On 19 Oct 2003 21:18:09 -0700, you wrote:
>The problem seems to be that some of the values of d$Age.Month are 0
>and since the Weibull always has a value of 0 at 0, the log likelihood
>comes out insane. (I'm getting 0 values due to quantization error).
>OTOH when I remove the 0 values it works gr
Spencer Graves <[EMAIL PROTECTED]> writes:
> Bates and Watts (1988) Nonlinear Regression Analysis and Its
> Applications (Wiley) explain that parameter effects curvature
> seems to be vastly greater than the "intrinsic curvature" of the
> nonlinear manifold, onto which a res
If the algorithm works properly, you should get exactly the same
answer using a linear or a log scale for the parameters.
The bigger question is not bias but the accuracy of a normal
approximation for confidence intervals and regions. I have evaluated
this by making contour plots of
Spencer Graves <[EMAIL PROTECTED]> writes:
> I have not used "nlm", but that happens routinely with function
> minimizers trying to test negative values for one or more
> component of x. My standard approach to something like this is
> to parameterize "llfunc" in terms of l
I have not used "nlm", but that happens routinely with function
minimizers trying to test negative values for one or more component of
x. My standard approach to something like this is to parameterize
"llfunc" in terms of log(shape) and log(scale), as follows:
llfunc <- function (x) { -s
I'm trying to fit a Weibull distribution to some data via maximum
likelihood estimation. I'm following the procedure described by Doug
Bates in his "Using Open Source Software to Teach Mathematical
Statistics" but I keep getting warnings about NaNs being converted to
maximum positive value:
> llfu
