View source: R/hermite_estimator.R
Note that the estimators must be of the same type to be merged i.e. both estimators must have a consistent est_type, either "univariate" or "bivariate". In addition, the N and standardize arguments must be the same for both estimators in order to merge them. Finally, note that exponentially weighted estimators cannot be merged. If the Hermite estimators are not standardized, the merged estimator will be exactly equivalent to constructing a single estimator on the data set formed by combining the data sets used to update the respective hermite_estimator inputs. If the input Hermite estimators are standardized however, then the equivalence will be approximate but still reasonably accurate in most cases.
A hermite_estimator_univar or hermite_estimator_bivar object. The first Hermite series based estimator.
A hermite_estimator_univar or hermite_estimator_bivar object. The second Hermite series based estimator.
An object of class hermite_estimator_univar or hermite_estimator_bivar.
## Not run: hermite_est_1 <- hermite_estimator(N = 10, standardize = FALSE, observations = rnorm(30)) hermite_est_2 <- hermite_estimator(N = 10, standardize = FALSE, observations = rnorm(30)) hermite_merged <- merge_pair(hermite_est_1, hermite_est_2) ## End(Not run)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.