Hi All,
I have two questions:
I am computing a linear regression model with 0 as Intercept.
Well, I would like the sum of my predicted values be equal to a constant
and therefore analyze if my coefficients are significatively different
using or not this constraint.
Does anyone know how I can constrain my model in a such way?
Here is the code:
data<-read.table ("input.txt", header=T, dec=".", sep="\t"); attach(data)
lm <-lm(pop ~ ag + sav + mf -1, data=data)
pred <- predict(lm)
sum(pred)
So I want to constrain my sum (pred) to be equal to C, with C=sum(pop)
My second question is: is it possible to make the same constraint BUT with
C as a vector of values?
Let's say If I have 5 observations in 'data', with 'pop' as the first
column. I want to compute the same model as above with a 'vector' of
constraints. In that case, C=xi (with i from 1 to 3)
x1 = sum (data[c(2,4), 1])
x2 = sum(data [c(1,3), 1])
x3 = data[5,1]
Thanks in advance -- Any help is welcome!
Jean-Paul Kibambe
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