perform clustering on the coefficients and identify subgroups

[njtierney]

library(tidyverse)
library(brolgar)
#> Loading required package: tsibble
#> 
#> Attaching package: 'tsibble'
#> The following object is masked from 'package:dplyr':
#> 
#>     id
library(gghighlight)

wages_slope <- wages_ts %>% 
  key_slope(ln_wages ~ xp) 

kmeans_data <- na.omit(wages_slope) %>%
  mutate(scaled = as.numeric(scale(.slope_xp)))

kmeans_fit <- kmeans(kmeans_data$.intercept, centers = 5)

wages_slope_kmeans <- kmeans_data %>%
  mutate(kmeans = kmeans_fit$cluster) %>%
  select(-scaled) %>%
  left_join(wages_ts, by = "id")

ggplot(wages_slope_kmeans,
       aes(x = xp, 
           y = ln_wages, 
           group = id,
           colour = factor(kmeans))) + 
  geom_line() +
  gghighlight() +
  facet_wrap(~kmeans) 

^{Created on 2019-07-25 by the reprex package (v0.3.0)}

[njtierney]

Another approach could be to use curveRep

https://rdrr.io/cran/Hmisc/man/curveRep.html