ClinicalNoiseModel {Umpire} | R Documentation |
A ClinicalNoiseModel
represents the additional human and measurement
noise that is layered on top of any biological variabilty when measuring
clinical variables.
ClinicalNoiseModel(nFeatures, shape = 1.02, scale = 0.05/shape)
nFeatures |
An integer; the number of additive scale parameters to sample from the gamma distribution. |
shape |
The |
scale |
The |
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)
An object of class NoiseModel
.
Kevin R. Coombes krc@silicovore.com, Caitlin E. Coombes caitlin.coombes@osumc.edu
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)