Bootstrap for sampling distribution of sample mean

Assessing assumptions

  • Our \(t\)-tests assume normality of variable being tested
  • but, Central Limit Theorem says that normality matters less if sample is “large”
  • in practice “approximate normality” is enough, but how do we assess whether what we have is normal enough?
  • so far, use histogram/boxplot and make a call, allowing for sample size.

What actually has to be normal

  • is: sampling distribution of sample mean
  • the distribution of sample mean over all possible samples
  • but we only have one sample!
  • Idea: assume our sample is representative of the population, and draw samples from our sample (!), with replacement.
  • This gives an idea of what different samples from the population might look like.
  • Called bootstrap, after expression “to pull yourself up by your own bootstraps”.

Packages

library(tidyverse)

Blue Jays attendances

jays$attendance
 [1] 48414 17264 15086 14433 21397 34743 44794 14184 15606 18581 19217 21519
[13] 21312 30430 42917 42419 29306 15062 16402 19014 21195 33086 37929 15168
[25] 17276
  • A bootstrap sample:
s <- sample(jays$attendance, replace = TRUE)
s
 [1] 21195 34743 21312 44794 16402 19014 34743 21195 17264 18581 19014 19217
[13] 34743 19217 14433 15062 16402 15062 34743 15062 15086 15168 15086 48414
[25] 30430
  • It is easier to see what is happening if we sort both the actual attendances and the bootstrap sample:
sort(jays$attendance)
 [1] 14184 14433 15062 15086 15168 15606 16402 17264 17276 18581 19014 19217
[13] 21195 21312 21397 21519 29306 30430 33086 34743 37929 42419 42917 44794
[25] 48414
sort(s)
 [1] 14433 15062 15062 15062 15086 15086 15168 16402 16402 17264 18581 19014
[13] 19014 19217 19217 21195 21195 21312 30430 34743 34743 34743 34743 44794
[25] 48414

Getting mean of bootstrap sample

  • A bootstrap sample is same size as original, but contains repeated values (eg. 15062) and missing ones (42917).
  • We need the mean of our bootstrap sample:
mean(s)
[1] 23055.28
  • This is a little different from the mean of our actual sample:
mean(jays$attendance)
[1] 25070.16
  • Want a sense of how the sample mean might vary, if we were able to take repeated samples from our population.
  • Idea: take lots of bootstrap samples, and see how their sample means vary.

Setting up bootstrap sampling

  • Begin by setting up a dataframe that contains a row for each bootstrap sample. I usually call this column sim. Do just 4 to get the idea:
tibble(sim = 1:4)

Drawing the bootstrap samples

  • Then set up to work one row at a time, and draw a bootstrap sample of the attendances in each row:
tibble(sim = 1:4) %>% 
  rowwise() %>% 
  mutate(sample = list(sample(jays$attendance, replace = TRUE)))
  • Each row of our dataframe contains all of a bootstrap sample of 25 observations drawn with replacement from the attendances.

Sample means

  • Find the mean of each sample:
tibble(sim = 1:4) %>% 
  rowwise() %>% 
  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>%   
  mutate(my_mean = mean(sample))
  • These are (four simulated values of) the bootstrapped sampling distribution of the sample mean.

Make a histogram of them

  • rather pointless here, but to get the idea:
tibble(sim = 1:4) %>% 
  rowwise() %>% 
  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% 
  mutate(my_mean = mean(sample)) %>% 
  ggplot(aes(x = my_mean)) + geom_histogram(bins = 3)  -> g

The (pointless) histogram

g

Now do again with a decent number of bootstrap samples

  • say 1000, and put a decent number of bins on the histogram also:
tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(sample = list(sample(jays$attendance, replace = TRUE))) %>% 
  mutate(my_mean = mean(sample)) %>% 
  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10) -> g

The (better) histogram

g

Comments

  • This is very close to normal
  • The bootstrap says that the sampling distribution of the sample mean is close to normal, even though the distribution of the data is not
  • A sample size of 25 is big enough to overcome the skewness that we saw
  • This is the Central Limit Theorem in practice
  • It is surprisingly powerful.
  • Thus, the \(t\)-test is actually perfectly good here.

Comments on the code 1/2

  • You might have been wondering about this:
tibble(sim = 1:4) %>% 
  rowwise() %>% 
  mutate(sample = list(sample(jays$attendance, replace = TRUE)))

Comments on the code 2/2

  • how did we squeeze all 25 sample values into one cell?
    • sample is a so-called “list-column” that can contain anything.
  • why did we have to put list() around the sample()?
    • because sample produces a collection of numbers, not just a single one
    • the list() signals this: “make a list-column of samples”.

Two samples

  • Assumption: both samples are from a normal distribution.
  • In this case, each sample should be “normal enough” given its sample size, since Central Limit Theorem will help.
  • Use bootstrap on each group independently, as above.

Kids learning to read

ggplot(kids, aes(x=group, y=score)) + geom_boxplot()

Getting just the control group

  • Use filter to select rows where something is true:
kids %>% filter(group=="c") -> controls
controls

Bootstrap these

tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(sample = list(sample(controls$score, replace = TRUE))) %>% 
  mutate(my_mean = mean(sample)) %>% 
  ggplot(aes(x = my_mean)) + geom_histogram(bins = 10)

… and the treatment group:

kids %>% filter(group=="t") -> treats
tibble(sim = 1:1000) %>% 
  rowwise() %>% 
  mutate(sample = list(sample(treats$score, replace = TRUE))) %>% 
  mutate(my_mean = mean(sample)) %>% 
  ggplot(aes(x = my_mean)) + geom_histogram(bins = 15)

Comments

  • sampling distributions of sample means both look pretty normal, though treatment group is a tiny bit left-skewed
  • as we thought, no problems with our two-sample \(t\) at all.