# NOT RUN {
# Load the MVAD data
data(mvad)
mvad$Location <- factor(apply(mvad[,5:9], 1L, function(x)
which(x == "yes")), labels = colnames(mvad[,5:9]))
mvad <- list(covariates = mvad[c(3:4,10:14,87)],
sequences = mvad[,15:86],
weights = mvad[,2])
mvad.cov <- mvad$covariates
# Create a state sequence object with the first two (summer) time points removed
states <- c("EM", "FE", "HE", "JL", "SC", "TR")
labels <- c("Employment", "Further Education", "Higher Education",
"Joblessness", "School", "Training")
mvad.seq <- seqdef(mvad$sequences[-c(1,2)], states=states, labels=labels)
# }
# NOT RUN {
# Fit a model with weights and a gating covariate
# Have the probability of noise-component membership be constant
# mod <- MEDseq_fit(mvad.seq, G=11, modtype="UUN", weights=mvad$weights,
# gating=~ gcse5eq, covars=mvad.cov, noise.gate=FALSE)
# Estimate standard errors using 100 WLBS samples
# (std <- MEDseq_stderr(mod, N=100))
# }
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