# some checks are done here, maybe more should be added (??also when transforming inputs, if something goes wrong its caught and message is printed)
#
# ################################
# First check if the output is set correctly
if(is.na(self$output)){
stop("Model output is NA, it must be set to the name of a variable in the data.list used.")
}
...
...
@@ -230,29 +230,29 @@ forecastmodel <- R6::R6Class("forecastmodel", public = list(
if(length(data[[self$output]])!=length(data$t)){
stop("The length of the model output '",self$output,"' is ",length(data[[self$output]]),", which is not equal to the length of the time vector (t), which is ",length(data$t))
stop("The input variable '",nm,"' doesn't have all needed horizons.\nIt has ",pst(names(data[[nm]]),collapse=","),"\nIt is missing ",pst("k",self$kseq[missingk],collapse=","))
}
## Check if the number of observations match
# Check if the number of observations match
if(nrow(data[[nm]])!=length(data$t)){
stop(pst("The input variable '",nm,"' doesn't have the same number of observations as time vector 't' in the data. It has ",nrow(data[[nm]]),", but 't' has ",length(data$t)))
}
}elseif(class(data[[nm]])=="numeric"){
## Observation input, check the length
# Observation input, check the length
if(length(data[[nm]])!=length(data$t)){
stop("The input variable '",nm,"' doesn't have the same number of observations as time vector 't' in the data. It has ",length(data[[nm]]),", but 't' has ",length(data$t))
}
...
...
@@ -263,11 +263,11 @@ forecastmodel <- R6::R6Class("forecastmodel", public = list(
#' - It can be manimulated directly in functions (without return). The code is written such that no external functions manipulate the model object, except for online updating.
#'
#' For online updating (i.e. receiving new data and updating the fit), then the model definition and the data becomes entangled, since transformation functions like low-pass filtering with \code{\link{lp}()} requires past values.
#' See the vignette ??(ref to online vignette) and note that \code{\link{rls_fit}()} resets the state, which is also done in all \code{xxx_fit} functions (e.g. \code{\link{rls_fit}}.
#' See the vignette ??(ref to online vignette, not yet available) and note that \code{\link{rls_fit}()} resets the state, which is also done in all \code{xxx_fit} functions (e.g. \code{\link{rls_fit}}.
#'
#'
#' @section Public fields used for setting up the model:
...
...
@@ -52,7 +52,7 @@
#'
#' - Lfits = list(): The regression fits, one for each k in kseq (simply a list with the latest fit).
#'
#' - datatr = NA: Transformed input data (data.list with all inputs; or local fitted models: ??data.frame with all data??)
#' - datatr = NA: Transformed input data (data.list with all inputs for regression)
