View source: R/informativeNoisePrior.R
informativeNoisePrior | R Documentation |
informativeNoisePrior
returns an informative noise prior for GFA, for
a given data collection. The function sets the noise residual parameters
such that the expected proportion of
variance explained is for all variables and groups (in contrast to being
proportional to their original scale). Recommended e.g. when the data is
'small n, large p', and the standard prior from getDefaultOpts
seems to overfit the model by not shutting off any component with high
initial K.
informativeNoisePrior(Y, opts, noiseProportion = 0.5, conf = 1)
Y |
The data. For details, see function |
opts |
Model options. See function |
noiseProportion |
proportion of total variance to be explained by noise. Suggested to lie between 0.01 and 0.99. |
conf |
Confidence in the prior, relative to confidence in the data. Suggested to lie between 0.01 and 100. |
The input model options (opts) with an updated residual noise prior, corresponding to the elements prior.alpha_0t and prior.beta_0t.
#Given data collection Y
opts <- getDefaultOpts()
## Not run: opts <- informativeNoisePrior(Y,opts,0.2,1)
## Not run: res <- gfa(Y,opts=opts)
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