Description Usage Arguments Details Value References Examples
View source: R/cov-estim-condreg.R
Computes the condition-number regularized (CONDREG) estimator of the covariance matrix.
1 2 3 4 5 6 7 8 | sigma_estim_condreg(
data,
k = NULL,
k_seq = NULL,
k_seq_len = 50,
nfolds = 5,
zeromean_log = FALSE
)
|
data |
an nxp data matrix. |
k |
a double, indicating the regularization parameter (or the maximum condition number for the estimated covariance matrix). Default value is NULL and the optimal k is found with a cross-validation (CV), optimizing the negative likelihood. |
k_seq |
a vector of doubles, specifying the grid of k values to search over within the CV. Default value is NULL and the sequence is generated in dependence of the sample covariance matrix, the user-supplied length and the minimum deviation ratio of the values. |
k_seq_len |
an integer, indicating the length of k_seq. Default value is 50. |
nfolds |
an integer, specifying the number of folds for the CV. Default value is 5. |
zeromean_log |
a logical, indicating whether the data matrix has zero means (TRUE) or not (FALSE). Default value is FALSE. |
The CONDREG estimator is elaborated in detail in \insertCitewon2013condition;textualCovEstim. More information on the functionality can be found in \insertCitecondregpackage;textualCovEstim.
a list with the following entries
a pxp estimated covariance matrix.
an estimation specific tuning parameter, here the regularization parameter k.
1 2 3 4 5 6 | data(sp200)
sp_rets <- sp200[,-1]
results_condreg <- sigma_estim_condreg(sp_rets)
sigma_condreg <- results_condreg[[1]]
param_condreg <- results_condreg[[2]]
sigma_condreg <- sigma_estim_condreg(sp_rets, k=100)[[1]]
|
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