英文:
Extract breakpoints from multiple variables from grouped data using piecewise (segmented) regression
问题
我想使用分段回归法并从我的分组数据中提取多个变量的分段点。
我已经使用以下代码逐个处理每个变量和组：
library(segmented)mod_lm <- lm(y ~ x, data = df) #进行线性回归mod_seg <- segmented(mod_lm, seg.Z = ~ x) #进行分段回归mod_seg$psi #提取分段点和估计的标准误差
我想在因变量上运行这个操作，而自变量保持不变。我的数据看起来像这样：
x Group Var y9 Group1 Var1 0.69016 Group1 Var1 0.63465 Group1 Var1 0.80895 Group1 Var1 0.12747 Group1 Var1 0.64261 Group1 Var2 0.10592 Group1 Var2 0.69894 Group1 Var2 0.11297 Group1 Var2 0.14587 Group1 Var2 0.81852 Group2 Var1 0.79500 Group2 Var1 0.05331 Group2 Var1 0.18663 Group2 Var1 0.38768 Group2 Var1 0.27882 Group2 Var2 0.15598 Group2 Var2 0.33821 Group2 Var2 0.63469 Group2 Var2 0.60388 Group2 Var2 0.2026
我该如何获取这些分段点并将它们存储在一个新的数据框中？
英文:
I would like to use piecewise regression and extract breakpoints across multiple variables from my grouped data.
I have used following code to do it one by one for each variable & group:
library(segmented)mod_lm <- lm(y ~ x, data = df) #Do LMmod_seg <- segmented(mod_lm, seg.Z = ~ x) #Do segmented regressionmod_seg$psi #Extract breakpoint & standard error of the estimate
I would like to run this across dependent variables, while independent variable remains the same. I also have grouping variable in the data, which I would also like to have included.
My data looks like this:
x Group Var y9 Group1 Var1 0.69016 Group1 Var1 0.63465 Group1 Var1 0.80895 Group1 Var1 0.12747 Group1 Var1 0.64261 Group1 Var2 0.10592 Group1 Var2 0.69894 Group1 Var2 0.11297 Group1 Var2 0.14587 Group1 Var2 0.81852 Group2 Var1 0.79500 Group2 Var1 0.05331 Group2 Var1 0.18663 Group2 Var1 0.38768 Group2 Var1 0.27882 Group2 Var2 0.15598 Group2 Var2 0.33821 Group2 Var2 0.63469 Group2 Var2 0.60388 Group2 Var2 0.2026
How can I get the breakpoints and store them in a new dataframe?
答案1
得分: 1
This heavily relies on this answer: https://stackoverflow.com/questions/68460350/how-to-use-segmented-package-when-working-with-data-frames-with-dplyr-package-to
我不确定这是否符合你的需求，但我们可以循环遍历各个组并在最后提取psi。如果你愿意的话，也可以将列名改为initial、Est.和St.Err。在这个数据集中，我无法完全理解“清理”这个概念，因为它只返回了其中一个组的结果。
library(tidyverse)library(segmented)suppressWarnings(df %>%nest_by(Group, Var) %>%mutate(mod_lm = list(lm(y ~ x, data = data)),mod_seg = list(tryCatch(segmented(mod_lm, seg.Z = ~x),error = function(e) list(NA))),psi = list(mod_seg[['psi']])) %>%unnest(cols = psi, keep_empty = TRUE))#> 断点估计值：5.895779#> # A tibble: 4 x 6#> # Groups: Group, Var [4]#> Group Var data mod_lm mod_seg psi[,1] [,2] [,3]#> <chr> <chr> <list<tibble[,2]>> <list> <list> <dbl> <dbl> <dbl>#> 1 Group1 Var1 [5 x 2] <lm> <list [1]> NA NA NA#> 2 Group1 Var2 [5 x 2] <lm> <lm> NA NA NA#> 3 Group2 Var1 [5 x 2] <lm> <segmentd> 2 2.00 1.17#> 4 Group2 Var2 [5 x 2] <lm> <list [1]> NA NA NA
数据：
read.table(text = "x Group Var y9 Group1 Var1 0.69016 Group1 Var1 0.63465 Group1 Var1 0.80895 Group1 Var1 0.12747 Group1 Var1 0.64261 Group1 Var2 0.10592 Group1 Var2 0.69894 Group1 Var2 0.11297 Group1 Var2 0.14587 Group1 Var2 0.81852 Group2 Var1 0.79500 Group2 Var1 0.05331 Group2 Var1 0.18663 Group2 Var1 0.38768 Group2 Var1 0.27882 Group2 Var2 0.15598 Group2 Var2 0.33821 Group2 Var2 0.63469 Group2 Var2 0.60388 Group2 Var2 0.2026", header = T, stringsAsFactor = F) -> df
英文:
This heavily relies on this answer: https://stackoverflow.com/questions/68460350/how-to-use-segmented-package-when-working-with-data-frames-with-dplyr-package-to
I am not sure if this is what you are after, but we can loop over the groups and extract psi at the end. You can also rename the columns to be initial, Est., and St.Err if you like. With this dataset, I cannot quite wrap my head around "cleaning up" since it only returns results for one of the groups.
library(tidyverse)library(segmented)suppressWarnings(df %>%nest_by(Group, Var) %>%mutate(mod_lm = list(lm(y ~ x, data = data)),mod_seg = list(tryCatch(segmented(mod_lm, seg.Z = ~x),error = function(e) list(NA))),psi = list(mod_seg[['psi']])) %>%unnest(cols = psi, keep_empty = TRUE))#> breakpoint estimate(s): 5.895779#> # A tibble: 4 x 6#> # Groups: Group, Var [4]#> Group Var data mod_lm mod_seg psi[,1] [,2] [,3]#> <chr> <chr> <list<tibble[,2]>> <list> <list> <dbl> <dbl> <dbl>#> 1 Group1 Var1 [5 x 2] <lm> <list [1]> NA NA NA#> 2 Group1 Var2 [5 x 2] <lm> <lm> NA NA NA#> 3 Group2 Var1 [5 x 2] <lm> <segmentd> 2 2.00 1.17#> 4 Group2 Var2 [5 x 2] <lm> <list [1]> NA NA NA
Data:
read.table(text = "x Group Var y9 Group1 Var1 0.69016 Group1 Var1 0.63465 Group1 Var1 0.80895 Group1 Var1 0.12747 Group1 Var1 0.64261 Group1 Var2 0.10592 Group1 Var2 0.69894 Group1 Var2 0.11297 Group1 Var2 0.14587 Group1 Var2 0.81852 Group2 Var1 0.79500 Group2 Var1 0.05331 Group2 Var1 0.18663 Group2 Var1 0.38768 Group2 Var1 0.27882 Group2 Var2 0.15598 Group2 Var2 0.33821 Group2 Var2 0.63469 Group2 Var2 0.60388 Group2 Var2 0.2026", header = T, stringsAsFactor = F) -> df
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