To do each example, just click the "Submit" button. You do not have to type in any R instructions or specify a dataset. That's already done for you.
library(bootstrap) says we are going to
use code in the bootstrap library, which is not available
without this command.
In this example, it is a fair guess that the name of the data set may
have something to do with mice. The only ones on the list that look
like that are mouse.c and mouse.t.
Since the actual help pages for the data sets are completely useless,
saying only See Efron and Tibshirani (1993) for details on these
datasets
but not giving a page number or anything else of the slightest
help in identifying which data set it is, the only way to find out whether
these really are the data sets we want is to look at them. For example,
library(bootstrap) data(mouse.c) print(mouse.c)prints
mouse.c and, sure enough, these are the 9 numbers
in the control group for the mouse data.
data statement, as in the example.
for loop makes nboot bootstrap samples
x.star and calculates for each the statistical functional of
interest theta.star[i], which in this example is the mean.
hist(theta.star) abline(v = theta.hat, lty = 2)does. The vertical dotted line added by the second statement is at
theta.hat.
This plot shows whether the distribution is normal or not and whether the estimator is biased or not.
This example differs from the preceding one only in the estimator being a 15% trimmed mean rather than the ordinary mean. Everything else is the same.
mean function from the preceding example
is replaced by the foo function, which calculates
the 15% trimmed mean. Everything else is the same, except the
last statement in the preceding example, which gives the
theoretical standard error of the ordinary sample mean is irrelevant here,
because even if we knew a lot of theory we still couldn't give a useful
theoretical standard error for a 15% trimmed mean (or most other complicated
estimators).
library and data statements.
law data set for this example is an R data frame.
In the printout it looks like a matrix, and matrix notation can be
used to extract the variables. The notation law[ , 2]
indicates column 2 of the law data frame.
sample statement in this example does something
a little different from the mice example above.
The problem is that we don't have a univariate random variable to sample. We need to sample (with replacement from the original sample) pairs (Xk, Yk).
We do that by sampling random indices. The vector k.star
produced by the sample statement is a random sample with
replacement from the numbers 1, 2, . . ., n. When we use such
a vector as an index to another vector, it pulls out the elements indicated.
The two statements
k.star <- sample(n, replace = TRUE) x.star <- x[k.star]does exactly the same thing as the single statement from the mice example
x.star <- sample(x, replace = TRUE)But (a very big but) the three statements from this example
k.star <- sample(n, replace = TRUE) x.star <- x[k.star] y.star <- y[k.star]do something very different from the two statements
x.star <- sample(x, replace = TRUE) y.star <- sample(y, replace = TRUE)that a naive person might think would be the way to resample with replacement from the data.
The point is that the wrong thing
(using two sample statements) produces independent
samples x.star and y.star. We don't have
to use the bootstrap to know their correlation is exactly zero!
The right thing
(using just one sample statement and an auxiliary index
vector k.star) produces
samples x.star and y.star that have the same
pairing they had in the original data. A pair
(x.star[i], y.star[i])
are a pair
(x[j], y[j])
from the original data (for some j).
No resample with replacement from the original data
.
Instead we simulate from the parametric model that is assumed
for the data. This is still a bootstrap
rather than pure simulation
because the estimated parameter value is not the true unknown parameter value.
So we still have a sample is not the population
issue.
mvrnorm function in the MASS library
(
on-line help) simulates multivariate
normal random vectors. A multivariate normal distribution is determined
by two structuredparameters, the mean vector and the variance matrix (also called
covariancematrix,
variance-covariancematrix, and
dispersionmatrix).
But the correlation coefficient is invariant under changes of location and scale. Thus the mean vector does not affect the distribution of the sample correlation coefficient. Nor do scale changes. Hence we can use the correlation matrix instead of the variance matrix without affecting the distribution of the sample correlation coefficient. The code
print(cor.mat <- cor(law))calculates and prints the sample correlation matrix. It has ones on the diagonal and the sample correlation coefficient as its off-diagonal elements. That's why
rho.hat is
cor.mat[1, 2].
In the real world
, the matrix cor.mat is the
sample correlation matrix and its [1, 2] element is
the plug-in estimate of the parameter of interest.
In the bootstrap world
, the matrix cor.mat is
(treated as) the population correlation matrix and its
[1, 2] element is (treated as) the parameter of interest.
The statement
law.star <- mvrnorm(n, c(0, 0), cor.mat)produces a random sample from the parametric model with parameter
rho.hat.
The two statements following calculate rho.star[i]
from the (parametric) bootstrap data law.star in exactly
the same way rho.hat is calculated from the original data
law.
rho.star
stored. Just transform it to find the sampling distribution of z.
z <- 0.5 * log((1 + rho) / (1 - rho))has a name inverse hyperbolic tangent and a standard R function to calculate it
z <- atanh(rho)which has an inverse function hyperbolic tangent
rho <- tanh(z)
Because of the skewness of the distribution of rho.hat
(as shown by the skewness of the histogram of rho.star)
rho.hat + c(-1, 1) * qnorm(0.975) * sd(rho.star)is not a very good 95% confidence interval for the true unknown parameter rho. (Point estimate plus or minus 1.96 standard errors of the point estimate assumes normality or at least approximate normality and here we aren't anywhere close to normality.)
Because of the approximate normality of the distribution of z.hat
(as shown by the approximate normality of the histogram of z.star)
z.hat + c(-1, 1) * qnorm(0.975) * sd(z.star)is a pretty good 95% confidence interval for the true unknown parameter
zeta = atanh(rho). Hence
tanh(z.hat + c(-1, 1) * qnorm(0.975) * sd(z.star))is a pretty good 95% confidence interval for the true unknown parameter rho.
The Moral of the Story. Work on a parameter that has an approximately normal estimator. If the original parameter of interest (here rho) doesn't have an approximately normal estimator, then change parameters to one (here zeta) that does. Then transform back to get a c. i. for the original parameter.
We will return to this theme when we discuss better
bootstrap
confidence intervals (Chapters 14 and 22 in Efron and Tibshirani).
library and data statements.
for loop and we save all the junk
we want to know about in five data structures
eigenval.1
eigenval.2
eigenval.sum
eigenvec.1
eigenvec.2
scor,
we do the sampling in two steps
k.star <- sample(n, replace = TRUE) scor.star <- scor[k.star, ]First we generate the vector
k.star which contains the indices
(subscripts) of the data vectors that go into the bootstrap sample.
Then we use the R subscripting operations to generate the corresponding
data structure.
This is analogous to the way we did the nonparametric bootstrap for the correlation coefficient and for a similar reason. Whenever we have a complicated data structure, we will need this trick of resampling indices rather than data.
scor is a matrix and so is scor.star.
Every row of scor.star is a row of scor.
The i-th row of scor.star is the
k.star[i]-th row of scor.
And that's exactly what we want since k.star[i] is a random
integer in the range of allowed row indices of scor.
var(...) with
var(...) * (n - 1) / n because Efron and Tibshirani use
the variance of the empirical distribution (divide by n)
rather than the so-called sample variance(divide by
n - 1)
which is what the R var function calculates.
patch up signs . . . .Solves the problem that Efron and Tibshirani moan about on the bottom half of p. 69 the right way rather than the wrong way. If the sign is arbitrary, fix the signs to be consistent rather than just throwing out the ones with the
wrongsigns, which may bias the bootstrap calculation.
Here we calculate the inner product with the corresponding eigenvector for the original data and adjust the signs of the bootstrap eigenvectors so the inner products are all positive.
boxplot function wants a list of vectors rather than a
matrix. The data.frame converts a matrix into a list of vectors
which are the columns of the matrix. That's why we use that.
You may be wondering how anyone is supposed to come up with that. Simple. The on-line help for the boxplot function has an example illustrating this trick. I just copied the example.
var(eigenvec.1) and var(eigenvec.2)
are those variance matrices.
But it's not easy to interpret a five dimensional variance matrix. So
we take a hint from the nature of the problem. If eigenvalues and eigenvectors
are in general a good way to look at (symmetric) matrices, then they are
in particular a good way to look at
var(eigenvec.1) and var(eigenvec.2)
and var(eigenvec.2) - var(eigenvec.1).
Hence we look at the eigenvalues and eigenvectors (of the variance matrix of the bootstrap sampling distribution of an eigenvector of the data). (eigenvectors of . . . an eigenvector! Woof!)
It is clear that all of the eigenvalues for the variance of the first eigenvector are much smaller than the corresponding eigenvalues for the second eigenvector. Hence the first eigenvector is much less variable than the second.