sample.probeset: sample.probeset

In RPA: RPA: Robust Probabilistic Averaging for probe-level analysis

Description

Toydata generator for probeset data.

Usage

sample.probeset(P = 10, n = 20, shape = 1, scale = 1, mu.real = 2)

Arguments

P	Number of probes.
n	Number of samples.
shape	Shape parameter of the inverse Gamma function used to generate the probe-specific variances.
scale	Scale parameters of the inverse Gamma function used to generate the probe-specific variances.
mu.real	Absolute signal level of the probeset.

Details

Generate random probeset with varying probe-specific affinities and variances. The toy data generator follows distributional assumptions of the RPA model and allows quantitative estimation of model accuracy with different options, noise levels and sample sizes. Probeset-level summary estimate is obtained as mu.real + d.

Value

A list with the following elements:

dat	Probeset data: probes x samples
tau2	Probe variances.
affinity	Probe affinities.
d	Probeset signal shape.
mu.real	Probeset signal level.
mu	Probeset-level total signal.

Author(s)

Leo Lahti [e-mail]

References

See citation("RPA")

Examples

# real <- sample.probeset(P = 10, n = 20, shape = 1, scale = 1, mu.real = 2)

RPA documentation built on Nov. 8, 2020, 7:47 p.m.