We elicit data-driven prior parameters from a
flowSet object for
specified channels. For each sample in the
flowSet object, we apply the
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, ... )
a character vector containing the channels in the
the method to elicit the prior parameters
the number of mixture components to identify
prior degrees of freedom of the Student's t mixture components.
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)
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
list of the necessary prior parameters
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
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
flowSet and aggregate the clusters to elicit the prior
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