ClinicalNoiseModel {Umpire} | R Documentation |
A Noise Model for Clinical Data
Description
A ClinicalNoiseModel
represents the additional human and measurement
noise that is layered on top of any biological variabilty when measuring
clinical variables.
Usage
ClinicalNoiseModel(nFeatures, shape = 1.02, scale = 0.05/shape)
Arguments
nFeatures |
An integer; the number of additive scale parameters to sample from the gamma distribution. |
shape |
The |
scale |
The |
Details
We model both additive and multiplicative noise, so that the observed
expression of clinical variable c in sample i is given by:
Y_ci = S_ci + E_ci
, where Y_ci = observed expression,
S_ci = true biological signal.
In the ClinicalNoiseModel (as opposed to the NoiseModel
),
we model the additive noise as E_ci ~ N(0,tau)
,
without multiplicative noise or an additive bias/offset in the clinical model.
The standard deviation hyperparameters of the additive noise tau
is modeled by the gamma distribution tau ~ Gamma(shape, scale)
Value
An object of class NoiseModel
.
Author(s)
Kevin R. Coombes krc@silicovore.com, Caitlin E. Coombes caitlin.coombes@osumc.edu
See Also
Examples
showClass("NoiseModel")
## generate a ClinicalEngine with 20 features and 4 clusters
ce <- ClinicalEngine(20, 4, TRUE)
## generate 300 simulated patients
set.seed(194718)
dset <- rand(ce, 300)
cnm <- ClinicalNoiseModel(nrow(ce@localenv$eng), shape=2, scale=0.1)
cnm
noisy <- blur(cnm, dset$data)
hist(noisy)