3)
jp_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
jp_quantiles="extramodels not selected"
}
#
# 15 model 6: custom prior on shape parameter
#
if(extramodels|means){
lambdad_custom=c(0,-1/v2hat,customprior)
dq=dmgs(lddi,lddd,mu1,lambdad_custom,mu2,dim=3)
custom_quantiles=ml_quantiles+dq/(nx*fhat)
} else {
custom_quantiles="extramodels not selected"
}
#
# 16 means
#
means=gev_means(means,ml_params,lddi,lddi_k3,lddd,lddd_k3,
lambdad_flat,lambdad_rh_mle,
lambdad_rh_flat,lambdad_jp,lambdad_custom,
nx,dim=3)
ml_mean =means$ml_mean
flat_mean =means$flat_mean
rh_ml_mean =means$rh_ml_mean
rh_flat_mean =means$rh_flat_mean
jp_mean =means$jp_mean
custom_mean =means$custom_mean
#
# 17 waicscores
#
waic=gev_waic(waicscores,x,v1hat,d1,v2hat,fd2,v3hat,d3,lddi,lddd,
lambdad_rh_flat,aderivs)
waic1=waic$waic1
waic2=waic$waic2
#
# 19 rust
#
ru_quantiles="rust not selected"
if(rust){
rustsim=rgev_cp(nrust,x,rust=TRUE,mlcp=FALSE)
ru_quantiles=makeq(rustsim$ru_deviates,p)
}
#end of if(dmgs)
} else {
flat_quantiles=ml_quantiles
rh_ml_quantiles=ml_quantiles
rh_flat_quantiles=ml_quantiles
ru_quantiles=ml_quantiles
jp_quantiles=ml_quantiles
pw_quantiles=ml_quantiles
custom_quantiles=ml_quantiles
rh_flat_pdf=ml_pdf
flat_mean=ml_mean
rh_ml_mean=ml_mean
rh_flat_mean=ml_mean
jp_mean=ml_mean
custom_mean=ml_mean
}
list( ml_params=ml_params,
pw_params=pw_params,
ml_value=ml_value,
# ldd=ldd,
# lddi=lddi,
standard_errors=standard_errors,
ml_quantiles=ml_quantiles,
ml_max=ml_max,
revert2ml=revert2ml,
flat_quantiles=flat_quantiles,
rh_ml_quantiles=rh_ml_quantiles,
cp_quantiles=rh_flat_quantiles,
ru_quantiles=ru_quantiles,
jp_quantiles=jp_quantiles,
pw_quantiles=pw_quantiles,
custom_quantiles=custom_quantiles,
ml_pdf=ml_pdf,
cp_pdf=rh_flat_pdf,
maic=maic,
waic1=waic1,
waic2=waic2,
ml_mean=ml_mean,
flat_mean=flat_mean,
rh_ml_mean=rh_ml_mean,
cp_mean=rh_flat_mean,
jp_mean=jp_mean,
custom_mean=custom_mean,
cp_method=crhpflat_dmgs_cpmethod())
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
rgev_cp=function(n,x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,
minxi=-0.45,maxxi=0.45,
extramodels=FALSE,rust=FALSE,mlcp=TRUE,debug=FALSE,aderivs=TRUE){
# stopifnot(is.finite(n),!is.na(n),is.finite(x),!is.na(x),length(ics)==3)
stopifnot(is.finite(x),!is.na(x),length(ics)==3)
ml_params="mlcp not selected"
ml_deviates="mlcp not selected"
flat_deviates="mlcp not selected"
rh_ml_deviates="mlcp not selected"
jp_deviates="mlcp not selected"
cp_deviates="mlcp not selected"
ru_deviates="rust not selected"
if(mlcp){
q=qgev_cp(x,runif(n),ics=ics,d1=d1,fd2=fd2,d3=d3,extramodels=extramodels,
aderivs=aderivs)
ml_params=q$ml_params
ml_deviates=q$ml_quantiles
flat_deviates=q$flat_quantiles
rh_ml_deviates=q$rh_ml_quantiles
ru_deviates=q$ru_quantiles
cp_deviates=q$cp_quantiles
jp_deviates=q$jp_quantiles
}
if(rust){
th=tgev_cp(n,x)$theta_samples
ru_deviates=numeric(0)
for (i in 1:n){
ru_deviates[i]=rgev(1,mu=th[i,1],sigma=th[i,2],xi=th[i,3])
}
}
op=list(ml_params=ml_params,
ml_deviates=ml_deviates,
flat_deviates=flat_deviates,
rh_ml_deviates=rh_ml_deviates,
cp_deviates=cp_deviates,
ru_deviates=ru_deviates,
jp_deviates=jp_deviates,
cp_method=crhpflat_dmgs_cpmethod())
return(op)
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
dgev_cp=function(x,y=x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,
minxi=-0.45,maxxi=0.45,extramodels=FALSE,
rust=FALSE,nrust=1000,debug=FALSE,aderivs=TRUE){
stopifnot(is.finite(x),!is.na(x),is.finite(y),!is.na(y),length(ics)==3)
ics=gev_setics(x,ics)
opt1=optim(ics,gev_loglik,x=x,control=list(fnscale=-1))
v1hat=opt1$par[1]
v2hat=opt1$par[2]
v3hat=opt1$par[3]
if(v3hat<=(-1)){revert2ml=TRUE}else{revert2ml=FALSE}
ml_params=c(v1hat,v2hat,v3hat)
# gev_checkmle(ml_params,minxi,maxxi)
dd=dgevsub(x=x,y=y,ics=ics,d1=d1,fd2=fd2,d3=d3,customprior=0,
minxi=minxi,maxxi=maxxi,extramodels=extramodels,aderivs=aderivs)
ru_pdf="rust not selected"
if(rust&&(!revert2ml)){
th=tgev_cp(nrust,x)$theta_samples
ru_pdf=numeric(length(y))
for (ir in 1:nrust){
ru_pdf=ru_pdf+dgev(y,mu=th[ir,1],sigma=th[ir,2],xi=th[ir,3])
}
ru_pdf=ru_pdf/nrust
} else {
ru_pdf=dd$ml_pdf
}
op=list(
ml_params=dd$ml_params,
ml_pdf=dd$ml_pdf,
revert2ml=revert2ml,
ru_pdf=ru_pdf,
cp_method=nopdfcdfmsg())
return(op)
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
pgev_cp=function(x,y=x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,
minxi=-0.45,maxxi=0.45,extramodels=FALSE,
rust=FALSE,nrust=1000,debug=FALSE,aderivs=TRUE){
stopifnot(is.finite(x),!is.na(x),is.finite(y),!is.na(y),length(ics)==3)
ics=gev_setics(x,ics)
opt1=optim(ics,gev_loglik,x=x,control=list(fnscale=-1))
v1hat=opt1$par[1]
v2hat=opt1$par[2]
v3hat=opt1$par[3]
if(v3hat<=(-1)){revert2ml=TRUE}else{revert2ml=FALSE}
ml_params=c(v1hat,v2hat,v3hat)
# gev_checkmle(ml_params,minxi,maxxi)
dd=dgevsub(x=x,y=y,ics=ics,d1=d1,fd2=fd2,d3=d3,customprior=0,
minxi=minxi,maxxi=maxxi,extramodels=extramodels,aderivs=aderivs)
ru_cdf="rust not selected"
if(rust&&(!revert2ml)){
th=tgev_cp(nrust,x)$theta_samples
ru_cdf=numeric(length(y))
for (ir in 1:nrust){
ru_cdf=ru_cdf+pgev(y,mu=th[ir,1],sigma=th[ir,2],xi=th[ir,3])
}
ru_cdf=ru_cdf/nrust
} else {
ru_pdf=dd$ml_pdf
}
op=list(
ml_params=dd$ml_params,
ml_cdf=dd$ml_cdf,
revert2ml=revert2ml,
ru_cdf=ru_cdf,
cp_method=nopdfcdfmsg())
return(op)
}
#' @rdname gev_cp
#' @inheritParams man
#' @export
tgev_cp=function(n,x,ics=c(0,0,0),d1=0.01,fd2=0.01,d3=0.01,extramodels=FALSE,debug=FALSE){
# stopifnot(is.finite(n),!is.na(n),is.finite(x),!is.na(x),length(ics)==3)
stopifnot(is.finite(x),!is.na(x),length(ics)==3)
ics=gev_setics(x,ics)
# t=ru(gev_logf,x=x,n=n,d=3,init=c(0,1,0))
t=ru(gev_logf,x=x,n=n,d=3,init=ics)
list(theta_samples=t$sim_vals)
}
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