eval.fik.m: Eval probability for M step Computes the log directly as log...
In QuantumClone: Clustering Mutations using High Throughput Sequencing (HTS) Data
Description
Eval probability for M step
Computes the log directly as log density is faster to compute
Usage
1
eval.fik.m(Schrod, centers, weights, adj.factor, log = TRUE)
Arguments
Schrod
The shcrodinger list of matrices
centers
centers of the clusters
weights
weight of each cluster
adj.factor
The adjusting factor, taking into account contamination, copy number, number of copies
log
Should it compute the log distribution (TRUE) or probability (FALSE)
between two optimization steps. If NULL, will take 1/(median depth).
QuantumClone documentation built on May 2, 2019, 3:03 a.m.
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Description
Eval probability for M step Computes the log directly as log density is faster to compute
Usage
1 | eval.fik.m(Schrod, centers, weights, adj.factor, log = TRUE)
|
Arguments
Schrod |
The shcrodinger list of matrices |
centers |
centers of the clusters |
weights |
weight of each cluster |
adj.factor |
The adjusting factor, taking into account contamination, copy number, number of copies |
log |
Should it compute the log distribution (TRUE) or probability (FALSE) between two optimization steps. If NULL, will take 1/(median depth). |
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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