tuningBIC: Tuning parameter k in function alts using Bayesian...

In FARDEEP: Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares

Description

Tuning parameter k in function alts using Bayesian Information Criterion (BIC) with some adjustment.

Usage

tuningBIC(x, y, alpha1 = 0.1, alpha2 = 1.5, up = 10, low = 1,
  nn = TRUE, intercept = TRUE, lognorm = TRUE)

Arguments

x	input matrix of predictors with n rows and p columns.
y	input vector of dependent variable with length n.
alpha1	parameter used to adjust the upper bound of outliers. Take value from 0 to 1, default 0.1.
alpha2	parameter used to adjust the lower bound of outliers. Take value larger than 1, default 1.5.
up	upper bound of parameter k in function alts, default 10.
low	lower bound of parameter k in function alts, default 1.
nn	whether coefficients are non-negative, default TRUE.
intercept	whether intercept is included in model, default TRUE.
lognorm	whether noise is log-normal distributed, default TRUE.

Value

k: tuning result of parameter k for function alts.

Author(s)

Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie

References

Yuning Hao, Ming Yan, Blake R. Heath, Yu L. Lei and Yuying Xie. Fast and Robust Deconvolution of Tumor Infiltrating Lymphocyte from Expression Profiles using Least Trimmed Squares. <doi:10.1101/358366>

Examples

library(FARDEEP)
samp = sample.sim(n = 500, p = 20, sig = 1, a1 = 0.1, a2 = 0.2, nn = TRUE, intercept = TRUE)
k = tuningBIC(samp$x, samp$y, lognorm = FALSE)

FARDEEP documentation built on May 2, 2019, 7:29 a.m.