score: the score of new observations

In hhsmm: Hidden Hybrid Markov/Semi-Markov Model Fitting

the score of new observations

Description

computes the score (log-likelihood) of new observations using a trained model

Usage

score(xnew, fit)

Arguments

xnew	a new single observation, observation matrix or a list of the class hhsmmdata containing $x and $N elements
fit	a fitted model using the hhsmmfit function

Value

the vector of scores (log-likelihood) of xnew

Author(s)

Morteza Amini, [e-mail]

Examples

J <- 3
initial <- c(1, 0, 0)
semi <- c(FALSE, TRUE, FALSE)
P <- matrix(c(0.8, 0.1, 0.1, 0.5, 0, 0.5, 0.1, 0.2, 0.7), nrow = J, 
byrow = TRUE)
par <- list(mu = list(list(7, 8), list(10, 9, 11), list(12, 14)),
sigma = list(list(3.8, 4.9), list(4.3, 4.2, 5.4), list(4.5, 6.1)),
mix.p = list(c(0.3, 0.7), c(0.2, 0.3, 0.5), c(0.5, 0.5)))
sojourn <- list(shape = c(0, 3, 0), scale = c(0, 10, 0), type = "gamma")
model <- hhsmmspec(init = initial, transition = P, parms.emis = par,
dens.emis = dmixmvnorm, sojourn = sojourn, semi = semi)
train <- simulate(model, nsim = c(10, 8, 8, 18), seed = 1234, 
remission = rmixmvnorm)
test <- simulate(model, nsim = c(5, 4, 6, 7), seed = 1234, 
remission = rmixmvnorm)
clus = initial_cluster(train, nstate = 3, nmix = c(2, 2, 2), ltr = FALSE,
final.absorb = FALSE, verbose = TRUE)
semi <- c(FALSE, TRUE, FALSE)
initmodel1 = initialize_model(clus = clus, sojourn = "gamma",
M = max(train$N), semi = semi)
fit1 = hhsmmfit(x = train, model = initmodel1, M = max(train$N))
score(test, fit1)

hhsmm documentation built on March 18, 2022, 5:16 p.m.