Fit a bimodal gaussian distribution to a set of observations.
fitBimodal(
x,
prob = 0.95,
coverage = 0.8,
size = 10,
assign = FALSE,
boolean = FALSE,
verbose = TRUE,
maxit = 5000,
maxrestarts = 100,
bySampling = FALSE,
sample.size = 100,
tries = 2000,
force.tries = FALSE,
...
)a named numeric vector of cells/observations or a matrix of genes X cells (variables X observations). If the latter, the column means are first computed.
a numeric value >= 0 and <= 1; the minimum posterior probability required for an observation to be assigned to a mode. Default: 0.95
the fraction of observations that must have a posterior probability higher than <prob> to one of two modes in order for the distribution to qualify as bimodal. Default: 0.8
the minimum number of observations that must be assigned to a mode in order for the distribution to qualify as bimodal. Default: 10
if set to TRUE, returns a list of length two containing the vector names that were assigned to each mode. Default: FALSE
if set to TRUE, returns a boolean value indicating whether the distribution is bimodal. Default: FALSE
print progress messages. Default: TRUE
the maximum number of iterations. Default: 5000
the maximum number of restarts allowed. See normalmixEM for details. Default: 100
logical; if TRUE, the function uses a bootstrapping method to subsample values and identify the two modes iteratively. This method is more sensitive to differing mode sizes, so will be useful if you believe one group to be much smaller than the other. Default: TRUE
the number of observations to be sampled. Default: 100
the number of bootstrap replicates.
Should bootstrap replicates continue to run if bimodality has already been found? Logical. Default: FALSE
The posterior probabilities of each observation to one of two modes. If boolean = TRUE, return a boolean value indicating whether bimodality was found. If assign = TRUE, return a list of length two with the observations (IDs) in each mode.