On Thu, 14 Aug 2003 09:08:26 -0700, Spencer Graves
[EMAIL PROTECTED] wrote :
This seems to identify a possible bug in R 1.7.1 under Windows 2000:
tstDf - data.frame(y = 1:11, x=1:11)
fit - nls(y~a/x, data=tstDf, start=list(a=1))
predict(fit, se.fit=TRUE)
[1] 7.0601879 3.5300939 2.3533960
On Thu, 14 Aug 2003, Spencer Graves wrote:
This seems to identify a possible bug in R 1.7.1 under Windows 2000:
tstDf - data.frame(y = 1:11, x=1:11)
fit - nls(y~a/x, data=tstDf, start=list(a=1))
predict(fit, se.fit=TRUE)
[1] 7.0601879 3.5300939 2.3533960 1.7650470 1.4120376
You can use the well-known Taylor series approximation to the
variance of an arbitrary function:
Var( f(X) ) ~= Sum( s[i]^2*D2[i] ) + 2*Sum( Sum( s[i,j]*D[i]*D[j] ) )
where D2[i] is the second partial derivative of f(x) with respect
to the ith parameter and D[j] is the first partial derivative
This seems to identify a possible bug in R 1.7.1 under Windows 2000:
tstDf - data.frame(y = 1:11, x=1:11)
fit - nls(y~a/x, data=tstDf, start=list(a=1))
predict(fit, se.fit=TRUE)
[1] 7.0601879 3.5300939 2.3533960 1.7650470 1.4120376 1.1766980 1.0085983
[8] 0.8825235 0.7844653 0.7060188
Regarding the accuracy of the Taylor series approximation, my favorite
reference is Bates Watts (1988) Nonlinear Regression Analysis and Its
Applications (Wiley, esp. pp. 255-260). Recently, Brian Ripley also
Chambers Hastie (1992) Statistical Models in S (Wadsworth, ch. 10) and
Venables
On Thu, 14 Aug 2003 12:43:25 -0400, Duncan Murdoch [EMAIL PROTECTED]
wrote :
Perhaps the description below of what se.fit is supposed to do should
be modified.
I've done that now in the development version (to become 1.8.0).
Err, I mean in the patch version (but it should still end up in
On Thu, 14 Aug 2003 12:43:25 -0400, Duncan Murdoch [EMAIL PROTECTED]
wrote :
Perhaps the description below of what se.fit is supposed to do should
be modified.
I've done that now in the development version (to become 1.8.0).
Duncan Murdoch
__
[EMAIL
