Description Usage Arguments Details Value Examples
View source: R/gating-functions.R
We cluster the observations in fr
into K
clusters.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 | 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 end-points 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 |
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.
a rectangleGate
object consisting of all values beyond the
cutpoint calculated
1 2 3 4 | ## Not run:
gate <- gate_flowclust_1d(fr, params = "APC-A", K =2) # fr is a flowFrame
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.