diagnostics | R Documentation |
This function displays traceplots of the scaling parameter from the proposal distribution of the adaptive MCMC scheme and the associated acceptance probability.
diagnostics(mcmc)
mcmc |
An output of the |
When mcmc
is the output of fGEV
then this corresponds to a marginal estimation and therefore diagnostics
will display in a first plot the value of τ the scaling parameter in the multivariate normal proposal which directly affects the acceptance rate of the proposal parameter values that are displayed in the second plot.
When mcmc
is the output of fExtDep.np
, then this corresponds to an estimation of the dependence structure following the procedure given in Algorithm 1 of Beranger et al. (2021). If the margins are jointly estimated with the dependence (step 1 and 2 of the algorithm) then diagnostics
provides trace plots of the corresponding scaling parameters (τ_1,τ_2) and acceptance probabilities. For the dependence structure (step 3 of the algorithm), a trace plot of the polynomial order κ is given with the associated acceptance probability.
Simone Padoan, simone.padoan@unibocconi.it, https://faculty.unibocconi.it/simonepadoan/; Boris Beranger, borisberanger@gmail.com https://www.borisberanger.com; Giulia Marcon, giuliamarcongm@gmail.com
Beranger, B., Padoan, S. A. and Sisson, S. A. (2021). Estimation and uncertainty quantification for extreme quantile regions. Extremes, 24, 349-375.
fExtDep.np
.
################################################## ### Example - Pollution levels in Milan, Italy ### ################################################## if(interactive()){ ### Here we will only model the dependence structure data(MilanPollution) data <- Milan.winter[,c("NO2","SO2")] data <- as.matrix(data[complete.cases(data),]) # Thereshold u <- apply(data, 2, function(x) quantile(x, prob=0.9, type=3)) # Hyperparameters hyperparam <- list(mu.nbinom = 6, var.nbinom = 8, a.unif=0, b.unif=0.2) ### Standardise data to univariate Frechet margins f1 <- fGEV(data=data[,1], method="Bayesian", sig0 = 0.1, nsim = 5e+4) diagnostics(f1) burn1 <- 1:30000 gev.pars1 <- apply(f1$param_post[-burn1,],2,mean) sdata1 <- trans2UFrechet(data=data[,1], pars=gev.pars1, type="GEV") f2 <- fGEV(data=data[,2], method="Bayesian", sig0 = 0.1, nsim = 5e+4) diagnostics(f2) burn2 <- 1:30000 gev.pars2 <- apply(f2$param_post[-burn2,],2,mean) sdata2 <- trans2UFrechet(data=data[,2], pars=gev.pars2, type="GEV") sdata <- cbind(sdata1,sdata2) ### Bayesian estimation using Bernstein polynomials pollut1 <- fExtDep.np(method="Bayesian", data=sdata, u=TRUE, mar.fit=FALSE, k0=5, hyperparam = hyperparam, nsim=5e+4) diagnostics(pollut1) }
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