R线性回归中roll_lm和lm的区别

Question

I am looking to make a rolling linear regression, i found the function roll_lm, but it provide a different result from the function lm. Are they not supposed to yield the same results ?

# creating dataset
set.seed(123)
ABC <- sample(seq(from = 20, to = 50, by = 5), size = 50, replace = TRUE)
DCE <- sample(seq(from = 20, to = 50, by = 5), size = 50, replace = TRUE)

# Calculating rolling correlation and using last coeff
library(roll)
Rolling.Correl <- roll_lm(ABC , DCE, 50)
last(Rolling.Correl$coefficients[,2])
# [1] -0.233245

# Calculating basic regression using lm
Trad.Rolling.Correl <- lm(ABC ~ DCE)
Trad.Rolling.Correl

# Call:
# lm(formula = ABC ~ DCE)
#
# Coefficients:
# (Intercept)          DCE  
#     41.9204      -0.2112  

On this specific case, I get -0.233245 in one hand and -0.2112 in the other.

Answer 1

?roll_lm says roll_lm(x, y,...) , so you need to compare it with lm(DCE ~ ABC)

lm(formula = DCE ~ ABC)

# Coefficients:
# (Intercept)          ABC  
#     43.6236      -0.2332