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AR.R

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    AR.R 3.73 KiB
    ## Do this in a separate file to see the generated help:
    #library(devtools)
    #document()
    #load_all(as.package("../../onlineforecast"))
    #?AR
    
    #' Generate auto-regressive (AR) inputs in a model
    #'
    #' The AR function can be used in an onlineforecast model formulation. It
    #' creates the input matrices for including AR inputs in a model during the
    #' transformation stage. It takes the values from the model output in the provided data
    #' does the needed lagging.
    #'
    #' The lags must be given according to the one-step ahead model, e.g.:
    #'
    #' \code{AR(lags=c(0,1))} will give: Y_{t+1|t} = \eqn{\phi_1} y_{t-0} + \eqn{\phi_2} y_{t-1} + \eqn{\epsilon}_{t+1}
    #'
    #' and:
    #'
    #' \code{AR(lags=c(0,3,12))} will give: Y_{t+1|t} = \eqn{\phi}_1 y_{t-0} + \eqn{\phi}_2 y_{t-3} + \eqn{\phi}_3 y_{t-12} + \eqn{\epsilon}_{t+1}
    #'
    #' Note, that 
    #'
    #' For k>1 the coefficients will be fitted individually for each horizon, e.g.:
    #' 
    #' \code{AR(lags=c(0,1))} will be the multi-step AR: Y_{t+k|t} = \eqn{\phi}_{1,k} y_{t-0} + \eqn{\phi}_{2,k} y_{t-1} + \eqn{\epsilon}_{t+k|t}
    #'
    #' See the details in ??(ref til vignette).
    #' 
    #' @title Auto-Regressive (AR) input
    #' @param lags integer vector: The lags of the AR to include.
    #' @return A list of matrices, one for each lag in lags, each with columns according to model$kseq.
    #' @examples
    #'
    #' # Setup data and a model for the example
    #'
    #' model <- forecastmodel$new()
    #' model$output = "heatload"
    #' # Use the AR in the transformation stage
    #' model$add_inputs(AR = "AR(c(0,1))")
    #' # Regression parameters
    #' model$add_regprm("rls_prm(lambda=0.9)")
    #' # kseq must be added
    #' model$kseq <- 1:4
    #' # In the transformation stage the AR input will be generated
    #' # See that it generates two input matrices, simply with the lagged heat load at t for every k
    #' model$transform_data(subset(D, 1:10))
    
    #' # Fit with recursive least squares (no parameters prm in the model)
    #' fit <- rls_fit(c(lambda=0.99), model, D, returnanalysis=TRUE)
    
    #' # Plot the result, see "?plot_ts.rls_fit"
    #' plot(fit, xlim=c(asct("2010-12-20"),max(D$t)))
    #' # Plot for a short period with peaks
    #' plot(fit, xlim=c("2011-01-05","2011-01-07"))
    
    #' # For online updating, see ??ref{vignette}:
    #' # the needed lagged output values are stored in the model for next time new data is available
    #' model$yAR
    #' # The maximum lag needed is also kept
    #' model$maxlagAR
    #'
    #' @export
    
    AR <- function(lags){
        # Just sort them first
        lags <- sort(lags)
        # Get the data and the model from an environment above (this way has worked until now, not exactly sure why the environments are in this order)
        data <- parent.env(parent.frame())$data
        model <- parent.env(parent.frame())$self$model