On 8/2/07, Sandeman, L. R. <[EMAIL PROTECTED]> wrote:
> Hello,
>
> I have written code to plot an xyplot as follows:
>
> library(lattice)
> xyplot(len~ageJan1|as.factor(cohort),groups=sex,as.table=T,strip=strip.c
> ustom(bg='white',fg='white'),data=dat,
> xlab="Age (January 1st)",ylab="Length (cm)",main="Linear models for male
> and female cod, by cohort",type='p',
> lwd=1.5,auto.key=list(text=c("Male","Female"),points=F,rectangles=F,line
> s=T))
>
> I have fitted a linear model to the same data (for each sex in each
> cohort(year)). I would like to add the fitted models to the existing
> plot (one line for male and one for female, where each panel is a
> separate cohort - as in the above code....). I also want to do this
> with non-linear models if possible.
>
> I have trawled R help, and it seems that panel.superpose may be one
> method of attempting this, however, I am unable to produce any working
> code.
>
> My dataset is too large to put in as an example, but it is in the basic
> form as below (where sex is 1 for male and 5 for female).
>
> len age fitted_model cohort sex
> 1 24 2 30.05771 1977 1
> 2 31 3 36.64122 1977 1
> 3 22 2 27.73938 1978 1
> 4 34 3 36.64122 1977 1
> 5 22 2 27.73938 1978 1
> 6 31 3 36.64122 1977 1
> 7 34 3 36.64122 1977 1
> 8 28 2 27.73938 1978 1
> 9 23 2 27.73938 1978 1
> 10 24 2 27.73938 1978 1
> 11 25 2 27.73938 1978 1
> Etc...
You need to rearrange your data first, so that it looks like
len age cohort sex source
1 24 2 1977 1 obs
2 31 3 1977 1 obs
3 22 2 1978 1 obs
4 34 3 1977 1 obs
...
1 30.05771 2 1977 1 fitted
2 36.64122 3 1977 1 fitted
3 27.73938 2 1978 1 fitted
4 36.64122 3 1977 1 fitted
There are several ways to do that; options include reshape(),
make.groups(), and the reshape package.
After that, something like
xyplot(len ~ age | as.factor(cohort), data = <...>,
groups = interaction(sex, source),
type = c("p", "p", "l", "l"), distribute.type = TRUE)
should give you what you want (this does use panel.superpose
internally).
-Deepayan
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