"BXC (Bendix Carstensen)" <[EMAIL PROTECTED]> writes: > > apc <- glm( D ~ ns( Ax, knots=seq(50,80,10), Bo=c(40,90) ) + > + ns( Cx, knots=seq(1880,1940,20), Bo=c(1840,1960) ) + > + ns( Px, knots=seq(1960,1980,10), Bo=c(1940,2000) ) + > + offset( log( Y ) ), > + family=poisson ) > > pterm <- predict( apc, type="terms" ) > > plink <- predict( apc, type="link" ) > > ( apply( pterm, 1, sum ) + log( Y ) - plink )[1:10] > 1 2 3 4 5 6 7 8 9 > 10 > 6.85047 6.85047 6.85047 6.85047 6.85047 6.85047 6.85047 6.85047 6.85047 > 6.85047 > > coef( apc )[1] > (Intercept) > -13.61998 > > >From the help page for predict.glm I would have expected that the > constant 6.85 > was -intercept. > > What am I missing from predict.glm? (or from splines?)
Same thing that you're missing from predict(..., type="terms") in general. Try y <- rnorm(10) x <- runif(10) apc <- lm(y~x) predict(apc,type="terms") predict(apc,type="terms") - predict(apc,type="response") mean(predict(apc,type="terms")) and I think enlightenment will follow. -- O__ ---- Peter Dalgaard Blegdamsvej 3 c/ /'_ --- Dept. of Biostatistics 2200 Cph. N (*) \(*) -- University of Copenhagen Denmark Ph: (+45) 35327918 ~~~~~~~~~~ - ([EMAIL PROTECTED]) FAX: (+45) 35327907 ______________________________________________ [EMAIL PROTECTED] mailing list https://www.stat.math.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html
