select_smoothing {ssMRCD} | R Documentation |
Optimal Smoothing Parameter for ssMRCD based on Residuals
Description
The optimal smoothing value for the ssMRCD estimator is based on the residuals and the trimmed mean of the norm.
Usage
select_smoothing(
X,
groups,
weights,
lambda = seq(0, 1, 0.1),
TM = NULL,
alpha = 0.75,
seed = 123436,
return_all = TRUE,
cores = 1
)
Arguments
X |
data matrix containing observations. |
groups |
grouping vector corresponding to |
weights |
weight matrix for groups, see |
lambda |
vector of parameter values for smoothing, between 0 and 1. |
TM |
target matrix, if not given MCD (or MRCD if non regular) is used with default values and |
alpha |
percentage of outliers to be expected. |
seed |
seed for ssMRCD calculations. |
return_all |
logical, if FALSE the function returns only the optimal lambda. |
cores |
integer, number of cores used for parallel computing. |
Value
lambda_opt | optimal lambda for smoothing. |
COVS | ssMRCD object with optimal parameter setting. |
plot | plot for optimal parameter setting. |
residuals | mean of norm of residuals for varying lambda. |
Examples
# create data set
x1 = matrix(runif(200), ncol = 2)
x2 = matrix(rnorm(200), ncol = 2)
# create weighting matrix
W = matrix(c(0, 1, 1, 0), ncol = 2)
select_smoothing (X = rbind(x1, x2),
groups = rep(c(1,2), each = 100),
weights = W,
lambda = seq(0, 1, 0.1),
return_all = TRUE,
cores = 1)
[Package ssMRCD version 1.1.0 Index]