[R-SIG-Mac] predict.lm error

[Simon Urbanek]

That error was usually seen when plotting in a minimized window. This  
was fixed in subsequent R releases (and the wording of the exception  
was changed to encourage update). Simply restoring the Quartz window  
should work, the error should be otherwise harmless. Nonetheless as  
Brian pointed out Quartz was entirely rewritten for R 2.7.x so  
updating is strongly encouraged.

Cheers,
Simon


On Aug 12, 2008, at 18:07, Robert Chatfield  
<chatfield at alumni.rice.edu> wrote:

> qqplot(z,z.predict$fit)
> 2008-08-12 16:59:58.105 R[3203] *** Assertion failure in -
> [RDeviceView lockFocus], AppKit.subproj/NSView.m:3248
> 2008-08-12 16:59:58.105 R[3203] *** REngine.runREPL: caught ObjC
> exception in the main loop!
> *** Please report the following error on r-sig-mac at r-project.org
> along with the full description of how to reproduce it:
> *** reason: lockFocus sent to a view whose window is deferred and
> does not yet have a corresponding platform window
> *** name: NSInternalInconsistencyException, info: (null)
> *** Version: R 2.5.1 (42083) R.app R 2.5.1 GUI 1.20 (4535)/i386
> Consider saving your work soon in case this problem leads to a full
> crash.
>
>
> context:  Trying to understand the meaning of predict in the case of
> predict.lm
>
> error occurs with qqplot(z,z.predict$fit)  of very much different
> length vectors (z a matrix)
>
> predict.lm is supplied 40000 x,y pairs  for a two-parameter  
> regression,
> but
>
> length(z.predict$fit) is only 4912
>
> Code follows here
>
> #
> x.jH.min = 10^x.ljH.min
> x.jH.max=  10^x.ljH.max
> y.NO.min=10^y.lNO.min
> y.NO.max=10^y.lNO.max
> #
> x.jH.set = seq(x.jH.min, x.jH.max, by=x.jH.max/200)
> y.NO.set = seq(y.NO.min, y.NO.max, by=y.NO.max/200)
> #
> n.x.jH.set = length(x.jH.set)
> n.y.NO.set = length(y.NO.set)
> x.d = vector(mode="numeric",length=n.x.jH.set*n.y.NO.set)
> y.d = vector(mode="numeric",length=n.x.jH.set*n.y.NO.set)
> length(x.d)
> #
> z = matrix(nrow=length(x.jH.set),ncol=length(y.NO.set))
> dim(z)
> z=matrix(nrow=n.x.jH.set,ncol=n.y.NO.set)
> dim(z)
> ind.d = 1
> for ( xi  in seq(1,n.x.jH.set) ) {
>    xx = x.jH.set[xi]
>    for ( yi in seq(1,n.y.NO.set) ) {
>        yy = y.NO.set[yi]
>        z[xi,yj] = 10^(coefficients(logfit.wt.lm)[1] + coefficients
> (logfit.wt.lm)[2]*log10(xx) + coefficients(logfit.wt.lm)[3]*log10(yy))
>        x.d[ind.d] = xx
>        y.d[ind.d] = yy
>        ind.d = ind.d + 1
>        }
>    }
> ### 
> #####################################################################
> ############
> #
> xy.d = data.frame(cbind(x.d,y.d))
> names(xy.d)=c("x","y")
>
> z.predict=predict(logfit.wt.lm,data=xy.d,se.fit = TRUE)
>
>
>
> ASSUMPTIONS
> ...where these condistions are more or less all you need to run the
> code snippet.
>> length(x.d)
> [1] 40000
>> x.ljH.min
> [1] 7.75
>> x.ljH.max
> [1] 10.25
>> y.lNO.min
> [1] 9.75
>> y.lNO.max
> [1] 12.25
>>
>
>> coefficients(logfit.wt.lm
> + )
> (Intercept)  l.j.HCHO.m      l.NO.m
>  -0.4214285   0.4190370   0.5483868
>>
>    [[alternative HTML version deleted]]
>
> _______________________________________________
> R-SIG-Mac mailing list
> R-SIG-Mac at stat.math.ethz.ch
> https://stat.ethz.ch/mailman/listinfo/r-sig-mac
>
>