在 R 中应用逐年分段回归

问题描述

我有每日降雨量数据，我使用以下代码将其转换为年度累积值

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)

然后我想逐年应用分段回归以获得逐年断点.我可以像这样

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)

我使用 npsi = 3 有 3 个断点.现在如何逐年应用它的分段回归并获得估计的断点?

I have used npsi = 3 to have 3 breakpoints. Now how to dinimically apply it year-wise segmented regression and have the estimated breakpoints?

推荐答案

您可以将 lm 对象存储在列表中，并为每个 yearsegmented/代码>.

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

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