| xval.buffer {CaDENCE} | R Documentation |
Cross-validation indices with a buffer between training/validation datasets
Description
Caculates training/validation indices for N-fold cross-validation of a dataset. Cross-validation folds are taken as contiguous blocks of cases with an optional buffer to prevent leakage of information between training/validation subsets due to the presence of autocorrelation.
Usage
xval.buffer(n.cases, n.xval=5, buffer.length=0)
Arguments
n.cases |
an integer specifying the length of the dataset. |
n.xval |
an integer specifying the desired number of cross-validation folds. |
buffer.length |
an integer specifying the number of cases to be left out as a buffer between the training/validation subsets. |
Value
a list with n.xval elements, each containing:
train |
indices of training cases |
valid |
indices of validation cases |
References
Shabbar, A. and V. Kharin. 2007. An assessment of cross-validation for estimating skill of empirical seasonal forecasts using a global coupled model simulation. CLIVAR Exchanges. 12(4): 10-12.
Zeng, Z., W.W. Hsieh, A. Shabbar, and W.W. Burrows, 2011. Seasonal prediction of winter extreme precipitation over Canada by support vector regression, Hydrology and Earth System Sciences, 15: 65-74.
Examples
print(xval.buffer(100, n.xval = 3, buffer.length = 10))