Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
out.width = "100%"
)
## ----setup--------------------------------------------------------------------
library(sarp.snowprofile.alignment)
## -----------------------------------------------------------------------------
## Compute alignment:
dtwAlignment <- dtwSP(SPpairs$A_modeled, SPpairs$A_manual, open.end = FALSE)
## ---- eval=FALSE--------------------------------------------------------------
# ## Plot alignment:
# plotSPalignment(dtwAlignment = dtwAlignment)
## ---- echo=FALSE--------------------------------------------------------------
knitr::include_graphics("figures/alignment.png")
## ---- echo=FALSE, out.width="100%"--------------------------------------------
knitr::include_graphics("figures/legend_gtype.png")
## ---- eval=FALSE--------------------------------------------------------------
# ## Inspect local cost:
# plotCostDensitySP(dtwAlignment)
## ---- echo=FALSE--------------------------------------------------------------
knitr::include_graphics("figures/costDensity.png")
## -----------------------------------------------------------------------------
dtwAlignment$sim <- simSP(dtwAlignment$reference, dtwAlignment$queryWarped, verbose = TRUE, type = "HerlaEtAl2021")
## ----medoid, eval=TRUE--------------------------------------------------------
## rescaling and resampling of the snow profiles:
setRR <- reScaleSampleSPx(SPgroup)$set
## compute the pairwise distance matrix:
distmat <- medoidSP(setRR, retDistmat = T, progressbar = FALSE, verbose = TRUE)$distmat
## hierarchichal clustering:
setRR_hcl <- stats::hclust(as.dist(distmat), method = "complete")
## ---- echo=FALSE, out.width="100%"--------------------------------------------
knitr::include_graphics("figures/cluster_hierarchy.png")
## ---- echo=FALSE, eval=FALSE--------------------------------------------------
# ## This can be used to produce the cluster hierarchy plot:
#
# ## prepare plot:
# cluster_colors <- c("dark orange", "blue", "dark green", "red")
# setRR_dend <- stats::as.dendrogram(setRR_hcl)
# dendextend::labels_colors(setRR_dend) <- cluster_colors[stats::cutree(setRR_hcl, 4)[dendextend::order.dendrogram(setRR_dend)]]
# dendextend::labels_cex(setRR_dend) <- 2.5
# dendextend::labels(setRR_dend) <- seq(12)
#
# layout(matrix(c(1, 1, 2, 2), 2, 2, byrow = T), heights = c(1, 2))
#
# ## plot hierarchy
# plot(setRR_dend, yaxt = "n", xlim = c(1, nrow(distmat)))
# mtext("Cluster hierarchy", side = 2, line = 1)
#
# ## plot profiles
# plot(setRR[dendextend::order.dendrogram(setRR_dend)], SortMethod = 'unsorted', box = F, ylab = "",
# yPadding = 0, xPadding = 0, xaxs = 'i', yaxs = 'i')
# mtext("Rescaled snow height", side = 2, line = 1, las = 0)
# mtext("Individual snow profiles", side = 1, line = 2)
#
# ## plot vertical lines between most dominant clusters
# abline(v = 4.5, lwd = 3)
# abline(v = 7.5, lwd = 2, lty = "dashed")
# abline(v = 9.5, lwd = 2, lty = "dotted")
#
## ---- eval=TRUE---------------------------------------------------------------
unname(medoidSP(distmat = distmat[1:4, 1:4]))
## ---- eval=FALSE--------------------------------------------------------------
# fit <- smacof::mds(as.dist(distmat), type = "ordinal")
## ---- echo=FALSE, out.width="100%"--------------------------------------------
knitr::include_graphics("figures/configuration_plots.png")
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