mySpec <- hmm(
K = 3, R = 2,
observation =
Gaussian(mu = -10, sigma = 1) +
Gaussian(mu = 0, sigma = 1) +
Gaussian(mu = 10, sigma = 1),
initial = Dirichlet(alpha = c(1, 1, 1)),
transition =
Dirichlet(alpha = c(1.0, 0.2, 0.2)) +
Dirichlet(alpha = c(0.2, 1.0, 0.2)) +
Dirichlet(alpha = c(0.2, 0.2, 1.0)),
name = "Univariate Gaussian"
)
mySim <- sim(mySpec, T = 500, chain = 1, iter = 500, seed = 9000)
y <- extract_ypred(mySim)[1, , ]
colnames(y) <- c("Height", "Weight")
myFit <- fit(mySpec, y = y, chain = 4, iter = 500, seed = 9000)
plot_series(myFit, legend.cex = 0.8)
plot_series(myFit, xlab = "Time steps", features = c("yColoredLine"))
plot_series(myFit, stateProbability = "smoothed", features = c("stateShade", "bottomColoredMarks"))
plot_state_probability(myFit, main = "Title", xlab = "Time")
plot_state_probability(myFit, features = c("stateShade"), main = "Title", xlab = "Time")
plot_state_probability(myFit, features = c("bottomColoredMarks"), main = "Title", xlab = "Time")
plot_state_probability(myFit, features = c("probabilityColoredDots"), main = "Title", xlab = "Time")
plot_state_probability(myFit, features = c("probabilityColoredLine"), main = "Title", xlab = "Time")
plot_state_probability(myFit, stateProbability = "filtered", features = c("bottomColoredMarks", "probabilityFan"), stateProbabilityInterval = posterior_intervals(c(0.05, 0.95)), main = "Title", xlab = "Time")
plot_ppredictive(myFit, type = c("density", "cumulative", "summary"), fun = median)
plot_ppredictive(myFit, type = c("density", "boxplot"), fun = median, subset = 1:10)
plot_ppredictive(myFit, type = c("density", "boxplot", "scatter"), fun = median, fun1 = mean, fun2 = median, subset = 1:40)
plot_ppredictive(myFit, type = c("density", "cumulative", "hist"), fun = median)
plot_ppredictive(myFit, type = c("density", "cumulative", "ks"))
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