Uncertainty estimates: Move common docu to template (#280)

Author: pschil

R/f_interface_predict_clvfittedspending.R

@@ -27,6 +27,10 @@
 #'
 #' To predict for new customers, the output of \link[CLVTools:newcustomer]{newcustomer.spending} has to be given to \code{newdata}. See examples.
 #'
+#'
+#' @template template_predict_uncertainty
+#'
+#'
 #' @seealso models to predict spending: \link{gg}.
 #' @seealso models to predict transactions: \link{pnbd}, \link{bgnbd}, \link{ggomnbd}.
 #' @seealso \code{\link[CLVTools:predict.clv.fitted.transactions]{predict}} for transaction models

R/f_interface_predict_clvfittedtransactions.R

@@ -76,25 +76,8 @@
 #' To account for time units which are not annual, the continuous rate has to be further adjusted
 #' to delta=ln(1+d)/k, where k are the number of time units in a year.
 #'
-#' @section Uncertainty Estimates:
-#' Bootstrapping is used to provide confidence intervals of all predicted metrics.
-#' These provide an estimate of parameter uncertainty.
-#' To create bootstrapped data, customer ids are sampled with replacement until reaching original
-#' length and all transactions of the sampled customers are used to create a new \code{clv.data} object.
-#' A new model is fit on the bootstrapped data with the exact same specification as used when
-#' fitting \code{object} (incl. start parameters and `optimx.args`) and it is then used to predict on this data.
-#'
-#' It is highly recommended to fit the original model (\code{object}) with a robust optimization
-#' method, such as Nelder-Mead (\code{optimx.args=list(method='Nelder-Mead')}).
-#' This ensures that the model can also be fit on the bootstrapped data.
-#'
-#' All prediction parameters, incl \code{prediction.end} and \code{continuous.discount.factor}, are forwarded
-#' to the prediction on the bootstrapped data.
-#' Per customer, the boundaries of the confidence intervals of each predicted metric are the
-#' sample quantiles (\code{quantile(x, probs=c((1-level)/2, 1-(1-level)/2)}).
-#'
-#' See \link{clv.bootstrapped.apply} to create a custom bootstrapping procedure.
 #'
+#' @template template_predict_uncertainty
 #'
 #'
 #' @seealso models to predict transactions: \link{pnbd}, \link{bgnbd}, \link{ggomnbd}.

man-roxygen/template_predict_uncertainty.R

@@ -0,0 +1,24 @@
+#'
+#'
+#'
+#' @section Uncertainty Estimates:
+#' Bootstrapping is used to provide confidence intervals of all predicted metrics.
+#' These provide an estimate of parameter uncertainty.
+#' To create bootstrapped data, customer ids are sampled with replacement until reaching original
+#' length and all transactions of the sampled customers are used to create a new \code{clv.data} object.
+#' A new model is fit on the bootstrapped data with the exact same specification as used when
+#' fitting \code{object} (incl. start parameters and `optimx.args`) and it is then used to predict on this data.
+#'
+#' It is highly recommended to fit the original model (\code{object}) with a robust optimization
+#' method, such as Nelder-Mead (\code{optimx.args=list(method='Nelder-Mead')}).
+#' This ensures that the model can also be fit on the bootstrapped data.
+#'
+#' All prediction parameters, incl \code{prediction.end} and \code{continuous.discount.factor}, are forwarded
+#' to the prediction on the bootstrapped data.
+#' Per customer, the boundaries of the confidence intervals of each predicted metric are the
+#' sample quantiles (\code{quantile(x, probs=c((1-level)/2, 1-(1-level)/2)}).
+#'
+#' See \link{clv.bootstrapped.apply} to create a custom bootstrapping procedure.
+#'
+#'
+#'