Description Usage Arguments Details Value Author(s) References See Also Examples
Estimates a covariance matrix of m
variables (rows) from n
samples (columns), when m > n
.
Shrinkage estimators of principal component loadings are used to construct a high-dimensional covariance matrix.
Although several options are available to control characteristics of jackstraw weighted shrinkage (see jaws.pca
),
the required inputs are the data matrix dat
and the number of principal components r
to be used.
1 2 3 |
dat |
a data matrix with |
r |
a number of significance principal components ( |
jaws.pca.obj |
a jaws.pca output (optional). |
stat.shrinkage |
PNV shrinkage may be applied to "F-statistics" or "loadings" (default: F-statistics). |
extra.shrinkage |
extra shrinkage methods may be used; see details below (optional). |
verbose |
a logical specifying to print the progress (default: TRUE). |
seed |
a seed for the random number generator (optional). |
By default, jaws.cov
computes two canonical jackstraw weighted shrinkage estimators, namely PIP
and PNV
.
Additionally, extra shrinkage techniques may apply, such as soft- or hard-thresholding posterior inclusion probabilities
extra.shrinkage=c("PIPsoft","PIPhard")
.
Please provide r
numerical threshold values to be applied to r
principal components.
This algorithm applies shrinkage to the signal component of the covariance matrix, and assumes the independently distributed noise.
Since this function relies on shrunken loadings of PCs, you may first run jaws.pca
on dat
with a greater control over optional arguments
and supply its output jaws.pca.obj
to this function.
jaws.cov
returns a list consisting of
PIP |
estimated covariance matrix based on posterior inclusion probabilities |
PNV |
estimated covariance matrix based on proportion of null variables |
With appropriate extra.shrinkage
options (for details, see the Supplementary Information of Chung and Storey (2013), the output may also include
PIPhard |
estimated covariance matrix based on hard-thresholding the |
PIPsoft |
estimated covariance matrix based on soft-thresholding the |
Neo Chung nchchung@gmail.com
Chung and Storey (2015) Forthcoming
jaws.pca jackstraw.PCA
1 2 3 4 5 6 7 8 9 10 | set.seed(1234)
## simulate data from a latent variable model: Y = BX + E
B = c(rep(1,50),rep(-1,50), rep(0,900))
X = rnorm(20)
E = matrix(rnorm(1000*20), nrow=1000)
dat = B %*% t(X) + E
dat = t(scale(t(dat), center=TRUE, scale=FALSE))
## estimate large-scale covariance matrix
jaws.cov.out = jaws.cov(dat, r=1)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.