It's not so simple, but consider the following:
x=rep(1:6, each=2, length=24)
y=rep(1:6, each=4)
z=rep(0:1, length=24)
set.seed(1)
tstDF <- data.frame(x=x, y=y, z=z,
w=(x-3.5)^2+(y-3.5)+z+0.1*rnorm(24))
fit <- lm(w~x+y+z+I(x^2)+I(y^2), tstDF)
x0 <- seq(1, 5, .5)
y0 <- seq(1, 6, .5)
Grid <- expand.grid(x=x0, y=y0)
Grid$z <- rep(0.5, dim(Grid)[1])
pred <- predict(fit, Grid)
contour(x0, y0, array(pred, dim=c(length(x0), length(y0))))
This kind of thing is discussed in Venables and Ripley (2002) Modern
Applied Statistics with S, 4th ed. (Springer). I highly recommend this
book.
spencer graves
Michael Hopkins wrote:
>
> Hi All
>
> I have been fitting regression models and would now like to produce some
> contour & image plots from the predictors.
>
> Is there an easy way to do this? My current (newbie) experience with R
> would suggest there is but that it's not always easy to find it!
>
> f3 <- lm( fc ~ poly( speed, 2 ) + poly( torque, 2 ) + poly( sonl, 2 ) +
> poly( p_rail, 2 ) + poly( pil_sep, 2 ) + poly( maf, 2 ) + (speed + torque +
> sonl + p_rail + pil_sep + maf)^2 )
>
> hat <- predict( f3 )
>
> contour( sonl, maf, hat )
>
> Error in contour.default(sonl, maf, hat) :
> increasing 'x' and 'y' values expected
>
> image(sonl, maf, hat)
>
> Error in image.default(sonl, maf, hat) : increasing 'x' and
'y' values
> expected
>
> I have tried na??ve sorting but no luck with that.
>
> I suspect I may need to produce a data grid of some kind but I'm not
clear
> how I would use R to specify such a 2D slice in the 6D design space.
>
> TIA
>
> Michael
>
> P.S. Whilst I'm asking the list - is there an easier way of expressing
a
> full 2nd order model than the one I used above? I'm sure there is but
> finding it etc etc...
>
>
>
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