On 01/10/14 08:50, eliza botto wrote:
Dear useRs,
I have this following data
dput(Prec)
c(42.2, 45.2, 46, 48, 54, 54.1, 59.4, 61, 62.2, 63.5, 65.024, 71.9, 73.4, 76.6,
76.708, 77.5, 77.724, 78, 81.3, 84.7, 84.836, 85.09, 88.2, 91.4, 94, 95.8, 96,
97.3, 101, 101, 101.5, 102.3, 102.87, 108.7, 109.5, 110.5, 110.7, 112, 114.3,
118.11, 121.412, 128.1, 131, 140, 142, 143.3, 151.4, 153.7, 189.4, 214.3)
I want to fit gumbel and log-normal distribution on it on the same window to
see which distribution fits it the best way.
Thankyou very much in advance.
If I understand you correctly the attached script should do what you want.
cheers,
Rolf Turner
--
Rolf Turner
Technical Editor ANZJS
#
# Script botto.txt
#
require(MASS)
require(FAdist)
x <- c(42.2, 45.2, 46, 48, 54, 54.1, 59.4, 61, 62.2, 63.5, 65.024,
71.9, 73.4, 76.6, 76.708, 77.5, 77.724, 78, 81.3, 84.7, 84.836,
85.09, 88.2, 91.4, 94, 95.8, 96, 97.3, 101, 101, 101.5, 102.3,
102.87, 108.7, 109.5, 110.5, 110.7, 112, 114.3, 118.11, 121.412,
128.1, 131, 140, 142, 143.3, 151.4, 153.7, 189.4, 214.3)
dx <- density(x)
s <- sd(x)
xbar <- mean(x)
beta.hat <- 6*s/pi
mu.hat <- xbar - 0.5772*beta.hat
fit.g <-
suppressWarnings(fitdistr(x,dgumbel,start=list(location=mu.hat,scale=beta.hat)))
fit.ln <- fitdistr(x,"lognormal")
cg <- fit.g$estimate
cln <- fit.ln$estimate
plot(dx,xlab="x",ylab="density",main="Comparison of fits",ylim=c(0,0.015))
plot(function(x){dgumbel(x,location=cg[1],scale=cg[2])},from=0,to=250,add=TRUE,col="red")
plot(function(x){dlnorm(x,meanlog=cln[1],sdlog=cln[2])},from=0,to=250,add=TRUE,col="blue")
legend("topright",lty=1,col=c("black","red","blue"),
legend=c("non-parametric density","fitted Gumbel density",
"fitted log normal density"))
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