plot.tuts_ar1: Graphical summaries and diagnostics of tuts_ar1' objects

In PeterFranke/tuts: Time Uncertain Time Series Analysis

Description

plot.tuts_ar1(x,type,...) plots summaries and diagnostics of tuts_ar1 objects.

Usage

## S3 method for class 'tuts_ar1'
plot(x, type, ...)

Arguments

x	A tuts_ar1 objects.
type	plot/diagnostic type (options: 'predTUTS' plots one step predictions of the model, 'GR' plots Gelman-Rubin diagnostics, 'cv' plots 5-fold cross validation, 'mcmc' plots diagnostics of mcmc objects)
...	list of optional parameters: 'burn' (burn-in parameter ranging from 0 to 0.7 with default value set to 0), and CI (credible interval ranging from 0.3 to 1 with default value set to 0.95)

Examples

# Import or simulate the data (simulation is chosen for illustrative purposes):
DATA=simtuts(N=50,Harmonics=c(10,30,0), sin.ampl=c(10,10, 0), cos.ampl=c(0,0,0),
trend=0,y.sd=3, ti.sd=1)
y=DATA$observed$y.obs
ti.mu=DATA$observed$ti.obs.tnorm
ti.sd= rep(1, length(ti.mu))

# Run the tuar1 model fitting with cross validation:
n.sim=1000
TUAR1=tuar1(y=y,ti.mu=ti.mu,ti.sd=ti.sd,n.sim=n.sim, CV=TRUE)

# Plots and diagnostics (optional parameters are listed in brackets):
plot(TUAR1,type='predTUTS')                   # One step out of salmple predictions of the model (CI, burn).
plot(TUAR1,type='par', burn=0.4)              # Distributions of parameters of the AR(1) model (burn).
plot(TUAR1,type='mcmc')                       # mcmc diagnostics.
plot(TUAR1,type='cv', burn=0.4, CI=0.9)       # 5 fold cross validation plot(CI, burn).
plot(TUAR1,type='GR')                         # Gelman-Rubin diagnostic (CI, burn).
plot(TUAR1,type='volatility')                 # Volatility realizaitons.

PeterFranke/tuts documentation built on May 30, 2019, 6:24 a.m.