gate_flowclust_1d.Rd
We cluster the observations in fr
into K
clusters.
gate_flowclust_1d(fr, params, filterId = "", K = NULL, trans = 0, min.count = 1, max.count = 1, nstart = 1, prior = NULL, criterion = c("BIC", "ICL"), cutpoint_method = c("boundary", "min_density", "quantile", "posterior_mean", "prior_density"), neg_cluster = 1, cutpoint_min = NULL, cutpoint_max = NULL, min = NULL, max = NULL, quantile = 0.99, quantile_interval = c(0, 10), plot = FALSE, debug = FALSE, ...)
fr  a 

params 

filterId  A 
K  the number of clusters to find 
trans, min.count, max.count, nstart  some flowClust parameters. see 
prior  list of prior parameters for the Bayesian

criterion  a character string stating the criterion used to choose the
best model. May take either "BIC" or "ICL". This argument is only relevant
when 
cutpoint_method  How should the cutpoint be chosen from the fitted

neg_cluster  integer. The index of the negative cluster. The cutpoint
is computed between clusters 
cutpoint_min  numeric value that sets a minimum thresold for the
cutpoint. If a value is provided, any cutpoint below this value will be set
to the given minimum value. If 
cutpoint_max  numeric value that sets a maximum thresold for the
cutpoint. If a value is provided, any cutpoint above this value will be set
to the given maximum value. If 
min  a numeric value that sets the lower bound for data filtering. If

max  a numeric value that sets the upper bound for data filtering. If

quantile  the quantile for which we will find the cutpoint using
the quantile 
quantile_interval  a vector of length 2 containing the endpoints of
the interval of values to find the quantile cutpoint. If the

plot  logical value indicating that the fitted 
debug 

...  additional arguments that are passed to 
a rectangleGate
object consisting of all values beyond the
cutpoint calculated
By default, the cutpoint is chosen to be the boundary of the first two
clusters. That is, between the first two cluster centroids, we find the
midpoint between the largest observation from the first cluster and the
smallest observations from the second cluster. Alternatively, if the
cutpoint_method
is min_density
, then the cutpoint is the point
at which the density between the first and second smallest cluster centroids
is minimum.
if (FALSE) { gate < gate_flowclust_1d(fr, params = "APCA", K =2) # fr is a flowFrame }