abc.ci(bigcity, corr, conf=c(0.90,0.95))

#parameteres bootstrap
#a klima mukodesi idejere

air.fun <- function(data)
{ ybar <- mean(data$hours)
para <- c(log(ybar),mean(log(data$hours)))
ll <- function(k) {
if (k <= 0) out <- 1e200 # not NA
else out <- lgamma(k)-k*(log(k)-1-para[1]+para[2])
out
}
khat <- nlm(ll,ybar^2/var(data$hours))$estimate
c(ybar, khat)
}
air.rg <- function(data, mle)
# Function to generate random exponential variates. mle will contain
# the mean of the original data
{ out <- data
out$hours <- rexp(nrow(out), 1/mle)
out
}
air.boot <- boot(aircondit, air.fun, R=999, sim="parametric",
ran.gen=air.rg, mle=mean(aircondit$hours))
# The bootstrap p-value can then be approximated by
sum(abs(air.boot$t[,2]-1) > abs(air.boot$t0[2]-1))/(1+air.boot$R)

 0.468 #azaz H0 elfogadhato

air.rg <- function(data, mle)
rexp(length(data), 1/mle)
air.boot <- boot(aircondit$hours, mean, R=999, sim="parametric",
ran.gen=air.rg, mle=mean(aircondit$hours))
plot(air.boot)
# In the difference

####irjunk sajatot
#3 reteg, retegenkent 100-200-300 megf
# mas es mas ve-vel es szorassal
#####

mu=c(10,10+rnorm(1),10+rnorm(1))
sig=c(1,1.5,2)

xdat1=rnorm(100,mu[1],sig[1])
xdat2=rnorm(200,mu[2],sig[2])
xdat3=rnorm(300,mu[3],sig[3])
xdat=c(xdat1,xdat2,xdat3)

m=mean(xdat)

 par(mfrow=c(1,3))
hist(xdat1)
hist(xdat2)
hist(xdat3)
xdat=c(xdat1,xdat2,xdat3)
#boot1
#pop veletlen, fix elemek
N=500
ered=rep(0,times=N)
for (i in 1:N)
{b=trunc(3*runif(3)+1)
ix=rbind(c(1,100),c(101,300),c(301,600))
e=0
for(j in 1:3)
e=c(e,xdat[ix[b[j],1]:ix[b[j],2]])
e=e[2:length(e)]
ered[i]=mean(e)
}
hist(ered)

#boot2
#pop fix, veletlen elemek
N=500
ered2=rep(0,times=N)
for (i in 1:N)
{e=0
for(j in 1:3)
{e=c(e,xdat[ix[j,1]+sample(100*j,replace=T)-1])
e=e[2:length(e)]
}
ered2[i]=mean(e)
}
hist(ered2)

#boot3
#pop veletlen, veletlen elemek
N=500
ered3=rep(0,times=N)
for (i in 1:N)
{b=trunc(3*runif(3)+1)
ix=rbind(c(1,100),c(101,300),c(301,600))
e=0
for(j in 1:3)
{
e=c(e,xdat[ix[b[j],1]+sample(100*b[j],replace=T)-1])
}
e=e[2:length(e)]
ered3[i]=mean(e)
}
hist(ered3)


#szoras-proba
for (i in 1:100)
{mu=c(10,10+rnorm(1),10+rnorm(1))
sig=c(1,1.5,2)

xdat4=rnorm(100,mu[3],sig[3])
xdat3=c(xdat3,xdat4)}
var(xdat3)

#szoras az egeszre
ee=rep(0,times=500)
for (i in 1:500)
{
mu=c(10,10+rnorm(1),10+rnorm(1))
sig=c(1,1.5,2)

xdat1=rnorm(100,mu[1],sig[1])
xdat2=rnorm(200,mu[2],sig[2])
xdat3=rnorm(300,mu[3],sig[3])
xdat=c(xdat1,xdat2,xdat3)
ee[i]=mean(xdat)
}
par(mfrow=c(1,1))
hist(ee)

m=mean(xdat)