Just a small fix to my solution; inserted below.
On 2010-08-27 3:51, Peter Ehlers wrote:
On 2010-08-26 15:52, Marlin Keith Cox wrote:
I agree. I typically do not use non-linear functions, so am seeing the
"art" in describing functions of non-linear plots. One last thing. I
tried
to use a self-starting Weibull function with the posted data and received
the following error.
model<-nls(Level~ SSweibull(Time,Asym,Drop,lrc,pwr))
Error in qr.default(.swts * attr(rhs, "gradient")) :
NA/NaN/Inf in foreign function call (arg 1)
I do not understand the error statement.
The problem is the zeros in your independent variable (see
the definition of coefficient 'lrc'). You can try using
SSweibull(Time + 0.000001, ...). But a better way would
be to use
init <- getInitial(Level~ SSweibull(Time,Asym,Drop,lrc,pwr))
This needs a 'data=' argument for getInitial(). I had put
the variables in a data.frame, then modified the code by
deleting 'data=dat', forgetting that getInitial() does not
default to looking in the global environment for its data.
Easily fixed with
init <- getInitial(Level~ SSweibull(Time,Asym,Drop,lrc,pwr),
data=.GlobalEnv)
or, of course, with an appropriate data.frame.
-Peter Ehlers
(which will often give the converged values). You can
follow up with
fm <- nls(Level ~ Asym-Drop*exp(-exp(lrc)*Time^pwr), start = init)
-Peter Ehlers
kc
On Thu, Aug 26, 2010 at 1:44 PM, Bert Gunter<gunter.ber...@gene.com>
wrote:
My opinions only below; consume at your own risk.
On Thu, Aug 26, 2010 at 2:20 PM, Marlin Keith Cox<marlink...@gmail.com>
wrote:
The background you requested are energetic level (joules) in a group of
starved fish over a time period of 45 days. Weekly, fish (n=5) were
removed
killed and measured for energy. This was done at three temperatures. I
am
comparing the rates at which the fish consume stored body energy at
each
of
the three temperatures. Initial data looks like the colder fish
have different rates (as would be expected) than do warmer fish. In all
cases the slope is greatest at the beginning of the curve and flattens
after
several weeks. This is what is interesting - where in time the line
starts to flatten out.
By calculating a non-linear equation of a line, I was hoping to use the
first and second derivatives of the function to compare and explain
differences between the three temperature.
Bad idea. Derivatives from fitted curves are generally pretty
imprecisely determined. And you don't need them: If the curves are
being (adequately/appropriately) parameterized as Weibull, then all
the information is in the parameters anyway, which can be directly
modeled, fitted, and compared as functions of temperature -- provided
that the design permits this (i.e. provides sufficient precision for
the characterizations/comparisons).
If you don't know how to do this, seek further statistical help.
--
Bert Gunter
Genentech Nonclinical Statistics
The data originally posted was an example of one of the curves
experienced.
kc
On Thu, Aug 26, 2010 at 9:48 AM, David Winsemius<dwinsem...@comcast.net
wrote:
On Aug 26, 2010, at 1:35 PM, Marlin Keith Cox wrote:
I need the parameters estimated for a non-linear equation, an example
of
the
data is below.
# rm(list=ls()) I really wish people would add comments to
destructive
pieces of code.
Time<-c( 0, 0, 0, 0, 0, 2, 2, 2, 2, 2, 3, 3, 3, 3, 3, 4, 4, 4,
4, 4, 5, 5, 5, 5, 5, 8, 8, 8, 8, 8)
Level<-c( 100, 110, 90, 95, 87, 60, 65, 61, 55, 57, 40, 41,
50,
47,
44, 44, 42, 38, 40, 37, 37, 35, 40, 34, 32, 20, 22, 25,
27,
29)
plot(Time,Level,pch=16)
You did not say what sort of "non-linear equation" would best suit,
nor
did
you offer any background regarding the domain of study. There must be
many
ways to do this. After looking at the data, a first pass looks like
this:
lm(log(Level) ~Time )
Call:
lm(formula = log(Level) ~ Time)
Coefficients:
(Intercept) Time
4.4294 -0.1673
exp(4.4294)
[1] 83.88107
points(unique(Time), exp(4.4294 -unique(Time)*0.1673), col="red",
pch=4)
Maybe a Weibull model would be more appropriate.
--
David Winsemius, MD
West Hartford, CT
[[alternative HTML version deleted]]
>
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--
Bert Gunter
Genentech Nonclinical Biostatistics
467-7374
http://devo.gene.com/groups/devo/depts/ncb/home.shtml
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