Some useful comments have already been made. I would like to comment on
the two definitions of the p-value under (4) -- since I thought exactly
about this issue a while back. Maybe this will be useful ...
Suppose the distribution of a test statistic Z under H0 is given by f(Z)
and that the distribution is not symmetric. A bootstrap distribution
(under H0) may in fact look like that. We can define the critical values
for alpha = .05 in two different ways:
(1) We find a single value of z so that the sum of the lower and upper
tail is equal to .05 (red shaded region, corresponding to z = 9.51 and z
= -9.51). Since the distribution is very skewed, there is no area in the
lower tail, so that the red-shaded region in the upper tail is actually
equal to .05.
(2) We put .025 in the lower and .025 in the upper tail (blue shaded
regions, corresponding to z = -3.82 and z = 11.53 in the figure).
The first definition treats deviations on both ends of the sampling
distribution equally. The second definition acknowledges the fact that
deviations of a certain magnitude are more likely to occur in the upper
than in the lower tail. Hence, the critical value on the upper tail is
more extreme than on the lower tail. This is, in fact, how critical
values are typically defined for non-symmetric distributions.
Now given some data, we observe the value z of the test statistic. How
should we now calculate the two-sided p-value?
option 1: p = prob(|Z| > |z|)
-----------------------------
If z = 11.53, then p = .025 (reject H0)
If z = -11.53, then p = .025 (reject H0)
If z = 9.51, then p = .050 (reject H0)
If z = -9.51, then p = .050 (reject H0)
If z = -3.82, then p = .303 (do not reject H0)
If z = 1, then p = .779
Note that one could never actually get a p-value below .05 if z is
negative.
option 2: p = 2*prob(Z > z) if z is positive or 2*prob(Z < z) if z is
negative
---------------------------------------------------------------------
If z = 11.53, then p = .050 (reject H0)
If z = -11.53, then p = .000 (reject H0)
If z = 9.51, then p = .100 (do not reject H0)
If z = -9.51, then p = .000 (reject H0)
If z = -3.82, then p = .050 (reject H0)
If z = 1, then p = 1.07 !!!
For z = 9.51, we should not reject H0 according to the second definition
of the critical values. On the other hand, for z = -3.82, we should
reject H0 according to the second definition. So option 2 is more in
line how we typically define critical values in non-symmetric
distributions. However, note that the p value can be above 1 according
to the second definition!
######################################################################
alpha <- .05
shape <- 4
scale <- 2
xs <- seq(0, 30, .1)
#ys <- dchisq(xs, df=4)
ys <- dgamma(xs, shape=shape, scale=scale)
offset <- xs[which( ys == max(ys) )]
xs <- xs - offset
par(mar=c(5,4,2,2))
plot(xs, ys, type="l", lwd=2, xlim=c(-15,25), xlab="Z", ylab="f(Z)")
abline(v = 0)
########################################################################
#####
### some p-value calculations
round( pgamma(3.82+offset, shape=shape, scale=scale, lower.tail=F) +
pgamma(-3.82+offset, shape=shape, scale=scale, lower.tail=T) , 3 )
round( pgamma(0+offset, shape=shape, scale=scale, lower.tail=F) +
pgamma(-0+offset, shape=shape, scale=scale, lower.tail=T) , 3 )
round( pgamma(1+offset, shape=shape, scale=scale, lower.tail=F) +
pgamma(-1+offset, shape=shape, scale=scale, lower.tail=T) , 3 )
2*round( pgamma(1+offset, shape=shape, scale=scale, lower.tail=F) , 3 )
########################################################################
#####
upp.crit <- qgamma(alpha, shape=shape, scale=scale, lower.tail=F)
x.r <- seq(upp.crit, max(xs)+offset, length = 100)
y.r <- c( dgamma(x.r, shape=shape, scale=scale), 0, 0)
x.r <- c(x.r, max(xs), upp.crit) - offset
polygon(x.r, y.r, density = 50, col="red", angle=135)
text(upp.crit-offset, .03, paste("z = ", round(upp.crit-offset,2)),
pos=4, offset=0, col="red")
text(-1*(upp.crit-offset), .03, paste("z = ",
-1*round(upp.crit-offset,2)), pos=2, offset=0, col="red")
low.crit <- qgamma(alpha/2, shape=shape, scale=scale,
lower.tail=T)
x.l <- seq(min(xs), low.crit, length = 100)
y.l <- c( dgamma(x.l, shape=shape, scale=scale) , 0, 0)
x.l <- c(x.l, low.crit, min(xs)) - offset
polygon(x.l, y.l, density = 50, col="blue")
text(low.crit-offset, .05, paste("z = ", round(low.crit-offset,2)),
pos=2, offset=0, col="blue")
upp.crit <- qgamma(alpha/2, shape=shape, scale=scale,
lower.tail=F)
x.r <- seq(upp.crit, max(xs)+offset, length = 100)
y.r <- c( dgamma(x.r, shape=shape, scale=scale), 0, 0)
x.r <- c(x.r, max(xs), upp.crit) - offset
polygon(x.r, y.r, density = 50, col="blue")
text(upp.crit-offset, .05, paste("z = ", round(upp.crit-offset,2)),
pos=4, offset=0, col="blue")
abline(h = 0)
######################################################################
I hope this helps!
Best,
Wolfgang Viechtbauer
Department of Methodology and Statistics
University of Maastricht, The Netherlands
http://www.wvbauer.com/
----Original Message----
From: r-help-bounces at r-project.org [mailto:r-help-bounces at r-project.org]
On
Behalf Of Johan Jackson Sent: Sunday, April 12, 2009 23:48
To: r-help at r-project.org
Subject: [R] p-values from bootstrap - what am I not understanding?
> Dear stats experts:
> Me and my little brain must be missing something regarding
bootstrapping.
> I understand how to get a 95%CI and how to hypothesis test using
> bootstrapping (e.g., reject or not the null). However, I'd also like
to
> get a p-value from it, and to me this seems simple, but it seems
no-one
> does what I would like to do to get a p-value, which suggests I'm not
> understanding something. Rather, it seems that when people want a
p-value
> using resampling methods, they immediately jump to permutation testing
> (e.g., destroying dependencies so as to create a null distribution).
SO -
> here's my thought on getting a p-value by bootstrapping. Could someone
> tell me what is wrong with my approach? Thanks:
>
> STEPS TO GETTING P-VALUES FROM BOOTSTRAPPING - PROBABLY WRONG:
>
> 1) sample B times with replacement, figure out theta* (your statistic
of
> interest). B is large (> 1000)
>
> 2) get the distribution of theta*
>
> 3) the mean of theta* is generally near your observed theta. In the
same
> way that we use non-centrality parameters in other situations, move
the
> distribution of theta* such that the distribution is centered around
the
> value corresponding to your null hypothesis (e.g., make the
distribution
> have a mean theta = 0)
>
> 4) Two methods for finding 2-tailed p-values (assuming here that your
> observed theta is above the null value): Method 1: find the percent of
> recentered theta*'s that are above your observed theta. p-value = 2 *
> this percent Method 2: find the percent of recentered theta*'s that
are
> above the absolute value of your observed value. This is your p-value.
>
> So this seems simple. But I can't find people discussing this. So I'm
> thinking I'm wrong. Could someone explain where I've gone wrong?
>
>
> J Jackson