# the score of new observations computes the score (log-likelihood) of new observations using a trained model ```r score(xnew, fit, ...) ``` ## Arguments - `xnew`: a new single observation, observation matrix or a list of the class `hhsmmdata` containing $x and $N elements - `fit`: a fitted model using the `hhsmmfit` function - `...`: additional parameters for the dens.emission and mstep functions ## Returns the vector of scores (log-likelihood) of `xnew` ## Examples ```r ### first example J <- 3 initial <- c(1, 0, 0) semi <- c(FALSE, TRUE, FALSE) P <- matrix(c(0.8, 0.1, 0.1, 0.5, 0, 0.5, 0.1, 0.2, 0.7), nrow = J, byrow = TRUE) par <- list(mu = list(list(7, 8), list(10, 9, 11), list(12, 14)), sigma = list(list(3.8, 4.9), list(4.3, 4.2, 5.4), list(4.5, 6.1)), mix.p = list(c(0.3, 0.7), c(0.2, 0.3, 0.5), c(0.5, 0.5))) sojourn <- list(shape = c(0, 3, 0), scale = c(0, 10, 0), type = "gamma") model <- hhsmmspec(init = initial, transition = P, parms.emis = par, dens.emis = dmixmvnorm, sojourn = sojourn, semi = semi) train <- simulate(model, nsim = c(10, 8, 8, 18), seed = 1234, remission = rmixmvnorm) test <- simulate(model, nsim = c(5, 4, 6, 7), seed = 1234, remission = rmixmvnorm) clus = initial_cluster(train, nstate = 3, nmix = c(2, 2, 2), ltr = FALSE, final.absorb = FALSE, verbose = TRUE) semi <- c(FALSE, TRUE, FALSE) initmodel1 = initialize_model(clus = clus, sojourn = "gamma", M = max(train$N), semi = semi) fit1 = hhsmmfit(x = train, model = initmodel1, M = max(train$N)) score(test, fit1) ### second example num_states <- 3 semi <- rep(TRUE, num_states) init_probs <- rep(1/num_states, num_states) transition_matrix <- matrix(1/(num_states-1), nrow = num_states, ncol = num_states) for (i in seq_along(semi)) { if (semi[i]) { transition_matrix[i, i] <- 0 } } parms_emission <- list(prob = list(c(0.6, 0.2, 0.1, 0.1), c(0.2, 0.6, 0.1, 0.1), c(0.5, 0.3, 0.1, 0.1))) sojourn <- list(shape = c(1, 3, 1), scale = c(3, 10, 4), type = "gamma") dens_emission <- dmultinomial.hhsmm initmodel <- hhsmmspec( init = init_probs, transition = transition_matrix, parms.emission = parms_emission, sojourn = sojourn, dens.emission = dens_emission, remission = rmultinomial.hhsmm, mstep = mstep.multinomial, semi = semi ) prepared_data <- hhsmmdata(as.matrix(sample(1:4,100,replace=TRUE))) fit1 <- hhsmmfit(x = prepared_data, model=initmodel, n=4, M=max(prepared_data$N)) score(xnew = prepared_data, fit = fit1, n=4) ``` ## Author(s) Morteza Amini, morteza.amini@ut.ac.ir

score function