OK, but that's not the point of my comment.
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 Fri, Jan 22, 2021 at 5:03 PM Abby Spurdle wrote:
> Sorry, Bert.
>
Sorry, Bert.
The fitdistr function estimates parameters via maximum likelihood.
(i.e. The "lognormal" part of this, is not a kernel).
On Fri, Jan 22, 2021 at 5:14 AM Bert Gunter wrote:
>
> In future, you should try to search before posting. I realize that getting
> good search terms can sometime
Hello,
A solution based on Marc's first one, maybe easier? (It doesn't rely on
multiplying the dlnorm values by 400 since it plots the histogram with
freq = FALSE.)
set.seed(2020)
data <- rlnorm(100, meanlog = 1, sdlog = 1)
library(MASS)
f <- fitdistr(data, "lognormal")
f$estimate
p <- pr
In future, you should try to search before posting. I realize that getting
good search terms can sometimes be tricky, but not in this case: 'plot
density with histogram in R' or similar on rseek.org or just on your usual
search platform brought up several ways to do it.
As a (slightly offtopic) si
Two solutions not exactly equivalent ;
data <- rlnorm(100, meanlog = 1, sdlog = 1)
histdata <- hist(data, ylim=c(0, 100))
library(MASS)
f <- fitdistr(data, "lognormal")
f$estimate
lines(x=seq(from=0, to=50, by=0.1),
� y=dlnorm(x=seq(from=0, to=50, by=0.1), meanlog =
f$estimate["meanlog"], sdl
Hi,
I would like to plot the histogram of data and fit it with a lognormal
distribution.
The ideal, would be to superimpose the fit on the histogram and write
the results of the fit on the figure.
Right now, I was able to plot the histogram and fit the density with a
lognormal, but I can't
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