| estimateDimension {PRISMA} | R Documentation |
Estimate Inner Dimension
Description
Matrix factorization methods compress the original data matrix A \in
R^{f,N} with f features and N samples into two parts,
namely A = B C with B \in R^{f,k}, C\in R^{k,
N}. The function estimateDimension estimates k based on a noise
model estimated from a scrambled version of the original data matrix.
Usage
estimateDimension(prismaData, alpha = 0.05, nScrambleSamples = NULL)
Arguments
prismaData |
A prismaData object loaded via loadPrismaData |
alpha |
Error probability for confidence intervals |
nScrambleSamples |
The number of scrambled samples that should be used to estimate the noise model. NULL means to use the complete data set. |
Value
estDim |
prismaDimension object that can be printed and plotted. |
Author(s)
Tammo Krueger <tammokrueger@googlemail.com>
References
R. Schmidt. Multiple emitter location and signal parameter estimation. IEEE Transactions on Antennas and Propagation, 34(3):276 – 280, 1986.
Examples
# please see the vingette for examles