Fixed for check, submitted as version 0.10.0

DESCRIPTION

 Package: onlineforecast
 Type: Package
 Title: Forecast Modelling for Online Applications
-Version: 0.9.4
+Version: 0.10.0
 Description: A framework for fitting adaptive forecasting models. Provides a way to use forecasts as input to models, e.g. weather forecasts for energy related forecasting. The models can be fitted recursively and can easily be setup for updating parameters when new data arrives. See the included vignettes, the website <https://onlineforecasting.org> and the paper "Short-term heat load forecasting for single family houses" <doi:10.1016/j.enbuild.2013.04.022>.
 License: GPL-3
 Encoding: UTF-8

R/as.data.list.R

@@ -21,7 +21,7 @@
 #' @title Convert to data.list class
 #' @param object The object to be converted into a data.list
 #' @return a value of class data.list
-#' @seealso \code{For specific detailed info see the children, e.g. \link{onlinefocast:::as.data.list.data.frame} }
+#' @seealso \code{For specific detailed info see the children, e.g. \link{as.data.list.data.frame} }
 #' 
 #' @rdname as.data.list
 #' @export

R/in_range.R

@@ -19,7 +19,6 @@
 #' @param tstart The start of the period.
 #' @param time The timestamps as POSIX.
 #' @param tend The end of the period. If not given then the period will have no end. 
-#' @param timezone The timezone of the timestamps, time.
 #' @return A logical vector indicating the selected period with TRUE
 #' @name in_range
 #' @examples

R/lagdf.R

@@ -22,9 +22,9 @@
 #' lagdf(1:10, 3)
 #' # Back in time
 #' lagdf(1:10, -3)
-#' # Works but returns a numric
+#' # Works but returns a numeric column
 #' lagdf(as.factor(1:10), 3)
-#' # Works and returns a character
+#' # Works and returns a character column
 #' lagdf(as.character(1:10), 3)
 #' # Giving several lag values
 #' lagdf(1:10, c(1:3))

R/lapply.R

@@ -7,6 +7,7 @@
 #' Helper which does lapply and then cbind
 #' @param X object to apply on
 #' @param FUN function to apply
+#' @param ... passed on to lapply
 #' @export
 lapply_cbind <- function(X, FUN, ...){
   val <- lapply(X, FUN, ...)
@@ -16,6 +17,7 @@ lapply_cbind <- function(X, FUN, ...){
 #' Helper which does lapply and then rbind
 #' @param X object to apply on
 #' @param FUN function to apply
+#' @param ... passed on to lapply
 #' @export
 lapply_rbind <- function(X, FUN, ...){
   val <- lapply(X, FUN, ...)
@@ -25,6 +27,7 @@ lapply_rbind <- function(X, FUN, ...){
 #' Helper which does lapply, cbind and then as.data.frame
 #' @param X object to apply on
 #' @param FUN function to apply
+#' @param ... passed on to lapply
 #' @export
 lapply_cbind_df <- function(X, FUN, ...){
   val <- lapply(X, FUN, ...)
@@ -34,6 +37,7 @@ lapply_cbind_df <- function(X, FUN, ...){
 #' Helper which does lapply, rbind and then as.data.frame
 #' @param X object to apply on
 #' @param FUN function to apply
+#' @param ... passed on to lapply
 #' @export
 lapply_rbind_df <- function(X, FUN, ...){
   val <- lapply(X, FUN, ...)

R/step_optim.R

@@ -55,11 +55,13 @@
 #' the score will be calculated using only the complete cases across horizons
 #' and models in each step, see the last examples.
 #'
+#' Note, that either kseq or kseqopt must be set on the modelfull object. If kseqopt
+#' is set, then it is used no matter the value of kseq.
+#'
 #' @title Forward and backward model selection
 #' @param modelfull The full forecastmodel containing all inputs which will be
 #'     can be included in the selection.
 #' @param data The data.list which holds the data on which the model is fitted.
-#' @param kseq The horizons to fit for (if not set, then model$kseq is used)
 #' @param prm A list of integer parameters to be stepped. Given using the same
 #'     syntax as parameters for optimization, e.g. `list(I__degree = c(min=3,
 #'     max=7))` will step the "degree" for input "I".
@@ -78,6 +80,7 @@
 #'     in rls_optim()). Furthermore, information on complete cases are printed
 #'     and returned.
 #' @param scorefun The score function used.
+#' @param printout Logical. Passed on to fitting functions.
 #' @param mc.cores The mc.cores argument of mclapply. If debugging it can be
 #'     nessecary to set it to 1 to stop execution.
 #' @param ... Additional arguments which will be passed on to optimfun. For
@@ -117,7 +120,8 @@
 #' model$add_prmbounds(lambda = c(0.9, 0.99, 0.9999))
 #' 
 #' # Select a model, in the optimization just run it for a single horizon
-#' model$kseqopt <- 5
+#' # Note that kseqopt could also be set
+#' model$kseq <- 5
 #' # 
 #' prm <- list(mu_tday__nharmonics = c(min=3, max=7))
 #' 
@@ -125,7 +129,8 @@
 #' # Iterations in the prm optimization (MUST be increased in real applications)
 #' control <- list(maxit=1)
 #'
-#' # Run the default selection scheme, which is "both" (same as "backwardboth" if no start model is given)
+#' # 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)
 #'
 #' # The optim value from each step is returned
@@ -142,7 +147,7 @@
 #' Lforwardboth <- step_optim(model, D, prm, "forwardboth", control=control, mc.cores=1)
 #'
 #' # It's possible avoid removing specified inputs
-#' L <- step_optim(model, D, prm, keepinputs = c("mu","mu_tday"), control=control)
+#' L <- step_optim(model, D, prm, keepinputs=c("mu","mu_tday"), control=control)
 #' 
 #' # Give a starting model
 #' modelstart <- model$clone_deep()
@@ -172,7 +177,7 @@
 #'
 #' @export
 
-step_optim <- function(modelfull, data, prm=list(NA), kseq = NA, direction = c("both","backward","forward","backwardboth","forwardboth"), modelstart=NA, keepinputs = FALSE, optimfun = rls_optim, fitfun = NA, scorefun = rmse, printout = FALSE, mc.cores = getOption("mc.cores", 2L), ...){
+step_optim <- function(modelfull, data, prm=list(NA), direction = c("both","backward","forward","backwardboth","forwardboth"), modelstart=NA, keepinputs = FALSE, optimfun = rls_optim, fitfun = NA, scorefun = rmse, printout = FALSE, mc.cores = getOption("mc.cores", 2L), ...){
     # Do:
     # - checking of input, model, ...
     # - Maybe have "cloneit" argument in optimfun, then don't clone inside optim.
@@ -191,7 +196,7 @@ step_optim <- function(modelfull, data, prm=list(NA), kseq = NA, direction = c("
     istep <- 1
     # Different start up, if a start model is given
     if( class(modelstart)[1] == "forecastmodel" ){
-        # The full model will not be changed from here, so don't need to clone it
+        # The full model will not be changed from here, so no need to clone it
         mfull <- modelfull
         m <- modelstart$clone()
     }else{
@@ -227,6 +232,10 @@ step_optim <- function(modelfull, data, prm=list(NA), kseq = NA, direction = c("
             scoreCurrent <- Inf
         }
     }
+    # If kseqopt is set, then make sure that it is used in all runs (also when only running fitfun)
+    if(!is.na(m$kseqopt)){
+        m$kseq <- m$kseqopt
+    }
     # Find the inputs to keep, if any
     if(class(keepinputs) == "logical"){
         if(keepinputs){
@@ -250,14 +259,13 @@ step_optim <- function(modelfull, data, prm=list(NA), kseq = NA, direction = c("
         # If the init model is not yet optimized
         if(istep == 1 & length(L) == 0){
             # Optimize
-            res <- optimfun(m, data, kseq, printout=printout, scorefun=scorefun, ...)
+            res <- optimfun(m, data, printout=printout, scorefun=scorefun, ...)
             # Should we forecast only on the complete cases?
             if(class(fitfun) == "function"){
                 # Forecast to get the complete cases
                 mtmp <- m$clone_deep()
-                mtmp$kseq <- kseq
                 Yhat <- fitfun(res$par, mtmp, data, printout=printout)$Yhat
-                scoreCurrent <- sum(score(residuals(Yhat,data[[model$output]]),data$scoreperiod))
+                scoreCurrent <- sum(score(residuals(Yhat,data[[m$output]]),data$scoreperiod))
                 casesCurrent <- complete_cases(Yhat)
             }else{
                 scoreCurrent <- res$value
@@ -360,7 +368,7 @@ step_optim <- function(modelfull, data, prm=list(NA), kseq = NA, direction = c("
 
             # Run the optimization
             Lstep <- mclapply(1:length(mStep), function(i, ...){
-                optimfun(mStep[[i]], data, kseq, printout=printout, ...)
+                optimfun(mStep[[i]], data, printout=printout, scorefun=scorefun, ...)
             }, mc.cores=mc.cores, ...)
             names(Lstep) <- names(mStep)
 
@@ -368,11 +376,10 @@ step_optim <- function(modelfull, data, prm=list(NA), kseq = NA, direction = c("
             if(class(fitfun) == "function"){
                 LYhat <- mclapply(1:length(mStep), function(i){
                     mtmp <- mStep[[i]]$clone_deep()
-                    mtmp$kseq <- kseq
                     fitfun(Lstep[[i]]$par, mtmp, data, printout=printout)$Yhat
                 }, mc.cores=mc.cores)
                 # Use complete cases across models and horizons per default
-                scoreStep <- apply(score(residuals(LYhat,data[[model$output]]), data$scoreperiod), 2, sum)
+                scoreStep <- apply(score(residuals(LYhat,data[[m$output]]), data$scoreperiod), 2, sum)
                 casesStep <- sapply(LYhat, complete_cases)
             }else{
                 # Use the scores from optimfun
@@ -393,7 +400,7 @@ step_optim <- function(modelfull, data, prm=list(NA), kseq = NA, direction = c("
                 tmp <- cbind(tmp, apply(casesStep != casesCurrent, 2, sum))
                 nams(tmp)[2] <- "CasesDiff"
             }
-            onlineforecast:::print_to_message(tmp)
+            print_to_message(tmp)
 
             # Compare scores: Is one the step models score smaller than the current ref?
             imin <- which.min(scoreStep)

cran-comments.md

+#----------------------------------------------------------------
+# v0.10.0
+
+We have added features and done some small changes to functions. This version
+should be fully backward compatible.
+
+
+
+
+
+
+
 #----------------------------------------------------------------
 # v0.9.3
 # Response to review of v0.9.2 by Uwe Ligges

make.R

@@ -50,8 +50,8 @@ library(roxygen2)
 #test()
 
 # # Run the examples
-#load_all(as.package("../onlineforecast"))
-#run_examples()
+load_all(as.package("../onlineforecast"))
+run_examples()
 
 # # Run tests in a single file
 # test_file("tests/testthat/test-rls-heat-load.R")
@@ -60,10 +60,10 @@ library(roxygen2)
 # ----------------------------------------------------------------
 # Build the package
 document()
-build(".", vignettes=FALSE)
+build(".", vignettes=TRUE)
 
 # Install it
-install.packages("../onlineforecast_0.9.4.tar.gz")
+install.packages("../onlineforecast_0.10.0.tar.gz")
 
 library(onlineforecast)
 # ----------------------------------------------------------------
@@ -80,11 +80,11 @@ library(onlineforecast)
 # Test before release
 devtools::check()
 
-devtools::check_built("../onlineforecast_0.9.4.tar.gz")
+devtools::check_built("../onlineforecast_0.10.0.tar.gz")
 
 # Does give different results than check() above
 #system("R CMD check --as-cran ../onlineforecast_0.9.4.tar.gz")
-system("R CMD check ../onlineforecast_0.9.4.tar.gz")
+system("R CMD check ../onlineforecast_0.10.0.tar.gz")
 unlink("onlineforecast.Rcheck/", recursive=TRUE)
 
 # Use for more checking:

man/as.data.list.Rd

@@ -51,7 +51,7 @@ as.data.frame(as.data.list(X))
 
 }
 \seealso{
-\code{For specific detailed info see the children, e.g. \link{onlinefocast:::as.data.list.data.frame} }
+\code{For specific detailed info see the children, e.g. \link{as.data.list.data.frame} }
 
 as.data.list
 }

man/in_range.Rd

@@ -12,8 +12,6 @@ in_range(tstart, time, tend = NA)
 \item{time}{The timestamps as POSIX.}
 
 \item{tend}{The end of the period. If not given then the period will have no end.}
-
-\item{timezone}{The timezone of the timestamps, time.}
 }
 \value{
 A logical vector indicating the selected period with TRUE

man/lagdf.Rd

@@ -36,9 +36,9 @@ This function lags the columns with the integer values specified with the argume
 lagdf(1:10, 3)
 # Back in time
 lagdf(1:10, -3)
-# Works but returns a numric
+# Works but returns a numeric column
 lagdf(as.factor(1:10), 3)
-# Works and returns a character
+# Works and returns a character column
 lagdf(as.character(1:10), 3)
 # Giving several lag values
 lagdf(1:10, c(1:3))

man/lapply_cbind.Rd

@@ -10,6 +10,8 @@ lapply_cbind(X, FUN, ...)
 \item{X}{object to apply on}
 
 \item{FUN}{function to apply}
+
+\item{...}{passed on to lapply}
 }
 \description{
 Helper which does lapply and then cbind

man/lapply_cbind_df.Rd

@@ -10,6 +10,8 @@ lapply_cbind_df(X, FUN, ...)
 \item{X}{object to apply on}
 
 \item{FUN}{function to apply}
+
+\item{...}{passed on to lapply}
 }
 \description{
 Helper which does lapply, cbind and then as.data.frame

man/lapply_rbind.Rd

@@ -10,6 +10,8 @@ lapply_rbind(X, FUN, ...)
 \item{X}{object to apply on}
 
 \item{FUN}{function to apply}
+
+\item{...}{passed on to lapply}
 }
 \description{
 Helper which does lapply and then rbind

man/lapply_rbind_df.Rd

@@ -10,6 +10,8 @@ lapply_rbind_df(X, FUN, ...)
 \item{X}{object to apply on}
 
 \item{FUN}{function to apply}
+
+\item{...}{passed on to lapply}
 }
 \description{
 Helper which does lapply, rbind and then as.data.frame

man/step_optim.Rd

@@ -8,7 +8,6 @@ step_optim(
   modelfull,
   data,
   prm = list(NA),
-  kseq = NA,
   direction = c("both", "backward", "forward", "backwardboth", "forwardboth"),
   modelstart = NA,
   keepinputs = FALSE,
@@ -30,8 +29,6 @@ can be included in the selection.}
 syntax as parameters for optimization, e.g. `list(I__degree = c(min=3,
 max=7))` will step the "degree" for input "I".}
 
-\item{kseq}{The horizons to fit for (if not set, then model$kseq is used)}
-
 \item{direction}{The direction to be used in the selection process.}
 
 \item{modelstart}{A forecastmodel. If it's set then it will be used as the
@@ -53,6 +50,8 @@ and returned.}
 
 \item{scorefun}{The score function used.}
 
+\item{printout}{Logical. Passed on to fitting functions.}
+
 \item{mc.cores}{The mc.cores argument of mclapply. If debugging it can be
 nessecary to set it to 1 to stop execution.}
 
@@ -118,6 +117,9 @@ models, it can be very important to make sure that only complete cases are
 included when calculating the score. By providing the `fitfun` argument then
 the score will be calculated using only the complete cases across horizons
 and models in each step, see the last examples.
+
+Note, that either kseq or kseqopt must be set on the modelfull object. If kseqopt
+is set, then it is used no matter the value of kseq.
 }
 \examples{
 
@@ -147,7 +149,8 @@ model$add_regprm("rls_prm(lambda=0.9)")
 model$add_prmbounds(lambda = c(0.9, 0.99, 0.9999))
 
 # Select a model, in the optimization just run it for a single horizon
-model$kseqopt <- 5
+# Note that kseqopt could also be set
+model$kseq <- 5
 # 
 prm <- list(mu_tday__nharmonics = c(min=3, max=7))
 
@@ -155,7 +158,8 @@ prm <- list(mu_tday__nharmonics = c(min=3, max=7))
 # Iterations in the prm optimization (MUST be increased in real applications)
 control <- list(maxit=1)
 
-# Run the default selection scheme, which is "both" (same as "backwardboth" if no start model is given)
+# 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)
 
 # The optim value from each step is returned
@@ -172,7 +176,7 @@ Lbackwardboth <- step_optim(model, D, prm, "backwardboth", control=control)
 Lforwardboth <- step_optim(model, D, prm, "forwardboth", control=control, mc.cores=1)
 
 # It's possible avoid removing specified inputs
-L <- step_optim(model, D, prm, keepinputs = c("mu","mu_tday"), control=control)
+L <- step_optim(model, D, prm, keepinputs=c("mu","mu_tday"), control=control)
 
 # Give a starting model
 modelstart <- model$clone_deep()

vignettes/forecast-evaluation.Rmd

@@ -7,7 +7,7 @@ output:
     toc: true
     toc_debth: 3
 vignette: >
-  %\VignetteIndexEntry{Online updating of onlineforecast models}
+  %\VignetteIndexEntry{Forecast evaluation}
   %\VignetteEngine{knitr::rmarkdown}
   %\VignetteEncoding{UTF-8}
 ---

vignettes/model-selection.Rmd

@@ -150,8 +150,8 @@ model$add_prmbounds(lambda = c(0.9, 0.99, 0.9999))
 ```
 Finally, set the horizons to run (just keep a vector for later use):
 ```{r}
-# Select a model, just run it for a single horizon
-kseq <- 5
+# Select a model, just run optimization and score for a single horizon
+model$kseq <- 5
 ```
 
 Now we can use the `step_optim()` function for the selection.  In each step new models are generated, with either one removed input or one added input, and     then all the generated models are optimized and their scores compared. If     any new model have an improved score compared to the currently selected     model, then the new is selected and the process is repeated until no new     improvement is achieved.
@@ -184,7 +184,7 @@ The default procedure is backward selection with stepping in both directions:
 # Run the default selection, which is "both" and equivalent to "backwadboth"
 # Note the control argument, which is passed to optim, it's now set to few
 # iterations in the prm optimization
-Lboth <- step_optim(model, D, kseq, prm, direction="both", control=list(maxit=1))
+Lboth <- step_optim(model, D, prm, direction="both", control=list(maxit=1))
 ```
 We now have the models selected in each step in and we see that the final model
 is decreased:
@@ -194,14 +194,14 @@ getse(Lboth, "model")
 
 Forward selection:
 ```{r, message=FALSE, results="hide"}
-Lforward <- step_optim(model, D, kseq, prm, "forward", control=list(maxit=1))
+Lforward <- step_optim(model, D, prm, "forward", control=list(maxit=1))
 ```
 ```{r}
 getse(Lforward, "model")
 ```
 Same model is selected, which is also the case in backwards selection:
 ```{r, message=FALSE, results="hide"}
-Lbackward <- step_optim(model, D, kseq, prm, "backward", control=list(maxit=1))
+Lbackward <- step_optim(model, D, prm, "backward", control=list(maxit=1))
 ```
 ```{r}
 getse(Lbackward, "model")
@@ -215,7 +215,7 @@ modelstart <- model$clone_deep()
 # Remove two inputs
 modelstart$inputs[2:3] <- NULL
 # Run the selection
-L <- step_optim(model, D, kseq, prm, modelstart=modelstart, control=control)
+L <- step_optim(model, D, prm, modelstart=modelstart, control=list(maxit=1))
 ```
 ```{r}
 getse(L, "model")