Description Usage Arguments Value Examples
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.
1 2 3 4 5 6 7 8 9 10 11 | rcpp_mask_swamp_stat(
x,
y,
xquant,
yquant,
inv_rob_sdx,
rob_sdy,
number_subset,
size_subset,
est_clean_set,
asymvec )
|
x |
a matrix of elements. |
y |
a vector of elements. |
xquant |
quantiles of the columns of |
yquant |
quantiles vector of the vector |
inv_rob_sdx |
inverse of the median absolute deviation of the matrix |
rob_sdy |
median absolute deviation of the vector |
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. |
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 | ## 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)
|
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