Dear Chuck,
thanks for your reply. I think the graphs you showed me looks
beautifully. The only problem I see with it, is that the regression-
lines are potentially extended beyond the original data. So, I
changed some of your suggested code.
The weird thing is, when I try to implement the working code into a
function, it stops working. I can't figure out why, but it seems like
the called variables are not transferred properly to the xyplot-
function within the overall function.
I tried the following:
# using the fm2-estimated model
library(nlme)
library(lattice)
fm2 <- lme(distance ~ poly(age, 2) * Sex,
data = Orthodont, random = ~ 1)
newdat <- fm2$data
newdat$newpred <- predict(fm2, newdat, level = 0)
xyplot(newpred ~ age, groups=Sex, data = newdat, panel = function(x,
y, ...)
{
panel.superpose(x, y, type="l", ...)
}
)
#This works properly and is basically a simplified version of what
you suggested, but it doesn't extent the model beyond the data it was
based on. The following does not work though:
# now inside function
vis <- function(model, pred, grp, lvl)
{
newdat <- model$data
newdat$newpred <- predict(model, newdat, level = lvl)
xyplot(newpred ~ pred,
groups=grp,
data = newdat,
panel = function(x, y, ...)
{
panel.superpose(x, y, type="l", ...)
}
)
}
> vis(fm2,"age","Sex",0)
Error in !all.na : invalid argument type
In addition: Warning messages:
1: NAs introduced by coercion
2: NAs introduced by coercion
> vis(fm2,age,Sex,0)
Error in eval(expr, envir, enclos) : object "Sex" not found
I was wondering what I am missing here. Has it something to do with
the way a lattice function behaves within a function, or am I missing
something else here?
I hope someone can help me a bit further here.
Greetings,
Rense Nieuwenhuis
On Apr 6, 2007, at 14:24 , Chuck Cleland wrote:
> Rense Nieuwenhuis wrote:
>> Dear expeRts,
>>
>> I am trying to plot a lme-object {package nlme) in such a way, that
>> on a selected level the x-axis represents the value on a selected
>> predictor and the y-axis represents the predicted-outcome variable.
>> The graphs would than consist of several lines that each represent
>> one group. I can't find such a plotting function.
>>
>> I could write such a function myself, based on ranef() and fixef(),
>> but it would be a waste of time if such a function would already
>> exist.
>>
>> Does any of you such a function?
>
> I don't know of a single function with an lme object as argument,
> but
> for what I think you have in mind, here is how you might go about it:
>
> library(nlme)
>
> fm2 <- lme(distance ~ poly(age, 2) * Sex,
> data = Orthodont, random = ~ 1)
>
> newdat <- expand.grid(age = 8:14, Sex = c("Male","Female"))
>
> newdat$PREDDIST <- predict(fm2, newdat, level = 0)
>
> library(lattice)
>
> xyplot(PREDDIST ~ age, groups=Sex, ylab="Model Predicted Distance",
> data = newdat, xlab="Age",
> panel = function(x, y, ...){
> panel.grid(h=6,v=6)
> panel.superpose(x, y, type="l", ...)},
> main="Orthodont Growth Model",
> key = simpleKey(levels(newdat$Sex),
> lines=TRUE, points=FALSE)
> )
>
>> Regards,
>>
>> Rense Nieuwenhuis
>>
>> ______________________________________________
>> [email protected] mailing list
>> https://stat.ethz.ch/mailman/listinfo/r-help
>> PLEASE do read the posting guide http://www.R-project.org/posting-
>> guide.html
>> and provide commented, minimal, self-contained, reproducible code.
>
> --
> Chuck Cleland, Ph.D.
> NDRI, Inc.
> 71 West 23rd Street, 8th floor
> New York, NY 10010
> tel: (212) 845-4495 (Tu, Th)
> tel: (732) 512-0171 (M, W, F)
> fax: (917) 438-0894
>
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