Applying yearwise segmented regression in R - r

Question

I have daily rainfall data which I have converted to yearwise cumulative value using following code
library(seas)
library(data.table)
library(ggplot2)
#Loading data
data(mscdata)
dat <- (mksub(mscdata, id=1108447))
dat$julian.date <- as.numeric(format(dat$date, "%j"))
DT <- data.table(dat)
DT[, Cum.Sum := cumsum(rain), by=list(year)]
df <- cbind.data.frame(day=dat$julian.date,cumulative=DT$Cum.Sum)
Then I want to apply segmented regression year-wise to have year-wise breakpoints. I could able to do it for single year like
library("segmented")
x <- subset(dat,year=="1984")$julian.date
y <- subset(DT,year=="1984")$Cum.Sum
fit.lm<-lm(y~x)
segmented(fit.lm, seg.Z = ~ x, npsi=3)
I have used npsi = 3 to have 3 breakpoints. Now how to dinimically apply it year-wise segmented regression and have the estimated breakpoints?

Answer 1

Here's a short script to come out with a customised function so that you can run the different yearwise regressions.
## using tidyverse processes instead of mixing and matching with other data manipulation packages
library(tidyverse); library(segmented); library(seas)
## get mscdata from "seas" packages
data(mscdata)
dat <- (mksub(mscdata, id=1108447))
## generate cumulative sum of rain by year
d2 <- dat %>% group_by(year) %>% mutate(rain_cs = cumsum(rain)) %>% ungroup
## write a custom function
segmentedlm <- function(data, year){
subset.df <- data %>% filter(year == year)
fit.lm <- lm(rain_cs ~ julian.date, subset.df)
segmented(fit.lm, seg.Z = ~ julian.date, npsi=3)
}
# run the customised function for 1975 data
segmentedlm(d2, "1975") %>% plot(., main="1975")
segmentedlm(d2, "1984") %>% plot(., main = "1984")
To output the summary of segmented linear models of multiple years into a text file:
sink("output.txt")
lapply(c("1975", "1984"), function(x) segmentedlm(d2, x))
sink()
You can change the argument for lapply to input all the years.

Answer 2

You can store the lm object in a list and apply segmented for each year.
library(tidyverse)
data <- DT %>%
group_by(year) %>%
summarise(fit.lm = list(lm(Cum.Sum~julian.date)),
julian.date1 = list(julian.date)) %>%
mutate(out = map2(fit.lm, julian.date1, function(x, julian.date)
data.frame(segmented::segmented(x,
seg.Z = ~julian.date, npsi=3)$psi))) %>%
unnest_wider(out) %>%
unnest(cols = c(Initial, Est., St.Err)) %>%
dplyr::select(-fit.lm, -julian.date1)
# A tibble: 90 x 4
# year Initial Est. St.Err
# <int> <dbl> <dbl> <dbl>
# 1 1975 84.8 68.3 1.44
# 2 1975 168. 167. 9.31
# 3 1975 282. 281. 0.917
# 4 1976 84.8 68.3 1.44
# 5 1976 168. 167. 9.33
# 6 1976 282. 281. 0.913
# 7 1977 84.8 68.3 1.44
# 8 1977 168. 167. 9.32
# 9 1977 282. 281. 0.913
#10 1978 84.8 68.3 1.44
# … with 80 more rows