Nothing
## ----setup, include=FALSE-----------------------------------------------------
knitr::opts_chunk$set(fig.width=7, fig.height=7, fig.align = 'center',
fig.show='hold', warning=FALSE,
message=FALSE, progress=FALSE,
collapse=TRUE, comment="#>")
if(isTRUE(capabilities("cairo"))) {
knitr::opts_chunk$set(dev.args=list(type="cairo"))
}
## ---- eval=FALSE--------------------------------------------------------------
# install.packages('devtools')
# devtools::install_github('Keefe-Murphy/MEDseq')
## ---- eval=FALSE--------------------------------------------------------------
# install.packages('MEDseq')
## -----------------------------------------------------------------------------
library(MEDseq)
## ---- echo=FALSE--------------------------------------------------------------
suppressMessages(library(TraMineR))
## -----------------------------------------------------------------------------
data(mvad, package="MEDseq")
mvad$Location <- factor(apply(mvad[,5L:9L], 1L, function(x) which(x == "yes")),
labels = colnames(mvad[,5L:9L]))
mvad <- list(covariates = mvad[c(3L:4L,10L:14L,87L)],
sequences = mvad[,15L:86L],
weights = mvad[,2L])
mvad.cov <- mvad$covariates
mvad.seq <- seqdef(mvad$sequences[-c(1L,2L)],
states = c("EM", "FE", "HE", "JL", "SC", "TR"),
labels = c("Employment", "Further Education", "Higher Education",
"Joblessness", "School", "Training"))
## ---- eval=FALSE--------------------------------------------------------------
# mod1 <- MEDseq_fit(mvad.seq, G=11, modtype="UUN", weights=mvad$weights, gating= ~ gcse5eq,
# covars=mvad.cov, control=MEDseq_control(noise.gate=FALSE))
## ---- eval=FALSE--------------------------------------------------------------
# # 10-component CUN model with no covariates.
# # CUN models have a precision parameter for each sequence position (i.e. time point),
# # though each time point's precision is common across clusters.
#
# mod2 <- MEDseq_fit(mvad.seq, G=10, modtype="CUN", weights=mvad$weights)
#
# # 12-component CC model with all covariates.
# # CC models have a single precision parameter across all clusters and time points.
#
# mod3 <- MEDseq_fit(mvad.seq, G=12, modtype="CC", weights=mvad$weights,
# gating= ~ . - Grammar - Location, covars=mvad.cov)
## ---- include=FALSE-----------------------------------------------------------
load(file="mvad_mod1.rda")
load(file="mvad_mod2.rda")
load(file="mvad_mod3.rda")
## -----------------------------------------------------------------------------
(comp <- MEDseq_compare(mod1, mod2, mod3, criterion="bic"))
## -----------------------------------------------------------------------------
opt <- comp$optimal
summary(opt, classification = TRUE, parameters = FALSE, network = FALSE)
## -----------------------------------------------------------------------------
print(opt$gating)
## ---- eval=FALSE--------------------------------------------------------------
# plot(opt, type="clusters")
## ---- echo=FALSE--------------------------------------------------------------
knitr::include_graphics("MVAD_Clusters.png")
## ---- eval=FALSE--------------------------------------------------------------
# plot(opt, type="central")
## ---- echo=FALSE--------------------------------------------------------------
knitr::include_graphics("MVAD_Central.png")
## ---- fig.height=8.5----------------------------------------------------------
plot(opt, type="dbsvals")
## -----------------------------------------------------------------------------
MEDseq_meantime(opt, MAP=TRUE, norm=TRUE)
## -----------------------------------------------------------------------------
data(biofam, package="MEDseq")
biofam <- list(covs = cbind(biofam[2L:9L], age = 2002 - biofam$birthyr),
sequences = biofam[10L:25L] + 1L)
bio.cov <- biofam$covs[,colSums(is.na(biofam$covs)) == 0]
bio.seq <- seqdef(biofam$sequences,
states = c("P", "L", "M", "L+M",
"C", "L+C", "L+M+C", "D"),
labels = c("Parent", "Left", "Married",
"Left+Marr", "Child", "Left+Child",
"Left+Marr+Child", "Divorced"))
## ---- eval=FALSE--------------------------------------------------------------
# # The UUN model includes a noise component.
# # Otherwise, there is a precision parameter for each time point in each cluster.
#
# bio <- MEDseq_fit(bio.seq, G=10, modtype="UUN", gating= ~ age,
# covars=bio.cov, noise.gate=FALSE)
## ---- include=FALSE-----------------------------------------------------------
load(file="bio_mod.rda")
## -----------------------------------------------------------------------------
print(bio)
## ---- eval=FALSE--------------------------------------------------------------
# plot(bio, type="clusters", seriated="both")
## ---- echo=FALSE--------------------------------------------------------------
knitr::include_graphics("BIO_Clusters.png")
## -----------------------------------------------------------------------------
plot(bio, type="precision", quant.scale=TRUE, seriated="clusters")
## ---- fig.height=8.5----------------------------------------------------------
plot(bio, type="aswvals")
## -----------------------------------------------------------------------------
seqplot(bio.seq, type="Ht")
## ---- fig.height=8.5----------------------------------------------------------
plot(bio, type="Ht", ylab=NA)
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