Nothing
if (interactive()) par.default <- par(ask=TRUE)
#----- Test Beta Distribution -----
# "shape1" Markov dependent
# "shape2" time dependent
Pi <- matrix(c(0.8, 0.2,
0.3, 0.7),
byrow=TRUE, nrow=2)
x <- seq(0.01, 0.99, 0.01)
plot(x, dbeta(x, shape1=0.5, shape2=2), type="l",
col="blue", ylab="Density")
points(x, dbeta(x, shape1=8, shape2=2), type="l", col="red")
n <- 1000
x <- dthmm(NULL, Pi, c(0,1), "beta", pm=list(shape1=c(0.5, 6)),
pn=list(shape2=rep(2, n)))
x <- simulate(x, nsim=n)
# use above parameter values as initial values
y <- BaumWelch(x)
# check parameter estimates
print(summary(y))
print(sum(y$delta))
print(y$Pi %*% rep(1, ncol(y$Pi)))
#----- Test Beta Distribution -----
# "shape2" Markov dependent
# "shape1" time dependent
Pi <- matrix(c(0.8, 0.2,
0.3, 0.7),
byrow=TRUE, nrow=2)
x <- seq(0.01, 0.99, 0.01)
plot(x, dbeta(x, shape1=2, shape2=6), type="l",
col="blue", ylab="Density")
points(x, dbeta(x, shape1=2, shape2=0.5), type="l", col="red")
n <- 1000
x <- dthmm(NULL, Pi, c(0,1), "beta", pm=list(shape2=c(0.5, 6)),
pn=list(shape1=rep(2, n)))
x <- simulate(x, nsim=n)
# use above parameter values as initial values
y <- BaumWelch(x)
# check parameter estimates
print(summary(y))
print(sum(y$delta))
print(y$Pi %*% rep(1, ncol(y$Pi)))
if (interactive()) par(par.default)
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