banded.sample.cv {FastBandChol} | R Documentation |
Selects bandwidth for sample covariance matrix by k-fold cross validation
banded.sample.cv(X, bandwidth, folds = 3, est.eval = TRUE, Frob = TRUE)
X |
A data matrix with n rows and p columns. Rows are assumed to be independent realizations from a p-variate distribution with covariance Σ. |
bandwidth |
A vector of candidate bandwidths. Candidate bandwidths can only positive integers such that the maximum is less than p-1 |
.
folds |
The number of folds used for cross validation. Default is |
est.eval |
Logical: |
Frob |
Logical: |
A list with
bandwidth.min |
the bandwidth minimizing cv error |
est |
the sample covariance matrix at bandwidth.min |
## set sample size and dimension n=20 p=100 ## create covariance with AR1 structure Sigma = matrix(0, nrow=p, ncol=p) for(l in 1:p){ for(m in 1:p){ Sigma[l,m] = .5^(abs(l-m)) } } ## simulation Normal data eo1 = eigen(Sigma) Sigma.sqrt = eo1$vec%*%diag(eo1$val^.5)%*%t(eo1$vec) X = t(Sigma.sqrt%*%matrix(rnorm(n*p), nrow=p, ncol=n)) ## perform cross validation k = 4:7 out2.cv = banded.sample.cv(X, bandwidth=k, folds=5)