ISOpureS2.model_optimize.kappa.kappa_deriv_loglikelihood: Compute derivative of loglikelihood with respect to kappa for...
In ISOpureR: Deconvolution of Tumour Profiles
Description
Usage
Arguments
Value
Author(s)
Description
Computes the derivative of the part of the loglikelihood function relevant to
optimizing kappa for step 2. Instead of performing constrained optimization on kappa
directly, we optimize the log of kappa in an unconstrained fashion.
Usage
1
ISOpureS2.model_optimize.kappa.kappa_deriv_loglikelihood(log_kappa, model)
Arguments
log_kappa
the 1xD matrix log(kappa - model\$MIN_KAPPA)
model
list containing all the parameters to be optimized
Value
The negative derivative of the part of the loglikelihood function relevant to kappa
with respect to log kappa (a Dx1 matrix).
Author(s)
Gerald Quon, Catalina Anghel, Francis Nguyen
ISOpureR documentation built on May 11, 2019, 1:02 a.m.
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Description Usage Arguments Value Author(s)
Description
Computes the derivative of the part of the loglikelihood function relevant to optimizing kappa for step 2. Instead of performing constrained optimization on kappa directly, we optimize the log of kappa in an unconstrained fashion.
Usage
1 | ISOpureS2.model_optimize.kappa.kappa_deriv_loglikelihood(log_kappa, model)
|
Arguments
log_kappa |
the 1xD matrix log(kappa - model\$MIN_KAPPA) |
model |
list containing all the parameters to be optimized |
Value
The negative derivative of the part of the loglikelihood function relevant to kappa with respect to log kappa (a Dx1 matrix).
Author(s)
Gerald Quon, Catalina Anghel, Francis Nguyen
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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