score | R Documentation |
computes the score (log-likelihood) of new observations using a trained model
score(xnew, fit)
xnew |
a new single observation, observation matrix
or a list of the class |
fit |
a fitted model using the |
the vector of scores (log-likelihood) of xnew
Morteza Amini, morteza.amini@ut.ac.ir
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)
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