sample.probeset: sample.probeset

Description Usage Arguments Details Value Author(s) References Examples

Description

Toydata generator for probeset data.

Usage

1
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 leo.lahti@iki.fi

References

See citation("RPA")

Examples

1
# 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.