hidetify (version 0.0.1)

rcpp_shidetify: Arma Asymmetric Single Detection Statistic

Description

rcpp_shidetify computes the asymmetric influence measure statistic for the single detection technique.

Usage

rcpp_shidetify(
  x, 
  y, 
  xquant, 
  yquant, 
  inv_rob_sdx, 
  rob_sdy,
  asymvec,
  row_indice)

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.

asymvec

vector of asymmetric points or percentiles.

row_indice

Initial indice of the observations

Value

A matrix of the influence measure statistic according to the asymmetric points.

Examples

# NOT RUN {
# }
# 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)

# Initial indice of the observations
row_indice <- 1:100

out <-
rcpp_shidetify(x, youtlier, xquant, yquant, inv_rob_sdx, rob_sdy, asymvec, row_indice)
# }