r:sampling_distribution:code.01
rm(list=ls())
rnorm2 <- function(n,mean,sd){
mean+sd*scale(rnorm(n))
}
se <- function(sample) {
sd(sample)/sqrt(length(sample))
}
ss <- function(x) {
sum((x-mean(x))^2)
}
################################
N.p <- 1000000
m.p <- 100
sd.p <- 10
p1 <- rnorm2(N.p, m.p, sd.p)
mean(p1)
sd(p1)
p2 <- rnorm2(N.p, m.p+20, sd.p)
mean(p2)
sd(p2)
m.p1 <- mean(p1)
sd.p1 <- sd(p1)
var(p1)
hist(p1, breaks=50, col = rgb(1, 1, 1, 0.5),
main = "histogram of p1 and p2",)
abline(v=mean(p1), col="black", lwd=3)
hist(p2, add=T, breaks=50, col=rgb(1,1,.5,.5))
abline(v=mean(p2), col="red", lwd=3)
hist(p1, breaks=50, col=rgb(0,.5,.5,.5))
abline(v=mean(p1),lwd=2)
abline(v=mean(p1)-sd(p1), lwd=2)
abline(v=mean(p1)+sd(p1), lwd=2)
abline(v=c(m.p1-2*sd.p1, m.p1+2*sd.p1), lwd=2, col='red')
abline(v=c(m.p1-3*sd.p1, m.p1+3*sd.p1), lwd=2, col='green')
# area bet black = 68%
# between red = 95%
# between green = 99%
pnorm(m.p1+sd.p1, m.p1, sd.p1)
pnorm(m.p1-sd.p1, m.p1, sd.p1)
pnorm(m.p1+sd.p1, m.p1, sd.p1) -
pnorm(m.p1-sd.p1, m.p1, sd.p1)
pnorm(m.p1+2*sd.p1, m.p1, sd.p1) -
pnorm(m.p1-2*sd.p1, m.p1, sd.p1)
pnorm(m.p1+3*sd.p1, m.p1, sd.p1) -
pnorm(m.p1-3*sd.p1, m.p1, sd.p1)
m.p1
sd.p1
(m.p1-m.p1)/sd.p1
((m.p1-sd.p1) - m.p1) / sd.p1
(120-100)/10
pnorm(1)-pnorm(-1)
pnorm(2)-pnorm(-2)
pnorm(3)-pnorm(3)
1-pnorm(-2)*2
pnorm(2)-pnorm(-2)
pnorm(120, 100, 10)
pnorm(2)-pnorm(-2)
zscore <- (120-100)/10
pnorm(zscore)-pnorm(-zscore)
zscore
# reminder.
pnorm(-1)
pnorm(1, 0, 1, lower.tail = F)
pnorm(110, 100, 10, lower.tail = F)
zscore <- (110-100)/10
pnorm(zscore, lower.tail = F)
pnorm(118, 100, 10, lower.tail = F)
pnorm(18/10, lower.tail = F)
z.p1 <- (p1-mean(p1))/sd(p1)
mean(z.p1)
round(mean(z.p1),10)
sd(z.p1)
pnorm(1.8)-pnorm(-1.8)
hist(z.p1, breaks=50, col=rgb(1,0,0,0))
abline(v=c(m.p1, -1.8, 1.8), col='red')
1-(pnorm(1.8)-pnorm(-1.8))
pnorm(1)-pnorm(-1)
1-(pnorm(-1)*2)
pnorm(2)-pnorm(-2)
1-(pnorm(-2)*2)
1-(pnorm(-3)*2)
#
hist(p1, breaks=50, col=rgb(.9,.9,.9,.9))
abline(v=mean(p1),lwd=2)
abline(v=mean(p1)-sd(p1), lwd=2)
abline(v=mean(p1)+sd(p1), lwd=2)
abline(v=c(m.p1-2*sd.p1, m.p1+2*sd.p1), lwd=2, col='red')
abline(v=c(m.p1-3*sd.p1, m.p1+3*sd.p1), lwd=2, col='green')
# 68%
a <- qnorm(.32/2)
b <- qnorm(1-.32/2)
c(a, b)
c(-1, 1)
# note that
.32/2
pnorm(-1)
qnorm(.32/2)
qnorm(pnorm(-1))
# 95%
c <- qnorm(.05/2)
d <- qnorm(1-.05/2)
c(c, d)
c(-2,2)
# 99%
e <- qnorm(.01/2)
f <- qnorm(1-.01/2)
c(e,f)
c(-3,3)
pnorm(b)-pnorm(a)
c(a, b)
pnorm(d)-pnorm(c)
c(c, d)
pnorm(f)-pnorm(e)
c(e, f)
qnorm(.5)
qnorm(1)
################################
s.size <- 10
means.temp <- c()
s1 <- sample(p1, s.size, replace = T)
mean(s1)
means.temp <- append(means.temp, mean(s1))
means.temp <- append(means.temp, mean(sample(p1, s.size, replace = T)))
means.temp <- append(means.temp, mean(sample(p1, s.size, replace = T)))
means.temp <- append(means.temp, mean(sample(p1, s.size, replace = T)))
means.temp <- append(means.temp, mean(sample(p1, s.size, replace = T)))
means.temp
iter <- 1000000
# means <- c()
means <- rep(NA, iter)
for (i in 1:iter) {
# means <- append(means, mean(sample(p1, s.size, replace = T)))
means[i] <- mean(sample(p1, s.size, replace = T))
}
length(means)
mean(means)
sd(means)
se.s <- sd(means)
hist(means, breaks=50,
xlim = c(mean(means)-5*sd(means), mean(means)+10*sd(means)),
col=rgb(1, 1, 1, .5))
abline(v=mean(means), col="black", lwd=3)
# now we want to get sd of this distribution
lo1 <- mean(means)-se.s
hi1 <- mean(means)+se.s
lo2 <- mean(means)-2*se.s
hi2 <- mean(means)+2*se.s
lo3 <- mean(means)-3*se.s
hi3 <- mean(means)+3*se.s
abline(v=mean(means), col="black", lwd=2)
# abline(v=mean(p2), colo='darkgreen', lwd=2)
abline(v=c(lo1, hi1, lo2, hi2, lo3, hi3),
col=c("red","red", "blue", "blue", "orange", "orange"),
lwd=2)
# meanwhile . . . .
se.s
se.z <- sqrt(var(p1)/s.size)
se.z <- c(se.z)
se.z
# sd of sample means (sd(means))
# = se.s
# when iter value goes to
# infinite value:
# mean(means) = mean(p1)
# and
# sd(means) = sd(p1) / sqrt(s.size)
# that is, se.s = se.z
# This is called CLT (Central Limit Theorem)
# see http://commres.net/wiki/cetral_limit_theorem
mean(means)
mean(p1)
sd(means)
var(p1)
# remember we started talking sample size 10
sqrt(var(p1)/s.size)
se.z
sd(means)
se.s
se.z
# because CLT
loz1 <- mean(p1)-se.z
hiz1 <- mean(p1)+se.z
loz2 <- mean(p1)-2*se.z
hiz2 <- mean(p1)+2*se.z
loz3 <- mean(p1)-3*se.z
hiz3 <- mean(p1)+3*se.z
c(lo1, loz1)
c(lo2, loz2)
c(lo3, loz3)
c(hi1, hiz1)
c(hi2, hiz2)
c(hi3, hiz3)
hist(means, breaks=50,
xlim = c(mean(means)-5*sd(means), mean(means)+10*sd(means)),
col = rgb(1, 1, 1, .5))
abline(v=mean(means), col="black", lwd=3)
# abline(v=mean(p2), colo='darkgreen', lwd=3)
abline(v=c(lo1, hi1, lo2, hi2, lo3, hi3),
col=c("darkgreen","darkgreen", "blue", "blue", "orange", "orange"),
lwd=2)
round(c(lo1, hi1))
round(c(lo2, hi2))
round(c(lo3, hi3))
round(c(loz1, hiz1))
round(c(loz2, hiz2))
round(c(loz3, hiz3))
m.sample.i.got <- mean(means)+ 1.5*sd(means)
m.sample.i.got
hist(means, breaks=30,
xlim = c(mean(means)-7*sd(means), mean(means)+10*sd(means)),
col = rgb(1, 1, 1, .5))
abline(v=mean(means), col="black", lwd=3)
abline(v=m.sample.i.got, col='darkgreen', lwd=3)
# what is the probablity of getting
# greater than
# m.sample.i.got?
m.sample.i.got
pnorm(m.sample.i.got, mean(means), sd(means), lower.tail = F)
pnorm(m.sample.i.got, mean(p1), se.z, lower.tail = F)
# then, what is the probabilty of getting
# greater than m.sample.i.got and
# less than corresponding value, which is
# mean(means) - m.sample.i.got - mean(means)
# (green line)
tmp <- mean(means) - (m.sample.i.got - mean(means))
abline(v=tmp, col='red', lwd=3)
2 * pnorm(m.sample.i.got, mean(p1), sd(means), lower.tail = F)
m.sample.i.got
### one more time
# this time, with a story
mean(p2)
sd(p2)
sample(p2, s.size)
m.sample.i.got <- mean(sample(p2, s.size))
m.sample.i.got
tmp <- mean(means) - (m.sample.i.got-mean(means))
tmp
hist(means, breaks=30,
xlim = c(tmp-4*sd(means), m.sample.i.got+4*sd(means)),
col = rgb(1, 1, 1, .5))
abline(v=mean(means), col="black", lwd=3)
abline(v=m.sample.i.got, col='blue', lwd=3)
# what is the probablity of getting
# greater than
# m.sample.i.got?
m.sample.i.got
pnorm(m.sample.i.got, mean(p1), sd(means), lower.tail = F)
# then, what is the probabilty of getting
# greater than m.sample.i.got and
# less than corresponding value, which is
# mean(means) - m.sample.i.got - mean(means)
# (green line)
abline(v=tmp, col='red', lwd=3)
2 * pnorm(m.sample.i.got, mean(p1), sd(means), lower.tail = F)
r/sampling_distribution/code.01.txt · Last modified: by hkimscil