View source: R/bayes-flowClust.R
prior_flowclust | R Documentation |
We elicit data-driven prior parameters from a flowSet
object for
specified channels. For each sample in the flowSet
object, we apply the
given prior_method
to elicit the priors parameters.
prior_flowclust(
flow_set,
channels,
prior_method = c("kmeans"),
K = 2,
nu0 = 4,
w0 = c(10, 10),
shrink = 1e-06,
...
)
flow_set |
a |
channels |
a character vector containing the channels in the
|
prior_method |
the method to elicit the prior parameters |
K |
the number of mixture components to identify |
nu0 |
prior degrees of freedom of the Student's t mixture components. |
w0 |
the number of prior pseudocounts of the Student's t mixture components. (only the first element is used and the rest is ignored at the moment) |
shrink |
the amount of eigenvalue shrinkage to add in the case the prior covariance matrices are singular. See details. |
... |
Additional arguments passed to the prior elicitation method selected |
Currently, we have implemented only two methods. In the case that one channel
is given, we use the kernel-density estimator (KDE) approach for each sample
to obtain K
peaks from which we elicit prior parameters. Otherwise,
if more than one channel is specified, we apply K-Means to each of the samples
in the flowSet
and aggregate the clusters to elicit the prior
parameters.
In the rare case that a prior covariance matrix is singular, we shrink the
eigenvalues of the matrix slightly to ensure that it is positive definite. For
instance, if the flow_set
has two samples, this case can occur. The
amount of shrinkage is controlled in shrink
.
list of the necessary prior parameters
## Not run:
library(flowCore)
data(GvHD)
prior_flowclust(GvHD[1:3], c("FSC-H", "SSC-H"))
## End(Not run)
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