From f13231e0654d8564b1511da6c83ff66e719713ad Mon Sep 17 00:00:00 2001
From: Peder <pbac@dtu.dk>
Date: Sat, 21 Aug 2021 14:39:40 +0200
Subject: [PATCH] maybe final 0.10.0
---
R/step_optim.R | 17 ++++++++++-------
man/step_optim.Rd | 17 ++++++++++-------
2 files changed, 20 insertions(+), 14 deletions(-)
diff --git a/R/step_optim.R b/R/step_optim.R
index 44b628c..3c84ec3 100644
--- a/R/step_optim.R
+++ b/R/step_optim.R
@@ -122,19 +122,20 @@
#' # Select a model, in the optimization just run it for a single horizon
#' # Note that kseqopt could also be set
#' model$kseq <- 5
-#' #
+#'
+#' # Set the parameters to step on, note the
#' prm <- list(mu_tday__nharmonics = c(min=3, max=7))
#'
#' # Note the control argument, which is passed to optim, it's now set to few
-#' # Iterations in the prm optimization (MUST be increased in real applications)
+#' # iterations in the offline parameter optimization (MUST be increased in real applications)
#' control <- list(maxit=1)
#'
-#' # On Windows multi cores are not supported, so for the examples use only one
+#' # On Windows multi cores are not supported, so for the examples use only one core
#' mc.cores <- 1
#'
#' # Run the default selection scheme, which is "both"
#' # (same as "backwardboth" if no start model is given)
-#' L <- step_optim(model, D, prm, control=control, mc.cores=mc.cores)
+#' \donttest{L <- step_optim(model, D, prm, control=control, mc.cores=mc.cores)
#'
#' # The optim value from each step is returned
#' getse(L, "optimresult")
@@ -157,8 +158,10 @@
#' modelstart$inputs[2:3] <- NULL
#' L <- step_optim(model, D, prm, modelstart=modelstart, control=control, mc.cores=mc.cores)
#'
-#' # If a fitting function is given, then it will be used for calculating the forecasts
-#' # ONLY on the complete cases in each step
+#' # If a fitting function is given, then it will be used for calculating the forecasts.
+#' # Below it's the rls_fit function, so the same as used internally in rls_fit, so only
+#' # difference is that now ONLY on the complete cases for all models in each step are used
+#' # when calculating the score in each step
#' L1 <- step_optim(model, D, prm, fitfun=rls_fit, control=control, mc.cores=mc.cores)
#'
#' # The easiest way to conclude if missing values have an influence is to
@@ -168,7 +171,7 @@
#' # Compare the selected models
#' tmp1 <- capture.output(getse(L1, "model"))
#' tmp2 <- capture.output(getse(L2, "model"))
-#' identical(tmp1, tmp2)
+#' identical(tmp1, tmp2)}
#'
#'
#' # Note that caching can be really smart (the cache files are located in the
diff --git a/man/step_optim.Rd b/man/step_optim.Rd
index e42a743..febd2e6 100644
--- a/man/step_optim.Rd
+++ b/man/step_optim.Rd
@@ -151,19 +151,20 @@ model$add_prmbounds(lambda = c(0.9, 0.99, 0.9999))
# Select a model, in the optimization just run it for a single horizon
# Note that kseqopt could also be set
model$kseq <- 5
-#
+
+# Set the parameters to step on, note the
prm <- list(mu_tday__nharmonics = c(min=3, max=7))
# Note the control argument, which is passed to optim, it's now set to few
-# Iterations in the prm optimization (MUST be increased in real applications)
+# iterations in the offline parameter optimization (MUST be increased in real applications)
control <- list(maxit=1)
-# On Windows multi cores are not supported, so for the examples use only one
+# On Windows multi cores are not supported, so for the examples use only one core
mc.cores <- 1
# Run the default selection scheme, which is "both"
# (same as "backwardboth" if no start model is given)
-L <- step_optim(model, D, prm, control=control, mc.cores=mc.cores)
+\donttest{L <- step_optim(model, D, prm, control=control, mc.cores=mc.cores)
# The optim value from each step is returned
getse(L, "optimresult")
@@ -186,8 +187,10 @@ modelstart <- model$clone_deep()
modelstart$inputs[2:3] <- NULL
L <- step_optim(model, D, prm, modelstart=modelstart, control=control, mc.cores=mc.cores)
-# If a fitting function is given, then it will be used for calculating the forecasts
-# ONLY on the complete cases in each step
+# If a fitting function is given, then it will be used for calculating the forecasts.
+# Below it's the rls_fit function, so the same as used internally in rls_fit, so only
+# difference is that now ONLY on the complete cases for all models in each step are used
+# when calculating the score in each step
L1 <- step_optim(model, D, prm, fitfun=rls_fit, control=control, mc.cores=mc.cores)
# The easiest way to conclude if missing values have an influence is to
@@ -197,7 +200,7 @@ L2 <- step_optim(model, D, prm, control=control, mc.cores=mc.cores)
# Compare the selected models
tmp1 <- capture.output(getse(L1, "model"))
tmp2 <- capture.output(getse(L2, "model"))
-identical(tmp1, tmp2)
+identical(tmp1, tmp2)}
# Note that caching can be really smart (the cache files are located in the
--
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