estimateDimension: Estimate Inner Dimension

In PRISMA: Protocol Inspection and State Machine Analysis

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 <[e-mail]>

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

Example output

Loading required package: Matrix
Loading required package: gplots

Attaching package: 'gplots'

The following object is masked from 'package:stats':

    lowess

Loading required package: ggplot2

PRISMA documentation built on May 1, 2019, 10:15 p.m.