print.adlp | R Documentation |
Class to estimate an ADLP model fitted by Minorization-Maximisation.
## S3 method for class 'adlp'
print(x, ...)
adlp(components_lst, newdata, partition_func, param_tol = 1e-16, ...)
x |
Object of class |
... |
Other named parameters passed onto further functions |
components_lst |
List of |
newdata |
Validation data to fit the ADLP partitions on |
partition_func |
Partition function used to subset the data. ADLP weights
will be generated for each partition. To specify partition preferences,
set the parameter to |
param_tol |
Tolerance for weights. Any value less than tolerance in magnitude is assumed zero. |
See adlp_component and adlp_components objects for more information on required format for inputs.
See adlp_partition for information on valid partition functions.
For an understanding of how partitions affect the performance of the ADLP ensemble, one might refer to Avanzi, Li, Wong and Xian (2022)
Object of class adlp
. This object has the following components:
adlp_components; List of adlp_components, see
also adlp_components
vector; vector of model weights fitted for each component
function; Partition function used to fit the components
mm_optim; Details related to the MM algorithm
see also MM_optim()
data.frame; Data.frame used to fit the ADLP
Avanzi, B., Li, Y., Wong, B., & Xian, A. (2022). Ensemble distributional forecasting for insurance loss reserving. arXiv preprint arXiv:2206.08541.
data(test_adlp_component)
test_component1 <- test_adlp_component
test_component2 <- test_adlp_component
test_components <- adlp_components(
component1 = test_component1,
component2 = test_component2
)
newdata <- test_component1$model_train$data
test_adlp <- adlp(test_components, newdata = newdata, response_name = "claims",
partition_func = adlp_partition_ap, tri.size = 40, size = 3)
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