This function is used to further control some aspects of the memory-based
learning process in the
1 2 3 4 5 6 7
a logical indicating if the dissimilarity matrix
a character vector which indicates the (internal) validation
method(s) to be used for assessing the global performance of the local models.
Possible options are:
a logical. It only applies when
an integer indicating the number of sampling iterations at
each local segment when
a numeric value indicating the percentage of calibration observations
to be retained at each sampling iteration at each local segment when
a logical. It indicates whether the prediction
limits at each local regression are determined by the range of the response
variable within each neighborhood. When the predicted value is outside
this range, it will be automatically replaced with the value of the nearest
range value. If
a logical indicating if parallel execution is allowed.
The validation methods available for assessing the predictive performance of the memory-based learning method used are described as follows:
Leave-nearest-neighbor-out cross-validation (
the group of neighbors of each observation to be predicted, the nearest observation
(i.e. the most similar observation) is excluded and then a local model is fitted
using the remaining neighbors. This model is then used to predict the value
of the response variable of the nearest observation. These predicted
values are finally cross validated with the actual values (See Ramirez-Lopez
et al. (2013a) for additional details). This method is faster than
Local leave-group-out cross-validation (
group of neighbors of each observation to be predicted is partitioned into
different equal size subsets. Each partition is selected based on a
stratified random sampling that uses the the distribution of
the response variable in the corresponding set of neighbors. When
p \mjeqn>=\geqslant 0.5 (i.e. the number of calibration
observations to retain is larger than 50
the sampling is conducted for selecting the validation samples, and when
p < 0.5 the sampling is conducted for selecting the calibration
samples (samples used for model fitting). The model fitted with the selected
calibration samples is used to predict the response values of the local
validation samples and the local root mean square error is computed.
This process is repeated \mjeqnmm times and the final local
error is computed as the average of the local root mean square errors
obtained for all the \mjeqnmm iterations. In the
\mjeqnmm is controlled by the
number argument and the size of the
subsets is controlled by the
p argument which indicates the
percentage of observations to be selected from the subset of nearest neighbors.
The global error of the predictions is computed as the average of the local
root mean square errors.
No validation (
"none"): No validation is carried out.
"none" is seleceted along with
"local_cv", then it will be ignored and the respective
validation(s) will be carried out.
list mirroring the specified parameters
Leonardo Ramirez-Lopez and Antoine Stevens
Ramirez-Lopez, L., Behrens, T., Schmidt, K., Stevens, A., Dematte, J.A.M., Scholten, T. 2013a. The spectrum-based learner: A new local approach for modeling soil vis-NIR spectra of complex data sets. Geoderma 195-196, 268-279.
Ramirez-Lopez, L., Behrens, T., Schmidt, K., Viscarra Rossel, R., Dematte, J. A. M., Scholten, T. 2013b. Distance and similarity-search metrics for use with soil vis-NIR spectra. Geoderma 199, 43-53.
# A control list with the default parameters mbl_control()
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