R users may want to note that there are some extensions in packages for
symbolic derivatives.
In particular, Duncan Murdoch added some "all in R" tools in the package nlsr
that I maintain. This
is a substitute for the nls() function that uses a fairly unsatisfactory
forward difference derivativ
I have never found the R symbolic differentiation helpful because my
functions are typically quite complicated, but was prompted by Steve
Ellison's suggestion to try it out in this case:
# reprex (see reprex package)
graphdta <- read.csv( text =
"t,c
0,100
40,78
80,59
120,38
160
> On Apr 6, 2018, at 8:15 AM, David Winsemius wrote:
>
>>
>> On Apr 6, 2018, at 8:03 AM, David Winsemius wrote:
>>
>>
>>> On Apr 6, 2018, at 3:43 AM, g l wrote:
>>>
Sent: Friday, April 06, 2018 at 5:55 AM
From: "David Winsemius"
Not correct. You already have `pr
> On Apr 6, 2018, at 8:03 AM, David Winsemius wrote:
>
>
>> On Apr 6, 2018, at 3:43 AM, g l wrote:
>>
>>> Sent: Friday, April 06, 2018 at 5:55 AM
>>> From: "David Winsemius"
>>>
>>>
>>> Not correct. You already have `predict`. It is capale of using the
>>> `newdata` values to do interpola
> On Apr 6, 2018, at 3:43 AM, g l wrote:
>
>> Sent: Friday, April 06, 2018 at 5:55 AM
>> From: "David Winsemius"
>>
>>
>> Not correct. You already have `predict`. It is capale of using the `newdata`
>> values to do interpolation with the values of the coefficients in the model.
>> See:
>>
> coef(graphmodeld)
>(Intercept) graphdata[, 1]
>4.513544204 -0.006820623
>
> This corresponds to the linear model but it is still not understood how this
> helps
> to determine gradients at specific points for the exponential model.
You have fitted log(y) against x. Your fitted linear
> Sent: Friday, April 06, 2018 at 1:44 PM
> From: "Jeff Newmiller"
>
> You did not try my suggestion. You tried David's, which has a leftover
> mistake from your guesses about what the argument to coef should be.
Yes, sorry for the mistake.
coef(graphmodeld)
(Intercept) graphdata[, 1]
4.
You did not try my suggestion. You tried David's, which has a leftover mistake
from your guesses about what the argument to coef should be.
--
Sent from my phone. Please excuse my brevity.
On April 6, 2018 3:30:10 AM PDT, g l wrote:
>> Sent: Friday, April 06, 2018 at 4:53 AM
>> From: "Jeff Newm
> Sent: Friday, April 06, 2018 at 5:55 AM
> From: "David Winsemius"
>
>
> Not correct. You already have `predict`. It is capale of using the `newdata`
> values to do interpolation with the values of the coefficients in the model.
> See:
>
> ?predict
>
The ยง details did not mention interpola
> Sent: Friday, April 06, 2018 at 4:53 AM
> From: "Jeff Newmiller"
> To: "g l"
> coef( graphmodeld )
>
coef(graphmodelp)
Error: $ operator is invalid for atomic vectors
A quick search engine query revealed primarily references to the dollar sign
($) operator which does not seem relevant to th
10 matches
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