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$estimate
p <- pretty(range(data))
x <- seq(from = min(p), to = max(p), by = 0.1)
hist(data, freq = FALSE)
lines(x, y = dlnorm(x,
meanlog = f$estimate["meanlog"],
sdlog = f$estimate["sdlog"])
)
Hope this helps,
Rui Barradas
Às 13:12 de 21/01/21, Marc Girondot via R-help escreveu:
Two solutions not exactly equivalent ;
data <- rlnorm(100, meanlog = 1, sdlog = 1)
histdata <- hist(data, ylim=c(0, 100))
library(MASS)
f <- fitdistr(data, "lognormal")
f$estimate
lines(x=seq(from=0, to=50, by=0.1),
� y=dlnorm(x=seq(from=0, to=50, by=0.1), meanlog =
f$estimate["meanlog"], sdlog = f$estimate["sdlog"])*400
)
library(HelpersMG)
m <- modeled.hist(breaks=histdata$breaks, FUN=plnorm,
����������������������������� meanlog = f$estimate["meanlog"], sdlog =
f$estimate["sdlog"], sum = 100)
points(m$x, m$y, pch=19, col="red")
Marc Girondot
Le 21/01/2021 � 12:54, Eric Leroy a �crit�:
Hi,
I would like to plot the histogram of data and fit it with a lognormal
distribution.
The ideal, would be to superimpose the fit on the histogram and write
the results of the fit on the figure.
Right now, I was able to plot the histogram and fit the density with a
lognormal, but I can't combine all together.
Here is the code I wrote :
histdata <- hist(dataframe$data)
library(MASS)
fitdistr(histdata$density, "lognormal")
Can you help me ?
Best regards,
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and provide commented, minimal, self-contained, reproducible code.
______________________________________________
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.
