Multiple linear regression by group in a rolling window in R - r

Question

My dataframe looks like this:
Date = c(rep(as.Date(seq(15000,15012)),2))
Group = c(rep("a",13),rep("b",13))
y = c(seq(1,26,1))
x1 = c(seq(0.01,0.26,0.01))
x2 = c(seq(0.02,0.26*2,0.02))
df = data.frame(Group,Date,y,x1,x2)
head(df,3)
Group
Date
y
x1
x2
a
2011-01-26
1
0.01
0.02
a
2011-01-27
2
0.02
0.04
a
2011-01-28
3
0.03
0.06
And I would like to do multiple regression by group (y as the dependent variable and x1, x2 as the independent variables) in a rolling window i.e. 3.
I have tried to achieve this using packages tidyverse and zoo with following codes but failed.
## define multi-var-linear regression function and get the residual
rsd <- function(df){
lm(formula = y~x1+x2, data = as.data.frame(df), na.action = na.omit) %>%
resid() %>%
return()
}
## apply it by group with rolling window
x <- df %>% group_by(Group) %>%
rollapplyr(. , width = 3, FUN = rsd)
The output of this code is not what I acutually want.
Does anyone know how to do multiple regression by group in a rolling window?
Thanks in advance, Giselle
Thank Grothendieck and Marcus for your codes!
It really helped me a lot:)
I now appened them here:
# Grothendieck method
rsd <- function(df){
lm(formula = y~x1+x2, data = as.data.frame(df), na.action = na.omit) %>%
resid() %>%
return()
}
width <- 5
df_m2 <-
df %>%
group_by(Group) %>%
group_modify(~ {
cbind(., rollapplyr(.[c("y", "x1", "x2")], width, rsd, fill = NA,
by.column = FALSE))
}) %>%
ungroup %>%
select(c("Group","Date","5")) %>%
dplyr::rename(residual_m2 = "5")
# Marcus method
output <- data.frame()
for (i in unique(df$Group)) {
a = df%>% subset(Group==i)
a[,"residual"] = NA
max = nrow(a)
if(max<5){
next
}
for (j in seq(5,max,by=1)) {
b = a %>% slice((j-4):j)
lm_ = lm(y~x1+x2, data = b)
a[j,]$residual = residuals(lm_)[5]
}
output <-
output %>%
rbind(a)
}

Answer 1

Use group_modify and use rollapplyr with the by.column = FALSE argument so that rsd is applied to all columns at once rather than one at a time.
Note that if you use width 3 with two predictors and an intercept the residuals will necessarily be all zero so we changed the width to 5.
library(dplyr, exclude = c("lag", "filter"))
library(zoo)
width <- 5
df %>%
group_by(Group) %>%
group_modify(~ {
cbind(., rollapplyr(.[c("y", "x1", "x2")], width, rsd, fill = NA,
by.column = FALSE))
}) %>%
ungroup

Answer 2

A good old-fashioned for-loop here could be:
for (i in unique(df$Group)){
for (j in (seq(15000,15012, 3))){
lm_ <- lm(formula = df[df$Group== i & df$Date %in% c(j, j+1, j+2), 3] ~ df[df$Group== i & df$Date %in% c(j, j+1, j+2), 4] + df[df$Group== i & df$Date %in% c(j, j+1, j+2), 5], na.action = na.omit)
print(paste('Group', i, 'Dates from', j, 'to', j+3, residuals(lm_)))
}
}