sample.sim: Generate random sample with different proportion of outliers...

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

Description

Generate random sample with different proportion of outliers and leverage points

Usage

sample.sim(n, p, sig, a1, a2, nn = TRUE, intercept = FALSE)

Arguments

n	number of observations.
p	number of independent variables (predictors).
sig	variance of dependent variable.
a1	proportion of outliers.
a2	proportion of leverage points in outliers.
nn	whether coefficients are non-negative, default TRUE.
intercept	whether intercept is included in model, default TRUE.

Value

y: vector of dependent variable.

x: matrix of predictors with n rows and p columns.

loc: index of added outliers.

beta: vector of coefficients.

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)

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