I'm having problems generating a QQ plot for a fitted distrubution. The data is fitted by
NormalFit <- fitdistr(obs, densfun="normal")
where obs are the observations.
I thought I can just do
qqnorm(NormalFit)
I want to do the same for
LogNormalFit <- fitdistr(obs, densfun="log-normal")
the fit only gives you the inferred parameters, so you need to sample from that, to give you the qqplot. See below for 3 examples on how to do it.
library(MASS)
set.seed(999)
par(mfrow=c(1,3))
#simulate data
obs = rnegbin(500, mu = 5, theta = 4)
#fit data
NormalFit <- fitdistr(obs, densfun="normal")
#plot
qqnorm(obs,main="qqnorm")
# generate theoretical quantiles
Theo_Quantile = qnorm(ppoints(length(obs)))
qqplot(Theo_Quantile,obs,main="ppoint")
# it's also similar to if you just sample from the fitted distribution
Fitted = rnorm(length(obs),NormalFit$estimate[1],NormalFit$estimate[2])
qqplot(Fitted,obs,main="Sampled from fitted")
