# Example uses of the functions in "JointMLEFunctions.r"

# NB most functions are not intended to be called directly. The two main functions for producing the diagnostic plots are
# "NHPP.diag" and "Expl.diag" for the NHPP model, and bivariate exponential dependence model, respectively. 

source("~/.../JointMLEFunctions.r")

# Required library ismev:
library(ismev)


#############################################################################################################
# NHPP examples
###############

xnorm<-abs(rnorm(5000))
NHPP.diag(xnorm, k=30, q1=0)

xexp<-rexp(10000)
NHPP.diag(xexp, k=30, q1=0)

xgamma<-rgamma(2000, 2, 5)
NHPP.diag(xgamma, k=20, q1=0)

# Use of other arguments:
#########################

# View different plots:

# White noise and Parameter Stability only
NHPP.diag(xnorm, k=30, q1=0, plots=c("WN","PS"))

# Likelihood ratio and White noise only
NHPP.diag(xnorm, k=30, q1=0, plots=c("LRT","WN"))

# Parameter Stability only
NHPP.diag(xnorm, k=30, q1=0, plots=c("PS"))


# Change size of markings etc by passing arguments through to "plot"

NHPP.diag(xnorm, k=30, q1=0, cex.lab=2, cex.axis=2, cex.main=2)

# Plot against raw threshold values rather than quantiles

NHPP.diag(xnorm, k=30, q1=0, UseQuantiles=F)

# Change the range of quantiles between which threshold selection is carried out:

NHPP.diag(xnorm, k=30, q1=0.2, q2=0.95)


#############################################################################################################
# Exponential examples
######################

library(mvtnorm)
xbvn<-rmvnorm(6000, sigma=matrix(c(1,0.7,0.7,1),2,2))

# Transform margins to exponential 
xbvn.exp<- -log(1-pnorm(xbvn))
Expl.diag(apply(xbvn.exp,1,min), k=30, q1=0, param="Rate")
Expl.diag(apply(xbvn.exp,1,min), k=30, q1=0, param="InvRate")