It can be done via an explicit call to terms[.formula] with keep.order = TRUE.
summary( lm(terms(y ~ ns(x, knots=c(30, 50, 70), intercept=T):A - 1 + B,
keep.order = TRUE)) )I've given other examples in the past, and I think this is in the White Book.
BTW, it is not quite the same as this drops Ba not Bd (as I think it should).
On Thu, 17 Feb 2005, BXC (Bendix Carstensen) wrote:
Consider the following two specifications of a model:
library( splines ) x <- 1:100 y <- rnorm( 100 ) w <- rep( 1, 100 ) A <- factor( sample( 1:2, 100, replace=T ) ) B <- factor( sample( letters[1:4], 100, replace=T ) ) summary( lm( y ~ ns( x, knots=c(30, 50, 70 ), intercept=T ):A - 1 + B ) ) summary( lm( y ~ ns( x, knots=c(30, 50, 70 ), intercept=T ):A - 1 + B:w ) )
The interaction with the constant variable w is how I got to have the two spline terms as "proper" intercept terms.
Is there another way to do this, or is it a featur of the model formulae that lower order terms appear before higher order terms regardless of the order they are specified in the model?
Bendix Carstensen ---------------------- Bendix Carstensen Senior Statistician Steno Diabetes Center Niels Steensens Vej 2 DK-2820 Gentofte Denmark tel: +45 44 43 87 38 mob: +45 30 75 87 38 fax: +45 44 43 07 06 [EMAIL PROTECTED] www.biostat.ku.dk/~bxc
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