transfer from old repo

2 Longterm/kmeans-clustering.R

@@ -0,0 +1,114 @@
+ds <- readr::read_csv("2 Longterm/assigndata.csv", na = c("", "NA")) |> na.omit()
+
+ds <- ds |> dplyr::mutate(mrs_1 = mrs_1 > 2)
+
+
+library(tidymodels)
+library(tidyverse)
+
+ds |>
+  na.omit() |>
+  kmeans(centers = 3)
+
+
+ds_num <- ds |> mutate(
+  across(where(is.double), ~ scale(.x)),
+  across(is.character, ~ as.numeric(factor(.x)))
+)
+
+ds_num |> kmeans(centers = 3)
+
+set.seed(321)
+kclusts <-
+  tibble(k = 1:9) %>%
+  mutate(
+    kclust = map(k, ~ kmeans(ds_num, .x)),
+    tidied = map(kclust, tidy),
+    glanced = map(kclust, glance),
+    augmented = map(kclust, augment, ds_num)
+  )
+
+kclusts
+
+clusters <-
+  kclusts %>%
+  unnest(cols = c(tidied))
+
+assignments <-
+  kclusts %>%
+  unnest(cols = c(augmented))
+
+clusterings <-
+  kclusts %>%
+  unnest(cols = c(glanced))
+
+ggplot(assignments, aes(x = pase_0, y = age)) +
+  geom_point(aes(color = .cluster), alpha = 0.8) +
+  facet_wrap(~k)
+
+ggplot(clusterings, aes(k, tot.withinss)) +
+  geom_line() +
+  geom_point()
+
+PCA
+
+pairs(assignments[-c(2:4)],
+  gap = 0,
+  bg = c("red", "yellow", "blue")[assignments$k],
+  pch = 21
+)
+
+
+pc.out <- prcomp(ds_num, center = TRUE, scale = TRUE)
+
+pc.sum <- summary(pc.out)
+
+pscr <- tibble(
+  x = 1:dim(pc.sum$importance)[2],
+  Proportion = pc.sum$importance[2, ],
+  Cumulative = pc.sum$importance[3, ]
+) %>%
+  pivot_longer(cols = -x) %>%
+  ggplot(aes(x = x, y = value, color = name)) +
+  geom_line() +
+  geom_point() +
+  ylim(0, 1) +
+  labs(
+    title = "Scree plot",
+    color = "Variance"
+  ) +
+  ylab("Variance") +
+  xlab("Principal components")
+
+
+###
+###
+
+install.packages("VarSelLCM")
+library(VarSelLCM)
+
+# Please indicate the number of cores you wan to use for parallelization
+nb.CPU <- 4
+# clustering without variable selection (about than 10/20 sec on 4 CPU)
+res_without <- ds |> select(!mrs_1) |> 
+  mutate(across(is.character, ~ factor(.x))) |> as.data.frame() |> 
+  VarSelCluster(
+    gvals = 1:5,
+    crit.varsel = "BIC",
+    vbleSelec = FALSE,
+    nbcores = nb.CPU
+  )
+
+summary(res_without)
+
+plot(res_without)
+
+plot(x=res_without, y="mdi_1")
+
+plot(x=res_without, y="sex")
+
+print(res_without)
+
+coef(res_without)
+
+VarSelShiny(res_without)