... but do note that a nonlinear fit to the raw data will give a(somewhat)
different result than a linear fit to the transformed data. In the former,
the errors are additive and in the latter they are multiplicative. Etc.
Cheers,
Bert
Bert Gunter
"The trouble with having an open mind is that people keep coming along and
sticking things into it."
-- Opus (aka Berkeley Breathed in his "Bloom County" comic strip )
On Tue, Nov 27, 2018 at 9:11 AM Sarah Goslee <sarah.gos...@gmail.com> wrote:
> Hi,
>
> Please also include R-help in your replies - I can't provide
> one-on-one tutorials.
>
> Without knowing where you got your sample code, it's hard to help. But
> what are you trying to do?
>
> It doesn't have to be that complicated:
>
> x <- 1:10
> y <- c(0.00, 0.00,0.0033,0.0009,0.0025,0.0653,0.1142,0.2872,0,1 )
> plot(x, y, pch=20)
>
> # basic straight line of fit
> fit <- glm(y~x)
>
> abline(fit, col="blue", lwd=2)
> exp.lm <- lm(y ~ exp(x))
> lines(1:10, predict(exp.lm, newdata=data.frame(x=1:10)))
>
>
> On Tue, Nov 27, 2018 at 9:34 AM Tolulope Adeagbo
> <tolulopeadea...@gmail.com> wrote:
> >
> > Hello,
> >
> > So I found this example online but there seems to be an issue with the
> "Start" points. the result is giving somewhat a straight line
> >
> > # get underlying plot
> > x <- 1:10
> > y <- c(0.00, 0.00,0.0033,0.0009,0.0025,0.0653,0.1142,0.2872,0,1 )
> > plot(x, y, pch=20)
> >
> > # basic straight line of fit
> > fit <- glm(y~x)
> > co <- coef(fit)
> > abline(fit, col="blue", lwd=2)
> >
> > # exponential
> > f <- function(x,a,b) {a * exp(b * x)}
> > fit <- nls(y ~ f(x,a,b), start = c(a=1 , b=c(0,1)))
> > co <- coef(fit)
> > curve(f(x, a=co[1], b=co[2]), add = TRUE, col="green", lwd=2)
> >
> >
> > # exponential
> > f <- function(x,a,b) {a * exp(b * x)}
> > fit <- nls(y ~ f(x,a,b), start = c(a=1, b=1))
> > co <- coef(fit)
> > curve(f(x, a=co[1], b=co[2]), add = TRUE, col="green", lwd=2)
> > # logarithmic
> > f <- function(x,a,b) {a * log(x) + b}
> > fit <- nls(y ~ f(x,a,b), start = c(a=1, b=1))
> > co <- coef(fit)
> > curve(f(x, a=co[1], b=co[2]), add = TRUE, col="orange", lwd=2)
> >
> > # polynomial
> > f <- function(x,a,b,d) {(a*x^2) + (b*x) + d}
> > fit <- nls(y ~ f(x,a,b,d), start = c(a=1, b=1, d=1))
> > co <- coef(fit)
> > curve(f(x, a=co[1], b=co[2], d=co[3]), add = TRUE, col="pink", lwd=2)
> >
> > On Tue, Nov 27, 2018 at 12:28 PM Sarah Goslee <sarah.gos...@gmail.com>
> wrote:
> >>
> >> Hi,
> >>
> >> Using rseek.org to search for exponential regression turns up lots of
> information, as does using Google.
> >>
> >> Which tutorials have you worked thru already, and what else are you
> looking for?
> >>
> >> Sarah
> >>
> >> On Tue, Nov 27, 2018 at 5:44 AM Tolulope Adeagbo <
> tolulopeadea...@gmail.com> wrote:
> >>>
> >>> Good day,
> >>> Please i nee useful materials to understand how to use R for
> exponential
> >>> regression.
> >>> Many thanks.
> >>
>
>
> --
> Sarah Goslee (she/her)
> http://www.numberwright.com
>
> ______________________________________________
> 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.
>
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