alts {FARDEEP} | R Documentation |
Using the basic idea of least trimmed square to detect and remove outliers before estimating the coefficients. Adaptive least trimmed square.
Description
Using the basic idea of least trimmed square to detect and remove outliers before estimating the coefficients. Adaptive least trimmed square.
Usage
alts(x, y, alpha1 = 0.1, alpha2 = 1.5, k = 6, nn = TRUE,
intercept = 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. |
k |
parameter used to determine the boundary of outliers in the following step of algorithm. Take value from 1 to 10, default 6. |
nn |
whether coefficients are non-negative,default TRUE. |
intercept |
whether intercept is included in model, default TRUE. |
Value
beta: estimation of coefficients.
number_outlier: number of outliers.
outlier_detect: index of detected outliers.
X.new: good observed points for independent variables.
Y.new: good observed points for dependent variables.
k: modified k (if the input value is not appropriate).
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)
result = alts(samp$x, samp$y, alpha1 = 0.1, alpha2 = 1.5, k = 6, nn = TRUE, intercept = TRUE)
coef = result$beta