G'Day R users!
Following an ordination using prcomp, I'd like to test which variables
singnificantly contribute to a principal component. There is a method
suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called
"bootstrapped eigenvector". It was asked for that in this forum in
January 2005 by J?r?me Lema?tre:
"1) Resample 1000 times with replacement entire raws from the original
data sets []
2) Conduct a PCA on each bootstrapped sample
3) To prevent axis reflexion and/or axis reordering in the bootstrap,
here are two more steps for each bootstrapped sample
3a) calculate correlation matrix between the PCA scores of the original
and those of the bootstrapped sample
3b) Examine whether the highest absolute correlation is between the
corresponding axis for the original and bootstrapped samples. When it is
not the case, reorder the eigenvectors. This means that if the highest
correlation is between the first original axis and the second
bootstrapped axis, the loadings for the second bootstrapped axis and use
to estimate the confidence interval for the original first PC axis.
4) Determine the p value for each loading. Obtained as follow: number of
loadings >=0 for loadings that were positive in the original matrix
divided by the number of boostrap samples (1000) and/or number of
loadings =<0 for loadings that were negative in the original matrix
divided by the number of boostrap samples (1000)."
(see https://stat.ethz.ch/pipermail/r-help/2005-January/065139.html ).
The suggested solution (by Jari Oksanen) was
function (x, permutations=1000, ...)
{
pcnull <- princomp(x, ...)
res <- pcnull$loadings
out <- matrix(0, nrow=nrow(res), ncol=ncol(res))
N <- nrow(x)
for (i in 1:permutations) {
pc <- princomp(x[sample(N, replace=TRUE), ], ...)
pred <- predict(pc, newdata = x)
r <- cor(pcnull$scores, pred)
k <- apply(abs(r), 2, which.max)
reve <- sign(diag(r[k,]))
sol <- pc$loadings[ ,k]
sol <- sweep(sol, 2, reve, "*")
out <- out + ifelse(res > 0, sol <= 0, sol >= 0)
}
out/permutations
}
However, in a post from March 2005 ( http://r-help.com/msg/6125.html )
Jari himself mentioned that there is a bug in this method.
I was wondering whether someone could tell me where the bug is or
whether there is a better method in R to test for significance of
loadings (not the significance of the PCs).
Maybe it is not a good idea to do it at all, but I would prefer to have
some guidline for interpretation rather than making decisions
arbitrarily. I tried to look everywhere before posting here.
I would be very thankful for any help,
Axel
--
Gravity is a habit that is hard to shake off.
Terry Pratchett
Axel Strau? schrieb:> G'Day R users! > > Following an ordination using prcomp,Sorry, correction. I mean "using princomp".> I'd like to test which variables singnificantly contribute to a > principal component. There is a method suggested by Peres-Neto and al. > 2003. Ecology 84:2347-2363 called "bootstrapped eigenvector". ....-- Gravity is a habit that is hard to shake off. Terry Pratchett
Hi Alex, I presume you've asked Jari what the bug is? He obviously knows and you are asking a lot for the rest of us to i) know the method you speak of and ii) debug the code of another. As an alternative, try function testdim() in the ade4 package. This implements the method of Dray: Dray, S (2008) On the number of principal components: A test of dimensionality based on measurements of similarity between matrices. Computational Statistics and Data Analysis, 58:2228:2237. G On Mon, 2009-01-19 at 09:53 +0100, Axel Strau? wrote:> G'Day R users! > > Following an ordination using prcomp, I'd like to test which variables > singnificantly contribute to a principal component. There is a method > suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called > "bootstrapped eigenvector". It was asked for that in this forum in > January 2005 by J?r?me Lema?tre: > "1) Resample 1000 times with replacement entire raws from the original > data sets [] > 2) Conduct a PCA on each bootstrapped sample > 3) To prevent axis reflexion and/or axis reordering in the bootstrap, > here are two more steps for each bootstrapped sample > 3a) calculate correlation matrix between the PCA scores of the original > and those of the bootstrapped sample > 3b) Examine whether the highest absolute correlation is between the > corresponding axis for the original and bootstrapped samples. When it is > not the case, reorder the eigenvectors. This means that if the highest > correlation is between the first original axis and the second > bootstrapped axis, the loadings for the second bootstrapped axis and use > to estimate the confidence interval for the original first PC axis. > 4) Determine the p value for each loading. Obtained as follow: number of > loadings >=0 for loadings that were positive in the original matrix > divided by the number of boostrap samples (1000) and/or number of > loadings =<0 for loadings that were negative in the original matrix > divided by the number of boostrap samples (1000)." > > (see https://stat.ethz.ch/pipermail/r-help/2005-January/065139.html ). > > The suggested solution (by Jari Oksanen) was > > > function (x, permutations=1000, ...) > { > pcnull <- princomp(x, ...) > res <- pcnull$loadings > out <- matrix(0, nrow=nrow(res), ncol=ncol(res)) > N <- nrow(x) > for (i in 1:permutations) { > pc <- princomp(x[sample(N, replace=TRUE), ], ...) > pred <- predict(pc, newdata = x) > r <- cor(pcnull$scores, pred) > k <- apply(abs(r), 2, which.max) > reve <- sign(diag(r[k,])) > sol <- pc$loadings[ ,k] > sol <- sweep(sol, 2, reve, "*") > out <- out + ifelse(res > 0, sol <= 0, sol >= 0) > } > out/permutations > } > > However, in a post from March 2005 ( http://r-help.com/msg/6125.html ) > Jari himself mentioned that there is a bug in this method. > > I was wondering whether someone could tell me where the bug is or > whether there is a better method in R to test for significance of > loadings (not the significance of the PCs). > Maybe it is not a good idea to do it at all, but I would prefer to have > some guidline for interpretation rather than making decisions > arbitrarily. I tried to look everywhere before posting here. > > I would be very thankful for any help, > > Axel >-- %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% Dr. Gavin Simpson [t] +44 (0)20 7679 0522 ECRC, UCL Geography, [f] +44 (0)20 7679 0565 Pearson Building, [e] gavin.simpsonATNOSPAMucl.ac.uk Gower Street, London [w] http://www.ucl.ac.uk/~ucfagls/ UK. WC1E 6BT. [w] http://www.freshwaters.org.uk %~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~%~% -------------- next part -------------- A non-text attachment was scrubbed... Name: not available Type: application/pgp-signature Size: 197 bytes Desc: This is a digitally signed message part URL: <https://stat.ethz.ch/pipermail/r-help/attachments/20090119/745bc1fe/attachment-0002.bin>
I don't know if there are bugs in the code, but the step 4) does not
compute significance... at least the way statisticians know it. The
fractions above or below 0 are not significance. I don't even know how
to call those... probably cdf of the bootstrap distribution evaluated
at zero.
Let's put the flies and the sausages separately, as a Russian saying
goes. If you bootstrap from your original data, you can get the
confidence intervals, but not the test statistics. What you can do
with your bootstrap from the raw data is accumulate the distribution
of the eigenvectors, along the lines of (assuming you are testing for
the significance of variables in your first component):
function (x, permutations=1000, ...)
{
pcnull <- princomp(x, ... )
res <- pcnull$loadings[,1]
bsresults = matrix( rep.int(NA, permutations*NROW(res)) ,
nrow=permutations, ncol=NROW(res) )
N <- nrow(x)
for (i in 1:permutations) {
pc <- princomp(x[sample(N, replace=TRUE), ], ... )
pred <- predict(pc, newdata = x)
r <- cor(pcnull$scores, pred)
k <- apply(abs(r), 2, which.max)
reve <- sign(diag(r[k,]))
sol <- pc$loadings[ ,k]
sol <- sweep(sol, 2, reve, "*")
bsresults[i,] <- t(sol[,1])
}
apply( bsresults, 2, quantile, c(0.05, 0.95) )
}
if I am not messing up the dimensions and other stuff too much.
However as a result you will get an approximately degenerate
distribution sitting on the unit sphere since the eigenvectors are
always normalized to have the length of 1. You can still do marginal
confidence intervals with that though, and see if 0 is covered.
The main problem here is I am not entirely sure the bootstrap is
applicable for the problem at hand. In other words, it is not clear
whether the bootstrap estimates are consistent for the true
variability. Eigenproblems are quite difficult and prone to
non-regularities (the above mentioned degeneracy is just one of them,
and probably not the worst one). There are different asymptotics
(Anderson's of fixed p and n \to \infty,
http://www.citeulike.org/user/ctacmo/article/3908837, and Johnstone
joint p and n \to \infty with p/n \to const,
http://www.citeulike.org/user/ctacmo/article/3908846), the
distributions are often non-standard, while the bootstrap works well
when the distributions of the estimates are normal. When you have
something weird, bootstrap may easily break down, and in a lot of
other situations, you need to come up with special schemes. See my
oh-so-favorite paper on the bootstrap,
http://www.citeulike.org/user/ctacmo/article/575126.
One of those special schemes (back to out muttons, or rather flies and
sausages) -- to set up the bootstrap for hypothesis testing and get
the p-values, you need to bootstrap from the distribution that
corresponds to the null. Beran and Srivastava (1985 Annals,
http://www.citeulike.org/user/ctacmo/article/3015345) discuss how to
rotate your data to conform to the null hypothesis of interest for
inference with covariance matrices and their functions (such as
eigenvalues, for instance). Whether you need to go into all this
trouble, I don't really know.
If you have an inferential problem of testing whether a particular
variable contributes to the overall index, and have a pretty good idea
of where each variable goes, may be you need to shift your paradigm
and look at confirmatory factor analysis models instead, estimable in
R with John Fox' sem package.
On 1/19/09, Axel Strau? <a.strauss at tu-bs.de>
wrote:> G'Day R users!
>
> Following an ordination using prcomp, I'd like to test which variables
> singnificantly contribute to a principal component. There is a method
> suggested by Peres-Neto and al. 2003. Ecology 84:2347-2363 called
> "bootstrapped eigenvector". It was asked for that in this forum
in January
> 2005 by J?r?me Lema?tre:
> "1) Resample 1000 times with replacement entire raws from the
original data
> sets []
> 2) Conduct a PCA on each bootstrapped sample
> 3) To prevent axis reflexion and/or axis reordering in the bootstrap, here
> are two more steps for each bootstrapped sample
> 3a) calculate correlation matrix between the PCA scores of the original
and
> those of the bootstrapped sample
> 3b) Examine whether the highest absolute correlation is between the
> corresponding axis for the original and bootstrapped samples. When it is
not
> the case, reorder the eigenvectors. This means that if the highest
> correlation is between the first original axis and the second bootstrapped
> axis, the loadings for the second bootstrapped axis and use to estimate the
> confidence interval for the original first PC axis.
> 4) Determine the p value for each loading. Obtained as follow: number of
> loadings >=0 for loadings that were positive in the original matrix
divided
> by the number of boostrap samples (1000) and/or number of loadings =<0
for
> loadings that were negative in the original matrix divided by the number of
> boostrap samples (1000)."
>
> (see
> https://stat.ethz.ch/pipermail/r-help/2005-January/065139.html
> ).
>
> The suggested solution (by Jari Oksanen) was
>
>
> function (x, permutations=1000, ...)
> {
> pcnull <- princomp(x, ...)
> res <- pcnull$loadings
> out <- matrix(0, nrow=nrow(res), ncol=ncol(res))
> N <- nrow(x)
> for (i in 1:permutations) {
> pc <- princomp(x[sample(N, replace=TRUE), ], ...)
> pred <- predict(pc, newdata = x)
> r <- cor(pcnull$scores, pred)
> k <- apply(abs(r), 2, which.max)
> reve <- sign(diag(r[k,]))
> sol <- pc$loadings[ ,k]
> sol <- sweep(sol, 2, reve, "*")
> out <- out + ifelse(res > 0, sol <= 0, sol >= 0)
> }
> out/permutations
> }
>
> However, in a post from March 2005 (
> http://r-help.com/msg/6125.html ) Jari himself mentioned
> that there is a bug in this method.
>
> I was wondering whether someone could tell me where the bug is or whether
> there is a better method in R to test for significance of loadings (not the
> significance of the PCs).
> Maybe it is not a good idea to do it at all, but I would prefer to have
> some guidline for interpretation rather than making decisions arbitrarily.
I
> tried to look everywhere before posting here.
>
> I would be very thankful for any help,
>
--
Stas Kolenikov, also found at http://stas.kolenikov.name
Small print: I use this email account for mailing lists only.