Lab: Nonparametric Bootstrapping

Part I

Getting Started

To Start R: Click on the R icon

Note: there is a bootstrap package with a function boott, BUT
we will use the original Efron and Tibshirani bootstrap function. There is also boot package, but we will not use that for this lab!

Let's get our own bootsrap function by:

> source("https://edoras.sdsu.edu/~babailey/stat672/bootstrap.r")

Here is the function: bootstrap.r

There is a help file available: bootstrap.help

Example 1: Mouse data: Bootstrap estimate of std. error for mean and median

The mouse data is survival time in days after surgery.

> mouse.c <- scan("https://edoras.sdsu.edu/~babailey/stat672/mouse.c.data")
> mouse.t <- scan("https://edoras.sdsu.edu/~babailey/stat672/mouse.t.data")

Bootstrap estimates for standard error for the mean of the treatment group:

> results1 <- bootstrap(mouse.t,50,mean)

What is in the results1 object?

Make a histogram of the bootstrap replicates.

> hist(results1$thetastar)
> thetahat <- mean(mouse.t)
> abline(v=thetahat, lty=2)

Now, the standard error of the 50 bootstrap replicates is

> sd(results1$thetastar)

Try this again with 1000 bootstrap samples, you can do this in one step

> results2 <- bootstrap(mouse.t,1000,mean,func=sd)

The value should be close to 23.36.

Try the above with the median and 1000 boostrap samples.

The value should be close to 37.83.

Confidence Intervals based on Bootstrap Percentiles

Recall, thetahat=86.85, the mean of the 7 treated mice. The bootstrap standard error of thetahat is 23.13, so if we choose alpha=.05, then the standard 90% normal confidence intervals for the true mean theta is [86.85 - 1.645*23.13, 86.85 + 1.645*23.13].

We could use the quantile function and construct percentiles of thetastar based on the 1000 bootstrap replications.

> conf.level <- 0.95
> probs <- (1 + c(-1, 1) * conf.level) / 2
> quantile(results1$thetastar, probs = probs)

What are the 5% and 95% percentiles of this histogram? What would be the percentile interval? (You could change conf.level to 0.9)

Example 2: More Complex Data Structures 

 From the help file:
# To bootstrap functions of more complex data structures,
# write theta so that its argument x
# is the set of observation numbers
#  and simply  pass as data to bootstrap the vector 1,2,..n.
# For example, to bootstrap
# the correlation coefficient from a set of 15 data pairs:

       xdata <- matrix(rnorm(30),ncol=2)
       n <- 15
       theta <- function(x,xdata){ cor(xdata[x,1],xdata[x,2]) }
       results <- bootstrap(1:n,20,theta,xdata)

Part II

Back to Kendall's tau

How can we bootrap CI for tau? Can you write a function to return the estimated tau values from data?

OK, try it. I wrote one and it is linked off the course calendar.

Example 2 Hormone Data: Linear Regression Bootstrapping Pairs

The hormone dataset is (Amount in milligrams of anti-inflammatory hormone remaining in 27 devices after a certain number of hours of wear. Sampled from 3 different lots A, B, and C.)

Let's get the hormone dataset and use help to see what's in it.

> hormone <- read.table("https://edoras.sdsu.edu/~babailey/stat672/hormone.data", header=T)

Plot the data.

> plot(hormone$hrs,hormone$amount)

Fit a linear regression model:

> fit <- lm(amount~hrs,hormone)
> summary(fit)

Write a function (for the argument theta) to be bootstrapped. It should fit a linear regression model to the bootstrap sample and return the estimated coefficients. 

 
> ls.pairs <- function(x, hormone)
{
        lm(amount ~ hrs, hormone[x,  ])$coefficients
}

Now we can generate bootstrap replicates calling the boostrap function.

> results <- bootstrap(1:27,100,theta=ls.pairs,hormone)

Calculate the std. error of the estimated coefficients.

> sd(results$thetastar[1,])
> sd(results$thetastar[2,])

Example 3 Hormone Data: Linear Regression Bootstrapping Residuals

Write a function (for the argument theta) to be bootstrapped. It should fit a linear regression model to the bootstrap sample and return the estimated coefficients. 

> ls.resid <- function(x, hormone, fit)
{
        lm(newY ~ hrs, data.frame(newY = fit$fitted.values + fit$residuals[x],
                hormone))$coefficients
}
or, there is a lsfit function: 
> ls.resid2 <- function(x, hormone, fit)
{
        lsfit(hormone$hrs, fit$fitted.values + fit$residuals[x])$coef
}

Now we can generate bootstrap replicates calling the boostrap function

> results3 <- bootstrap(1:27,1000,theta=ls.resid,hormone,fit)

Calculate the std. error of the estimated coefficients.

> sd(results3$thetastar[1,])
> sd(results3$thetastar[2,])

Now, if we were really clever, we would write a function(s) to calculate the standard errors and pass it to the boostrap function! (for both examples above!)

Example 4 Mouse data: Jackknife-after-Bootstrap

The mouse data is survival time in days after surgery.

Bootstrap estimates for standard error for the mean of the treatment group:

> set.seed(1)
> brep <- bootstrap(mouse.t,25,mean,func=sd)

What is in the brep$jack.boot.val ?

Let's see how to get the jackknife-after-bootstrap values.

First, set the seed again, and create the bootstrap matrix of indicies

> set.seed(1)
> matrix(sample(1:7, size = 7 * 25, replace = T), nrow = 25)

For example, if we want to compute brep$jac.boot.val[1] we would look for all the rows in the matrix that do not contain the number 1 and caculate the standard error of those $thetastar bootstrap estimates.

> sd(brep$thetastar[c(1,3,4,9,11,12,14,15,20,21,23,25)])

or depending on the version of R:

> sd(brep$thetastar[c(4,7,8,11,14,15,18,21,25)])

Now, to get the jackknife-after-bootstrap estimate of the standard error (brep$jack.boot.se) of the bootstrap estimate of the standard error (brep$func.thetastar) :

> sd(brep$jack.boot.val)*(7-1)/sqrt(7)

Pretty neat.

If you are interested in a jackknife function you can get your own function by:

> source("https://edoras.sdsu.edu/~babailey/stat672/jackknife.r")

Here is the function: jackknife.r

Better Bootstrap

We will use the function bcanon:

Let's get our own bcanon function by:

> source("https://edoras.sdsu.edu/~babailey/stat672/bcanon.r")

Here is the function: bcanon.r

There is a help file available: bcanon.help

Above, we used the quantile function and construct percentiles of thetastar based on the 1000 bootstrap replications.

Let's use the bcanon function:

> set.seed(6)
> bca <- bcanon(mouse.t, 1000, mean)

What's in bca?

How do they compare?

Permutation Tests

From the mouse data, the difference in the means, thetahat= zbar - ybar = 30.63, which suggests that the Treatment distribution F gives longer survival times than does the Control distribution G. Consider the hypothsis:

H_o: F=G

thetahat = 1/n sum_{g_i=z} data - 1/m sum_{g_i=y} data

  1. Choose B independent vectors g^*(1), g^*(2), ..., g^*(B), each consisting of n z's and m y's and each being randomly selected from all P(n,N=n+m) possible vectors. (B will ususally be at least 1000.)
  2. Evaluate the permutation replication of thetahat corresponding to each permutation vector.
  3. Estimate the permutataion achieved significance level (ASL) by

    #{thetahat^* >= thetahat}/B

See if you can write a function or source code to do a permutation test for the difference of the means of the mouse data.

Here is my function: myperm.r

OK, is there an R function for permutation tests?