If you're interested in comparing empirical to simulation
distributions, I see two alternatives to your density() approach
(which will be sensitive to your choice of bandwidth). Both of the
following have been used in my field to look at the fit of empirical
response time data to models of human information processing:
(1) estimate some number of quantiles for each set of 1000 replicates,
average the quantile points, then compare the results to a similar set
of quantiles generated from your simulation data.
(2) fit an a priori distribution to each set of 1000 replicates,
within each distribution parameter, average across sets, then compare
to parameters obtained from fitting the simulation data.
#generate some fake simulation data
sim.x = rnorm(1e5,100,20)
#make up some fake empirical data
a=data.frame(
set = rep(1:8,each=1e3)
,x=rnorm(8*1e3,100,20)+rexp(8*1e3,50)
)
#define a function to compute the geometric mean
##(fails with negative numbers!)
geometric.mean = function(x) exp(mean(log(x)))
########
# Quantile approach
########
#set up a data frame to collect empirical quantile data
emp.q=expand.grid(
set=unique(a$set)
,q=seq(.1,.9,.1)
,x=NA
)
#estimate empirical quantiles
for(set in unique(a$set)){
emp.q$x[emp.q$set==set] =
quantile(a$x[a$set==set],probs=unique(emp.q$q))
}
#compute the geomentric mean for each empirical quantile
emp.q.means = with(
emp.q
,aggregate(
x
,list(
q=q
)
,geometric.mean
)
)
#estimate the quantiles from the simulation data
sim.q = quantile(sim.x,unique(emp.q$q))
#assess the fit using sum squared scaled error
quantile.sum.sq.sc.err = sum(((emp.q.means$x-sim.q)/sim.q)^2)
print(quantile.sum.sq.sc.err)
########
# Fitting approach using the Generalized Lambda distribution
## GLD chosen because it's flexible and easily fit using the
### gld package
########
#install the gld package if necessary
if(!('gld' %in% installed.packages())){
install.packages('gld')
}
#load the gld package
library(gld)
#set up a data frame to collect empirical GLD parameters
emp.gld.par=expand.grid(
set=unique(a$set)
,lambda=1:4
,x=NA
)
#estimate empirical GLD parameters
## print-out keeps an eye on convergence (hopefully 0)
## takes a while to complete
for(set in unique(a$set)){
fit = starship(a$x[a$set==set])
cat('Set:',set,', convergence:',fit$optim.results$convergence,'\n')
emp.gld.par$x[emp.gld.par$set==set] = fit$lambda
}
#compute the geomentric mean for each empirical GLD parameter
emp.gld.par.means = with(
emp.gld.par
,aggregate(
x
,list(
lambda=lambda
)
,geometric.mean
)
)
#estimate the GLD parameters from the simulation data
sim.fit = starship(sim.x)
cat('Sim convergence:',sim.fit$optim.results$convergence,'\n')
sim.gld.par = sim.fit$lambda
#assess the fit using sum squared scaled error
gld.par.sum.sq.sc.err = sum(((emp.gld.par.means$x-sim.gld.par)/sim.gld.par)^2)
print(gld.par.sum.sq.sc.err)
--
Mike Lawrence
Graduate Student
Department of Psychology
Dalhousie University
Looking to arrange a meeting? Check my public calendar:
http://tinyurl.com/mikes-public-calendar
~ Certainty is folly... I think. ~
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