# Perform local and global decoding Function that performs local and global decoding (Viterbi) from the output of `est_lm_basic`, `est_lm_cov_latent`, `est_lm_cov_manifest`, and `est_lm_mixed`. The function is no longer maintained. Please look at `lmestDecoding` function ```r decoding(est, Y, X1 = NULL, X2 = NULL, fort = TRUE) ``` ## Arguments - `est`: output from `est_lm_basic`, `est_lm_cov_latent`, `est_lm_cov_manifest`, or `est_lm_mixed` - `Y`: single vector or matrix of responses - `X1`: matrix of covariates on the initial probabilities (`est_lm_cov_latent`) or on the responses (`est_lm_cov_manifest`) - `X2`: array of covariates on the transition probabilites - `fort`: to use Fortran routines ## Returns - **Ul**: matrix of local decoded states corresponding to each row of Y - **Ug**: matrix of global decoded states corresponding to each row of Y ## References Viterbi A. (1967) Error Bounds for Convolutional Codes and an Asymptotically Optimum Decoding Algorithm. **IEEE Transactions on Information Theory**, 13 , 260-269. Juan B., Rabiner L. (1991) Hidden Markov Models for Speech Recognition. **Technometrics**, 33 , 251-272. ## Author(s) Francesco Bartolucci, Silvia Pandolfi, University of Perugia (IT), http://www.stat.unipg.it/bartolucci ## Examples ```r ## Not run: # example for the output from est_lm_basic data(data_drug) data_drug <- as.matrix(data_drug) S <- data_drug[,1:5]-1 yv <- data_drug[,6] n <- sum(yv) # fit the Basic LM model k <- 3 est <- est_lm_basic(S, yv, k, mod = 1) # decoding for a single sequence out1 <- decoding(est, S[1,]) # decoding for all sequences out2 <- decoding(est, S) # example for the output from est_lm_cov_latent with difflogit parametrization data(data_SRHS_long) dataSRHS <- data_SRHS_long[1:1600,] TT <- 8 head(dataSRHS) res <- long2matrices(dataSRHS$id, X = cbind(dataSRHS$gender-1, dataSRHS$race == 2 | dataSRHS$race == 3, dataSRHS$education == 4, dataSRHS$education == 5, dataSRHS$age-50,(dataSRHS$age-50)^2/100), Y= dataSRHS$srhs) # matrix of responses (with ordered categories from 0 to 4) S <- 5-res$YY # matrix of covariates (for the first and the following occasions) # colums are: gender,race,educational level (2 columns),age,age^2) X1 <- res$XX[,1,] X2 <- res$XX[,2:TT,] # estimate the model est <- est_lm_cov_latent(S, X1, X2, k = 2, output = TRUE, param = "difflogit") # decoding for a single sequence out1 <- decoding(est, S[1,,], X1[1,], X2[1,,]) # decoding for all sequences out2 <- decoding(est, S, X1, X2) ## End(Not run) ```

decoding function