predict.hhsmmspec {hhsmm}

prediction of state sequence for hhsmm

Description

Predicts the state sequence of a hidden hybrid Markov/semi-Markov model for a new (test) data of class "hhsmmdata" with an optional prediction of the residual useful lifetime (RUL) for a left to right model

Usage

## S3 method for class 'hhsmmspec'
predict(object, newdata, method = "viterbi", M = NA, ...)

Arguments

object

a hidden hybrid Markov/semi-Markov model

newdata

a new (test) data of class "hhsmmdata"

method

the prediction method with two options:

• "viterbi" (default) uses the Viterbi algorithm for prediction

• "smoothing" uses the smoothing algorithm for prediction

M

maximum duration in states

...

additional parameters of the function predict.hhsmm

Value

a list containing the following items:

• x the observation sequence

• s the predicted state sequence

• N the vector of sequence lengths

• p the state probabilities

• RUL the point predicts of the RUL

• RUL.low the lower bounds for the prediction intervals of the RUL

• RUL.up the upper bounds for the prediction intervals of the RUL

Author(s)

Morteza Amini, [e-mail], Afarin Bayat, [e-mail]

References

Guedon, Y. (2005). Hidden hybrid Markov/semi-Markov chains. Computational statistics and Data analysis, 49(3), 663-688.

OConnell, J., & Hojsgaard, S. (2011). Hidden semi Markov models for multiple observation sequences: The mhsmm package for R. Journal of Statistical Software, 39(4), 1-22.

See Also

predict.hhsmm

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, 3, 3, 8), 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)
yhat1 <- predict(initmodel1, test)