EstHMMGen {GenHMM1d} | R Documentation |
Estimation of univariate hidden Markov model
Description
This function estimates the parameters from a univariate hidden Markov model
Usage
EstHMMGen(
y,
ZI = 0,
reg,
family,
start = 0,
max_iter = 10000,
eps = 1e-04,
size = 0,
theta0 = NULL,
graph = FALSE
)
Arguments
y |
observations; (n x 1) |
ZI |
1 if zero-inflated, 0 otherwise (default) |
reg |
number of regimes (including zero-inflated; must be > ZI) |
family |
distribution name; run the function distributions() for help |
start |
starting parameters for the estimation; (1 x p) |
max_iter |
maximum number of iterations of the EM algorithm; suggestion 10000 |
eps |
precision (stopping criteria); suggestion 0.001. |
size |
additional parameter for some discrete distributions; run the command distributions() for help |
theta0 |
initial parameters for each regimes; (r x p), default is NULL |
graph |
TRUE a graph, FALSE otherwise (default); only for continuous distributions |
Details
#############################################################################
Value
theta |
estimated parameters; (r x p) |
Q |
estimated transition matrix for the regimes; (r x r) |
eta |
conditional probabilities of being in regime k at time t given observations up to time t; (n x r) |
lambda |
conditional probabilities of being in regime k at time t given all observations; (n x r) |
U |
matrix of Rosenblatt transforms; (n x r) |
cvm |
cramer-von-Mises statistic for goodness-of-fit |
W |
pseudo-observations that should be uniformly distributed under the null hypothesis |
LL |
log-likelihood |
nu |
stationary distribution |
AIC |
Akaike information criterion |
BIC |
Bayesian information criterion |
CAIC |
consistent Akaike information criterion |
AICcorrected |
Akaike information criterion corrected |
HQC |
Hannan-Quinn information criterion |
stats |
empirical means and standard deviation of each regimes using lambda |
pred_l |
estimated regime using lambda |
pred_e |
estimated regime using eta |
runs_l |
estimated number of runs using lambda |
runs_e |
estimated number of runs using eta |
Examples
family = "gaussian"
Q = matrix(c(0.8, 0.3, 0.2, 0.7), 2, 2) ;
theta = matrix(c(-1.5, 1.7, 1, 1),2,2) ;
y = SimHMMGen(theta, Q=Q, family=family, n=100)$SimData
est = EstHMMGen(y, reg=2, family=family)