> On Apr 6, 2018, at 8:15 AM, David Winsemius <dwinsem...@comcast.net> wrote: > >> >> On Apr 6, 2018, at 8:03 AM, David Winsemius <dwinsem...@comcast.net> wrote: >> >> >>> On Apr 6, 2018, at 3:43 AM, g l <gnuli...@gmx.com> wrote: >>> >>>> Sent: Friday, April 06, 2018 at 5:55 AM >>>> From: "David Winsemius" <dwinsem...@comcast.net> >>>> >>>> >>>> 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 interpolation explicity; thanks. >>> >>>> The original question asked for a derivative (i.e. a "gradient"), but so >>>> far it's not clear that you understand the mathematical definiton of that >>>> term. We also remain unclear whether this is homework. >>>> >>> >>> The motivation of this post was simple differentiation of a tangent point >>> (dy/dx) manually, then wondering how to re-think in modern-day computing >>> terms. Hence the original question about asking the appropriate >>> functions/syntax to read further ("curiosity"), not the answer (indeed, >>> "homework"). :) >>> >>> Personal curiosity should be considered "homework". >> >> Besides symbolic differentiation, there is also the option of numeric >> differentiation. Here's an amateurish attempt: >> >> myNumDeriv <- function(x){ (exp( predict (graphmodeld, >> newdata=data.frame(t=x+.0001))) - >> exp( predict (graphmodeld, >> newdata=data.frame(t=x) )))/ >> .0001 } >> myNumDeriv(c(100, 250, 350)) > > I realized that this would not work in the context of your construction. I > had earlier made a more symbolic version using R formulae: > > graphdata<-read.csv(text='t,c > 0,100 > 40,78 > 80,59 > 120,38 > 160,25 > 200,21 > 240,16 > 280,12 > 320,10 > 360,9 > 400,7') > graphmodeld<-lm(log(c)~t, graphdata) > graphmodelp<-exp(predict(graphmodeld)) > plot(c~t, graphdata) > lines(graphdata[,1],graphmodelp)

Again I attempt to correct my incomplete code with this definition that had be modified to take a model object as its second argument: myNumDeriv <- function(x, mod) (exp( predict (mod, newdata=data.frame(t=x+.0001))) - exp( predict (mod, newdata=data.frame(t=x) )))/.0001 > myNumDeriv(c(100, 250, 350), graphmodeld ) > #---------------------------------------------- > 1 2 3 > -0.31464102 -0.11310753 -0.05718414 > > >> >> >> >> David Winsemius >> Alameda, CA, USA >> >> 'Any technology distinguishable from magic is insufficiently advanced.' >> -Gehm's Corollary to Clarke's Third Law >> >> ______________________________________________ >> R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >> https://stat.ethz.ch/mailman/listinfo/r-help >> PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >> and provide commented, minimal, self-contained, reproducible code. > > David Winsemius > Alameda, CA, USA > > 'Any technology distinguishable from magic is insufficiently advanced.' > -Gehm's Corollary to Clarke's Third Law > > ______________________________________________ > R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. David Winsemius Alameda, CA, USA 'Any technology distinguishable from magic is insufficiently advanced.' -Gehm's Corollary to Clarke's Third Law ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.