rcpp_mask_swamp_stat: Arma Masking and Swamping Statistics

In hidetify: Identify Influential Observations in High Dimension

Description

rcpp_mask_swamp_stat computes the min of the min and the max of the sum statistics of the asymmetric influence measure which represent the influential score of the observations in the swamping and masking steps, respectively.

Usage

rcpp_mask_swamp_stat(
  x, 
  y, 
  xquant, 
  yquant, 
  inv_rob_sdx, 
  rob_sdy,
  number_subset, 
  size_subset, 
  est_clean_set, 
  asymvec )

Arguments

x	a matrix of elements.
y	a vector of elements.
xquant	quantiles of the columns of x stacked in the matrix xquant.
yquant	quantiles vector of the vector y.
inv_rob_sdx	inverse of the median absolute deviation of the matrix x.
rob_sdy	median absolute deviation of the vector y.
number_subset	number of random subsets.
size_subset	size of random subsets.
est_clean_set	estimated cleaned set.
asymvec	vector of asymmetric points or percentiles.

Value

A matrix with the min of the min and the max of the sum statistics in the first and second column for each observation, respectively.

Examples

## Not run: 
## Simulate a dataset where the first 10 observations are influentials
require("MASS")
# the vector of asymmetric point
asymvec  <- c(0.25,0.5,0.75)

# the parameter of interest
beta_param <- c(3,1.5,0,0,2,rep(0,1000-5))

# the contamination parameter 
gama_param <- c(0,0,1,1,0,rep(1,1000-5))

# Covariance matrice for the predictors distribution 
sigmain <- diag(rep(1,1000))
for (i in 1:1000)
{
  for (j in i:1000) 
  {
    sigmain[i,j] <- 0.5^(abs(j-i))
    sigmain[j,i] <- sigmain[i,j]
  }
}

# set the seed
set.seed(13)

# the predictor matrix
x  <- mvrnorm(100, rep(0, 1000), sigmain)

# the error variable
error_var <- rnorm(100)

# the response variable
y  <- x %*% beta_param + error_var
y <- as.numeric(y)

### Generate influential observations

# the contaminated response variable
youtlier <- y
youtlier[1:10] <- x[1:10,] %*% (beta_param +  1.2*gama_param)  + error_var[1:10]
youtlier <- as.numeric(youtlier)

# the quantile of the predictors
xquant <- apply(x,2,quantile,asymvec)

# the quantile of contaminated response variable
yquant <- quantile(youtlier,asymvec)

# the inverse of the mad predictors
inv_rob_sdx <- 1/apply(x,2,mad)

# the mad contaminated response variable
rob_sdy <- mad(youtlier)

# the number of random subsets
number_subset <- 5

# the size of random subsets
size_subset <- 100/2

# the initial clean set
est_clean_set <- 1:100

out <- rcpp_mask_swamp_stat(
  x, 
  y, 
  xquant, 
  yquant, 
  inv_rob_sdx, 
  rob_sdy,
  number_subset, 
  size_subset, 
  est_clean_set, 
  asymvec )

## End(Not run)

hidetify documentation built on Aug. 20, 2021, 5:06 p.m.