plot.tuts_polyn: Graphical summaries and diagnostics of tuts_polyn objects

In PeterFranke/tuts: Time Uncertain Time Series Analysis

Description

plot.tuts_polyn plots summaries and diagnostics of a tuts_polyn object.

Usage

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

Arguments

x	A tuts_polyn object.
type	plot/diagnostic type (options:'predTUTS' plots one step out of sample predictions of the model, 'GR' plots Gelman-Rubin diagnostics, 'cv' plots 5-fold cross validation, 'mcmc' plots diagnostics of mcmc objects, and 'volatility' plots volatility realizations).
...	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))

# Set parameters and run the polynomial regression:
polyorder=2
n.sim=1000
PN=tupolyn(y=y,ti.mu=ti.mu,ti.sd=ti.sd,polyorder=polyorder,n.sim=n.sim, CV=TRUE)

# Plots and diagnostics (optional parameters are listed in brackets):
plot(PN,type='predTUTS',CI=0.95)          # One step out of salmple predictions of the model (CI, burn).
plot(PN,type='cv',burn=0.3)               # 5 fold cross-validation (CI, burn).
plot(PN,type='GR',CI=0.95)                # Gelman-Rubin diagnostic (CI).
plot(PN,type='mcmc')                      # MCMC diagnostics.
plot(PN,type='volatility')                # Volatility realizaitons.

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