[R] fit linear regression with multiple predictor and constrained intercept
P.Dalgaard at biostat.ku.dk
Wed Nov 28 11:45:18 CET 2007
robert.ptacnik at niva.no wrote:
> Hi group,
> I have this type of data
> x(predictor), y(response), factor (grouping x into many groups, with 6-20
> I want to fit a linear regression with one common intercept. 'factor'
> should only modify the slopes, not the intercept. The intercept is expected
> to be >0.
> If I use
> y~ x + factor, I get a different intercept for each factor level, but one
> slope only
> if I use
> y~ x * factor, I get the interaction term I want, but the intercept is not
> kept constant.
> Also, if I constrain teh intercept in the regression model (y~a+x*factor),
> I get estimates both for slope and intercept of each factor level.
You seem to be looking for the colon operator. In R, unlike certain
other statistical packages, the star implies inclusion of main effects:
a*b is a + b + a:b. There is some trickery about when you get factors
contrast coded in interaction terms (as far as I remember x:factor and
x+x:factor are two different parametrizations of the same model), but
you should be able to find that out by a little experimenting.
O__ ---- Peter Dalgaard Øster Farimagsgade 5, Entr.B
c/ /'_ --- Dept. of Biostatistics PO Box 2099, 1014 Cph. K
(*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918
~~~~~~~~~~ - (p.dalgaard at biostat.ku.dk) FAX: (+45) 35327907
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