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
<[EMAIL PROTECTED]> wrote:
qqplot(z,z.predict$fit)
2008-08-12 16:59:58.105 R[3203] *** Assertion failure in -
[RDeviceView lockFocus], AppKit.subproj/NSView.m:3248
2008-08-12 16:59:58.105 R[3203] *** REngine.runREPL: caught ObjC
exception in the main loop!
*** Please report the following error on [EMAIL PROTECTED]
along with the full description of how to reproduce it:
*** reason: lockFocus sent to a view whose window is deferred and
does not yet have a corresponding platform window
*** name: NSInternalInconsistencyException, info: (null)
*** Version: R 2.5.1 (42083) R.app R 2.5.1 GUI 1.20 (4535)/i386
Consider saving your work soon in case this problem leads to a full
crash.
context: Trying to understand the meaning of predict in the case of
predict.lm
error occurs with qqplot(z,z.predict$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
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