diagnostics: Diagnostics plots for MCMC algorithm.

In ExtremalDep: Extremal Dependence Models

Diagnostics plots for MCMC algorithm.

Description

This function displays traceplots of the scaling parameter from the proposal distribution of the adaptive MCMC scheme and the associated acceptance probability.

Usage

diagnostics(mcmc)    

Arguments

mcmc	An output of the fGEV or fExtDep.np function with method="Bayesian".

Details

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 \tau 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 (\tau_1,\tau_2) and acceptance probabilities. For the dependence structure (step 3 of the algorithm), a trace plot of the polynomial order \kappa is given with the associated acceptance probability.

Value

a graph of traceplots of the scaling parameter from the proposal distribution of the adaptive MCMC scheme and the associated acceptance probability.

Author(s)

Simone Padoan, [e-mail], https://faculty.unibocconi.it/simonepadoan/; Boris Beranger, [e-mail] https://www.borisberanger.com; Giulia Marcon, [e-mail]

References

Beranger, B., Padoan, S. A. and Sisson, S. A. (2021). Estimation and uncertainty quantification for extreme quantile regions. Extremes, 24, 349-375.

See Also

fExtDep.np.

Examples

##################################################
### Example - Pollution levels in Milan, Italy ###
##################################################
	
## Not run: 

### 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)


## End(Not run)
	

ExtremalDep documentation built on Sept. 26, 2023, 1:06 a.m.