View source: R/jackstraw_irlba.R
jackstraw_irlba  R Documentation 
Test association between the observed variables and their latent variables captured by principal components (PCs). PCs are computed using the augmented implicitly restarted Lanczos bidiagonalization algorithm (IRLBA; see irlba
).
jackstraw_irlba(
dat,
r = NULL,
r1 = NULL,
s = NULL,
B = NULL,
covariate = NULL,
verbose = TRUE,
...
)
dat 
a data matrix with 
r 
a number (a positive integer) of significant principal components. See permutationPA and other methods. 
r1 
a numeric vector of principal components of interest. Choose a subset of 
s 
a number (a positive integer) of “synthetic” null variables. Out of 
B 
a number (a positive integer) of resampling iterations. There will be a total of 
covariate 
a data matrix of covariates with corresponding 
verbose 
a logical specifying to print the computational progress. 
... 
additional arguments to 
This function computes m
pvalues of linear association between m
variables and their PCs.
Its resampling strategy accounts for the overfitting characteristics due to direct computation of PCs from the observed data
and protects against an anticonservative bias.
Provide the data matrix, with m
variables as rows and n
observations as columns.
Given that there are r
significant PCs, this function tests for linear association between
m
variables and their r
PCs.
You could specify a subset of significant PCs that you are interested in (r1
). If r1
is given,
then this function computes statistical significance of association between m
variables and r1
,
while adjusting for other PCs (i.e., significant PCs that are not your interest).
For example, if you want to identify variables associated with 1st and 2nd PCs,
when your data contains three significant PCs, set r=3
and r1=c(1,2)
.
Please take a careful look at your data and use appropriate graphical and statistical criteria
to determine a number of significant PCs, r
. The number of significant PCs depends on the data structure and the context.
In a case when you fail to specify r
, it will be estimated from a permutation test (Buja and Eyuboglu, 1992)
using a function permutationPA.
If s
is not supplied, s
is set to about 10% of m
variables.
If B
is not supplied, B
is set to m*10/s
.
jackstraw_irlba
returns a list consisting of
p.value 

obs.stat 

null.stat 

Neo Christopher Chung nchchung@gmail.com
Chung and Storey (2015) Statistical significance of variables driving systematic variation in highdimensional data. Bioinformatics, 31(4): 545554 https://academic.oup.com/bioinformatics/article/31/4/545/2748186
jackstraw jackstraw_subspace permutationPA
## simulate data from a latent variable model: Y = BL + E
B = c(rep(1,10),rep(1,10), rep(0,180))
L = rnorm(20)
E = matrix(rnorm(200*20), nrow=200)
dat = B %*% t(L) + E
dat = t(scale(t(dat), center=TRUE, scale=TRUE))
## apply the jackstraw
out = jackstraw_irlba(dat, r=1)
## Use optional arguments
## For example, set s and B for a balance between speed of the algorithm and accuracy of pvalues
## Not run:
## out = jackstraw_irlba(dat, r=1, s=10, B=200)
## End(Not run)
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