estimation: Estimation

Description Usage Arguments Value Examples

View source: R/estimation.R

Description

This function performs alternating direction method of multipliers optimization for a variety of loss functions to estimate the differential network given two samples of multivariate normal data.

Usage

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estimation(
  X,
  Y,
  lambdas = NULL,
  lambda_min_ratio = 0.3,
  nlambda = 10,
  a = NULL,
  loss = "lasso",
  tuning = "none",
  perturb = FALSE,
  stop_tol = 1e-05,
  max_iter = 500,
  correlation = FALSE,
  Delta_init = NULL,
  rho = NULL,
  gamma = NULL,
  verbose = FALSE
)

Arguments

X

The first multivariate normal sample.

Y

The second multivariate normal sample.

lambdas

Optional parameter - A list of the regularization values to be used within the loss functions.

lambda_min_ratio

Optional parameter - Defines the smallest regularization values as this proportion of the largest regularization value. Defaults to 0.3.

nlambda

Optional parameter - The number of regularization values considered. Defaults to 10.

a

Optional parameter - The thresholding parameter used in SCAD and MCP loss functions. Defaults to 3.7 with SCAD, and 3 with MCP respectively.

loss

Optional parameter - The loss function of choice to implement. The function allows for four choices, namely "lasso", "scad", "mcp" and "d-trace". Defaults to "lasso".

tuning

Optional parameter - The tuning method selected to determine the optimal value for the regularization parameter. Options are "none", "AIC", "BIC" and "EBIC". Defaults to "none".

perturb

Optional parameter - When set to TRUE perturbation as done by the CLIME software to improve performance is implemented. Options are TRUE or FALSE, with the function defaulting to FALSE.

stop_tol

Optional parameter - The stop tolerance to determine whether convergence has occurred. Defaults to 1e-5.

max_iter

Optional parameter - The maximum number of iterations that can be perform for any one regularization value. Defaults to 100.

correlation

Optional parameter - Determines whether the sample correlation matrices should be used in the place of the sample covariance matrices. Choices are TRUE and FALSE with the function defaulting to FALSE.

Delta_init

Optional parameter - Allows for the algorithm to provided an initial estimate of the differential network to ease computation.

rho

Optional parameter - Allows the user to adjust the ADMM step-size. Defaults to 1.

gamma

Optional parameter - Allows the user to adjust the EBIC value when EBIC is the selected tuning method. Defaults to 0.5.

verbose

Optional parameter - Allows the user to obtain a summary of the estimation results. Options are TRUE or FALSE, where FALSE indicates the summary is not provided. Defaults to FALSE.

Value

A list of various outputs, namely:

Examples

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data <- data_generator(n = 100, p = 50, seed = 123)
X <- data$X
Y <- data$Y
result <- estimation(X,Y)

dineR documentation built on Nov. 15, 2021, 5:09 p.m.