On Sun, 2009-04-05 at 01:13 -0400, jim holtman wrote: > Here is what I get from using 'fitdistr' in R to fit to a lognormal. > The resulting density plot from the distribution seems to be a reason > match to the data. > > > x <- scan() > 1: 0.80010 0.72299 0.69893 0.99597 0.89200 0.69312 0.73613 1.13559 > 9: 0.85009 0.85804 0.73324 1.04826 0.84002 > 14: 1.76330 0.71980 0.89416 0.89450 0.98670 0.83571 0.73833 0.66549 > 22: 0.93641 0.80418 0.95285 0.76876 0.82588 > 27: 1.09394 1.00195 1.14976 0.80008 1.11947 1.09484 0.81494 0.68696 > 35: 0.82364 0.84390 0.71402 0.80293 1.02873 > 40: > Read 39 items > > plot(density(x)) > > library(MASS) > > fitdistr(x, 'lognormal') > meanlog sdlog > -0.13480636 0.19118861 > ( 0.03061468) ( 0.02164785) > > lines(dlnorm(x, -.1348, .1911), col='red')
Hi Jim
I agree with your solution but my plot result not fine.
I obtain same result.
> fitdistr(x, 'lognormal')
meanlog sdlog
-0.13480636 0.19118861
( 0.03061468) ( 0.02164785)
In plot when I use points (blue) and curve (green) the fit o lognormal
and density(data) is fine but when I use lines (red) the fit is bad (in
attach I send a PDF of my output)
Do you know why this happen?
Thanks in advance
--
Bernardo Rangel Tura, M.D,MPH,Ph.D
National Institute of Cardiology
Brazil
P.S. my script is
x <- scan()
0.80010 0.72299 0.69893 0.99597 0.89200 0.69312 0.73613 1.13559
0.85009 0.85804 0.73324 1.04826 0.84002
1.76330 0.71980 0.89416 0.89450 0.98670 0.83571 0.73833 0.66549
0.93641 0.80418 0.95285 0.76876 0.82588
1.09394 1.00195 1.14976 0.80008 1.11947 1.09484 0.81494 0.68696
0.82364 0.84390 0.71402 0.80293 1.02873
require(MASS)
fitdistr(x, 'lognormal')
pdf("adj.pdf")
plot(density(x))
lines(dlnorm(x, -.1348, .1911), col='red')
points(x,dlnorm(x, -.1348, .1911), col='blue')
curve(dlnorm(x, -.1348, .1911), col='green',add=T)
dev.off()
adj.pdf
Description: Adobe PDF document
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