问题
I'm working with R on a sample of 145 observations. I have created five subsamples each with 29 observations, while the response variable q
has been sorted. As a result, subset1 contains the 29 lines of the data frame with the lowest output, subset2 contains the following 29 lines, etc.
I am regressing the variable q
on the predictors x1
, x2
ans x3
. I now need to perform two experiments :
- Constraining the error variance to be the same over all subsamples;
- Constraining the coefficients on
x2
andx3
as well as the error variance to be the same over the 5 OLS regressions.
So far my approach has been to use the package plm
which allows to perform panel regressions. However, I don't know to specifically constrain the error variance, or specific coefficients. Besides, I think there must be a way to do this with the more basic tools incorporated in R.
Please don't hesitate to provide alternative methods. Thanks in advance for your help !
回答1:
Looks like this is all you need:
set.seed(0)
dat <- data.frame(q = sort(rnorm(145)), x1 = rnorm(145), x2 = rnorm(145),
x3 = rnorm(145), group = gl(5, 29))
fit <- lm(q ~ x1 * group + x2 + x3, data = dat)
#Coefficients:
#(Intercept) x1 group2 group3 group4 group5
# -1.211435 0.049316 0.610405 1.128571 1.631891 2.502886
# x2 x3 x1:group2 x1:group3 x1:group4 x1:group5
# -0.027927 -0.015151 -0.004244 -0.074085 -0.044885 -0.074637
Here, I have introduced a grouping factor variable group
. Model estimation for all five groups are done at the same time. With formula:
q ~ x1 * group + x2 + x3
we have coefficients of x2
and x3
being the same for all groups. While the interaction x1*group
suggests that we have different intercept and slope for x1
for different groups.
If you don't want different intercept for each group, you can use formula:
q ~ x1 + x1 : group + x2 + x3
来源:https://stackoverflow.com/questions/39195558/r-constraining-coefficients-and-error-variance-over-multiple-subsample-regress