plotVitloc2d: Plot the Classified 2-D Data of a Bivariate HMM With Extra...
In HMMextra0s: Hidden Markov Models with Extra Zeros

Description Usage Arguments Author(s) References Examples

Plot the classified 2-D data with different colours representing different hidden states (or different clusters) obtained from the Viterbi path and confidence contours.

1 2	plotVitloc2d(object, R, Z, HMMest, CI.level=0.95, npoints=100, cols=NA, cex.lab=1.5, cex.axis=1.5, cex=1, cex.text=2)

`object`	is a list containing `y` (the estimated Viterbi path) and `v` (the estimated probability of each time point being in each state). This object is returned from running `Viterbi.hmm0norm2d(R, Z, HMMest)`.
`R`	is the observed data. `R` is a T 2* matrix, where T is the number of observations.
`Z`	is the binary data with the value 1 indicating that an event was observed and 0 otherwise. `Z` is a vector of length T.
`HMMest`	is a list which contains pie, gamma, sig, mu, and delta (the bivariate HMM parameter estimates).
`CI.level`	is a scalar or a vector, the confidence level for the ellipse contour of each state. Default is 0.95.
`npoints`	is the number of points used in the ellipse. Default is 100.
`cols`	is a vector defines the colors to be used for different states. If col=NA, then the default colors will be used.
`cex.lab`	specifies the size of the axis label text.
`cex.axis`	specifies the size of the tick label numbers/text.
`cex`	specifies the size of the points.
`cex.text`	specifies the size of the text indicting the state number.

Ting Wang and Jiancang Zhuang

Wang, T., Zhuang, J., Buckby, J., Obara, K. and Tsuruoka, H. (2018) Identifying the recurrence patterns of non-volcanic tremors using a 2D hidden Markov model with extra zeros. Journal of Geophysical Research, doi: 10.1029/2017JB015360.

pie <- c(0.008,0.2,0.4)
gamma <- matrix(c(0.99,0.007,0.003,
                  0.02,0.97,0.01,
                  0.04,0.01,0.95),byrow=TRUE, nrow=3)
mu <- matrix(c(35.03,137.01,
               35.01,137.29,
               35.15,137.39),byrow=TRUE,nrow=3)
sig <- array(NA,dim=c(2,2,3))
sig[,,1] <- matrix(c(0.005, -0.001,
                   -0.001,0.01),byrow=TRUE,nrow=2)
sig[,,2] <- matrix(c(0.0007,-0.0002,
                    -0.0002,0.0006),byrow=TRUE,nrow=2)
sig[,,3] <- matrix(c(0.002,0.0018,
                     0.0018,0.003),byrow=TRUE,nrow=2)
delta <- c(1,0,0)
y <- sim.hmm0norm2d(mu,sig,pie,gamma,delta, nsim=5000)
R <- y$x
Z <- y$z
HMMEST <- hmm0norm2d(R, Z, pie, gamma, mu, sig, delta)
Viterbi3 <- Viterbi.hmm0norm2d(R,Z,HMMEST)
plotVitloc2d(Viterbi3, R, Z,HMMEST)