Bootstrap for Gama Distribution , R

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alfa = c()
beta= c()

set.seed(1839)
for(i in 1:1000){
alfa[i] = sample(1:1000,1)
beta[i]= sample(1:1000,1)
}

set.seed(717)
pop = c()
 for(i in 1:1000){
pop[i]= rgamma(1,alfa[i],beta[i])
}

amostra = sample(pop,200,replace=F)

bot <- function(B,data,type = "media",quant = 0.95){
if(type=="media"){
r = B; dados = data; n= length(dados)
vm = c()
for(i in 1:r){
s = sample(dados,n,replace=T)
vm[i]=mean(s)
}
return(vm)
}

if(type=="mediana"){
r = B; dados = data; n= length(dados)
vm = c()
for(i in 1:r){
s = sample(dados,n,replace=T)
vm[i]=median(s)
}
return(vm)
}

if(type=="quantil"){
r = B; dados=data; n= length(dados);q = quant
vq= c()
for(i in 1:r){
s = sample (dados,n,replace=T)
vq[i] = quantile(s,q)
}
return(vq)
}

}

set.seed(1729)
ream= bot(10000, amostra,"media")

plot(5,5, ann = F, axes = T, type = "n",xlim=c(0,20),ylim=c(0,0.9))
lines(density (pop),col="red",lwd=1.5)
lines(density (amostra),col="blue",lwd=1.5)
lines(density (ream),col="darkgreen",lwd=1.5)
points(mean(pop),0,col="red",pch=2,cex=0.3)
points(mean(ream),0,col="darkgreen",pch=2,cex=0.3)

med.pop= mean(pop)
med.am= mean(amostra)
med.ream= mean(ream)
rbind(med.pop,med.am,med.ream)

mpop   = mean(pop)
mam    = mean(amostra)
mream = mean(ream)
rbind(mpop,mam,mream)

q.pop  = quantile (pop,0.95)
q.am    = quantile (amostra,0.95)
q.ream = quantile(ream,0.95)

rbind(q.pop,q.am,q.ream)

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Absolute running time: 0.83 sec, cpu time: 1.22 sec, memory peak: 51 Mb, absolute service time: 1,08 sec