DataRemix: Main DataRemix function

Description Usage Arguments Value

View source: R/Allfuncs.R

Description

Main DataRemix function

Usage

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DataRemix(svdres, fn, k_limits = c(1, ceiling(length(svdres$d)/2)), p_limits = c(-1,1), 
          mu_limits = c(1e-12,1), num_of_initialization = 5, num_of_thompson = 600, 
          basis = omega, xi = 0.1, full = T, verbose = T,...)

Arguments

svdres

The full SVD decomposition of input gene expression profile X, which is equal to svd(X)

fn

The custom function which takes svdres and some additional custom parameters as input and output the biological metric to be maximized. The output of fn() can be a vector with multiple objective values and the objective to be maximized should be the last element of the vector

k_limits

The lower and upper bound of k. By default the lower bound is set to be 1 and the upper bound is set to be the half of the rank

p_limits

The lower and upper bound of p. By default the lower bound is set to be -1 and the upper bound is set to be 1

mu_limits

The lower and upper bound of μ. By defult the lower bound is set to be 1e-12 and the upper bound is set to be 1

num_of_initialization

By default it uses 5 steps of Random Search to initialize the Thompson Sampling. We also provide a coarsed grid option for initialization with num_of_initialization set to be 0

num_of_thompson

The number of steps of Thompson Sampling, which is 600 by default

basis

The default mt is set to be 2,500. You can specify a smaller mt by using omega[1:2000,] for example, if you want to set mt to be 2,000

xi

A pre-defined probability which ensures the search doesn't stuck in the local optimum, which is 0.1 by default

full

If full is set to be True, the output will be the complete search history. Otherwise the output will only be the optimal combination of k, p and μ and the corresponding biological metric

verbose

If verbose is set to be True, then after each Thompson Sampling iteration, the function will print out the intermediate result

...

Additional arguments used as inputs to fn()

Value

para

If verbose is set to be True, the dimension of para is (num_of_initialization+num_of_thompson)-by-4(k, p, μ, biological metric). Otherwise, para is only a single vector with length 4 corresponding to the optimal objective value

full

If verbose is set to be True, the dimension of full is (num_of_initialization+num_of_thompson)-by-(k, p, μ, all returns from fn()). Otherwise, full is only a single vector corresponding to the optimal objectve value


wgmao/DataRemix documentation built on Aug. 27, 2018, 5:08 p.m.