[R] Constraining linear regression model

[Jean-Paul Kibambe Lubamba]

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