Have a look at the dnorm function:
?dnorm
Notes:
(1) You'd be better to say estimate (or fit) parameters (or
coefficients) and plot the resulting normal distribution (or normal
density), rather than say fit a density curve, because someone may
think you want a kernel density estimate with a Gaussian kernel.
(2) Densities integrate to one by definition. i.e. If a function
didn't integrate to one it wouldn't be a probability density function,
but still could be a function representing a probability
distribution...
On Sun, Apr 26, 2020 at 7:09 AM varin sacha via R-help
<r-help@r-project.org> wrote:
>
> Dear R-experts,
>
> I am trying to fit a gaussian density curve. More precisely, I would like to
> obtain the fitted "Gaussian curve".
> "m" is the gaussian mean, "sd" is the standard deviation and "k" is an
> arbitrary scaling parameter (since the gaussian density is constrained to
> integrate to 1, whereas my data isn't).
> There is no error message. In my opinion, the last command of my reproducible
> R script is not doing its job !
>
> Many thanks for your lights.
>
> ############################################################
> a <- as.Date(c("2020-02-25",
> "2020-02-26","2020-02-27","2020-02-28","2020-03-1","2020-03-2","2020-03-3","2020-03-4","2020-03-5","2020-03-6","2020-03-7","2020-03-8","2020-03-9","2020-03-10","2020-03-11","2020-03-12","2020-03-13","2020-03-14","2020-03-15","2020-03-16"))
>
> b<-
> c(20,28,45,68,89,123,154,190,245,302,460,379,298,300,245,189,165,100,90,78)
>
> d <-as.numeric(a)
>
> m<-11.84
>
> sd<-3.93
>
> k<-359.77
>
> plot(b~d)
>
> plot(function(d) k*exp(-0.5*(d-m)^2/sd^2),col=2,add=TRUE,xlim=range(d))
> ############################################################
>
>
> ______________________________________________
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