robregcc_sim {robregcc} | R Documentation |
Simulation data
Description
Simulate data for the robust regression with compositional covariates
Usage
robregcc_sim(n, betacc, beta0, O, Sigma, levg, snr, shft, m, C, out = list())
Arguments
n |
sample size |
betacc |
model parameter satisfying compositional covariates |
beta0 |
intercept |
O |
number of outlier |
Sigma |
covariance matrix of simulated predictors |
levg |
1/0 whether to include leveraged observation or not |
snr |
noise to signal ratio |
shft |
multiplying factor to model variance for creating outlier |
m |
test sample size |
C |
subcompositional matrix |
out |
list for obtaining output with simulated data structure |
Value
a list containing simulated output.
References
Mishra, A., Mueller, C.,(2019) Robust regression with compositional covariates. In prepration. arXiv:1909.04990.
Examples
## Simulation example:
library(robregcc)
library(magrittr)
## n: sample size
## p: number of predictors
## o: fraction of observations as outliers
## L: {0,1} => leveraged {no, yes},
## s: multiplicative factor
## ngrp: number of subgroup in the model
## snr: noise to signal ratio for computing true std_err
## Define parameters to simulate example
p <- 80 # number of predictors
n <- 300 # number of sample
O <- 0.10*n # number of outlier
L <- 1
s <- 8
ngrp <- 4 # number of sub-composition
snr <- 3 # Signal to noise ratio
example_seed <- 2*p+1 # example seed
set.seed(example_seed)
# Simulate subcomposition matrix
C1 <- matrix(0,ngrp,23)
tind <- c(0,10,16,20,23)
for (ii in 1:ngrp)
C1[ii,(tind[ii] + 1):tind[ii + 1]] <- 1
C <- matrix(0,ngrp,p)
C[,1:ncol(C1)] <- C1
# model parameter beta
beta0 <- 0.5
beta <- c(1, -0.8, 0.4, 0, 0, -0.6, 0, 0, 0, 0, -1.5, 0, 1.2, 0, 0, 0.3)
beta <- c(beta,rep(0,p - length(beta)))
# Simulate response and predictor, i.e., X, y
Sigma <- 1:p %>% outer(.,.,'-') %>% abs(); Sigma <- 0.5^Sigma
data.case <- vector("list",1)
set.seed(example_seed)
data.case <- robregcc_sim(n,beta,beta0, O = O,
Sigma,levg = L, snr,shft = s,0, C,out = data.case)
data.case$C <- C
# We have saved a copy of simulated data in the package
# with name simulate_robregcc
# simulate_robregcc = data.case;
# save(simulate_robregcc, file ='data/simulate_robregcc.rda')
X <- data.case$X # predictor matrix
y <- data.case$y # model response
[Package robregcc version 1.1 Index]