mbl_control  R Documentation 
mbl
functionThis function is used to further control some aspects of the memorybased
learning process in the mbl
function.
mbl_control(return_dissimilarity = FALSE, validation_type = c("NNv", "local_cv"), tune_locally = TRUE, number = 10, p = 0.75, range_prediction_limits = TRUE, progress = TRUE, allow_parallel = TRUE)
return_dissimilarity 
a logical indicating if the dissimilarity matrix
between 
validation_type 
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: 
tune_locally 
a logical. It only applies when

number 
an integer indicating the number of sampling iterations at
each local segment when 
p 
a numeric value indicating the percentage of observations to be retained
at each sampling iteration at each local segment when 
range_prediction_limits 
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 
progress 
a logical indicating whether or not to print a progress bar
for each observation to be predicted. Default is 
allow_parallel 
a logical indicating if parallel execution is allowed.
If 
The validation methods available for assessing the predictive performance of the memorybased learning method used are described as follows:
Leavenearestneighborout crossvalidation ("NNv"
): From
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 target response variable of the nearest observation. These predicted
values are finally cross validated with the actual values (See RamirezLopez
et al. (2013a) for additional details). This method is faster than
"local_cv"
.
Local leavegroupout crossvalidation ("local_cv"
): The
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 which takes into account the values of the
response variable of the corresponding set of neighbors. The selected
local subset is used as local validation subset and the remaining observations
are used for fitting a model. This model is used to predict the target
response variable values of the local validation subset 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 error of all the \mjeqnmm iterations. In the mbl
function
\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 neighbours.
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.
If "none"
is seleceted along with "NNv"
and/or
"local_cv"
, then it will be ignored and the respective
validation(s) will be carried out.
a list
mirroring the specified parameters
Leonardo RamirezLopez and Antoine Stevens
RamirezLopez, L., Behrens, T., Schmidt, K., Stevens, A., Dematte, J.A.M., Scholten, T. 2013a. The spectrumbased learner: A new local approach for modeling soil visNIR spectra of complex data sets. Geoderma 195196, 268279.
RamirezLopez, L., Behrens, T., Schmidt, K., Viscarra Rossel, R., Dematte, J. A. M., Scholten, T. 2013b. Distance and similaritysearch metrics for use with soil visNIR spectra. Geoderma 199, 4353.
f_diss
, cor_diss
, sid
,
ortho_diss
, mbl
# A control list with the default parameters mbl_control()
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