bpr_optimize: (DEPRECATED) Optimize BPR negative log likelihood function

Description Usage Arguments Value Author(s) See Also Examples

Description

(DEPRECATED) The function bpr_optimize minimizes the negative log likelihood of the BPR function. Since it cannot be evaluated analytically, an optimization procedure is used. The optim packages is used for performing optimization.

Usage

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bpr_optim(x, ...)

## S3 method for class 'list'
bpr_optim(
  x,
  w = NULL,
  basis = NULL,
  lambda = 1/2,
  opt_method = "CG",
  opt_itnmax = 100,
  is_parallel = TRUE,
  no_cores = NULL,
  ...
)

## S3 method for class 'matrix'
bpr_optim(
  x,
  w = NULL,
  basis = NULL,
  lambda = 1/2,
  opt_method = "CG",
  opt_itnmax = 100,
  ...
)

Arguments

x

The input object, either a matrix or a list.

...

Additional parameters.

w

A vector of parameters (i.e. coefficients of the basis functions)

basis

A 'basis' object. E.g. see create_rbf_object.

lambda

The complexity penalty coefficient for ridge regression.

opt_method

The optimization method to be used. See optim for possible methods. Default is "CG".

opt_itnmax

Optional argument giving the maximum number of iterations for the corresponding method. See optim for details.

is_parallel

Logical, indicating if code should be run in parallel.

no_cores

Number of cores to be used, default is max_no_cores - 2.

Value

Depending on the input object x:

Author(s)

C.A.Kapourani C.A.Kapourani@ed.ac.uk

See Also

create_basis, eval_functions

Examples

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# Example of optimizing parameters for synthetic data using default values
data <- meth_data
out_opt <- bpr_optim(x = data, is_parallel = FALSE, opt_itnmax = 3)

#-------------------------------------

# Example of optimizing parameters for synthetic data using 3 RBFs
ex_data <- meth_data
basis <- create_rbf_object(M=3)
out_opt <- bpr_optim(x = ex_data, is_parallel = FALSE, basis = basis,
                     opt_itnmax = 3)

#-------------------------------------

# Example of of specific promoter region using 2 RBFs
basis <- create_rbf_object(M=2)
w <- c(0.1, 0.1, 0.1)
data <- meth_data[[1]]
out_opt <- bpr_optim(x = data, w = w, basis = basis, fit_feature = "NLL",
                     opt_itnmax = 3)

andreaskapou/BPRMeth documentation built on June 11, 2020, 10:49 p.m.