Description Usage Arguments Examples
tuar1
estimates unbiased parameters of time-uncertain AR(1) model.
1 |
y |
A vector of observations. |
ti.mu |
A vector of estimates of timing of observations. |
ti.sd |
A vector of standard deviations of timing. |
n.sim |
A number of simulations. |
CV |
TRUE/FALSE cross-validation indicator. |
... |
list of optional parameters: |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 | # Note: Most of models included in tuts package are computationally intensive. In the example
# below I set parameters to meet CRAN's testing requirement of maximum 5 sec per example.
# A more practical example would contain N=50 in the first line of the code and n.sim=10000.
#1. Import or simulate the data (a simulation is chosen for illustrative purposes):
DATA=simtuts(N=10,Harmonics=c(4,0,0), sin.ampl=c(10,0, 0), cos.ampl=c(0,0,0),
trend=0,y.sd=2, ti.sd=0.2)
y=DATA$observed$y.obs
ti.mu=DATA$observed$ti.obs.tnorm
ti.sd= rep(0.2, length(ti.mu))
#2. Fit the model:
n.sim=1000
TUAR1=tuar1(y=y,ti.mu=ti.mu,ti.sd=ti.sd,n.sim=n.sim,CV=TRUE,n.cores=2)
#3. Generate summary results (optional parameters are listed in brackets):
summary(TUAR1) # Summary results (CI, burn).
#4. Generate plots and diagnostics of the model (optional parameters are listed in brackets):
plot(TUAR1,type='predTUTS') # One step out of salmple predictions (CI, burn).
plot(TUAR1,type='par', burn=0.4) # Distributions of parameters (burn).
plot(TUAR1,type='mcmc') # MCMC diagnostics.
plot(TUAR1,type='cv', burn=0.4, CI=0.9) # 5 fold cross validation (CI, burn).
plot(TUAR1,type='GR') # Gelman-Rubin diagnostic (CI, burn).
plot(TUAR1,type='volatility') # Volatility realizaitons.
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