Thank you for the clarification.
I'll share a function I got tomorrow morning.
Regards
On Tue, 27 Nov 2018, 18:38 Bert Gunter, <bgunter.4...@gmail.com> wrote:
> ... 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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