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#' @importFrom stats tsp
#' @importFrom stats tsp<-
#' @importFrom stats ts
#' @name components
#' @rdname components
#'
#' @title Extract STR components
#' @description \code{components} extracts components as time series from the result of an STR decomposition.
#' @seealso \code{\link{STRmodel}}, \code{\link{RSTRmodel}}, \code{\link{STR}}, \code{\link{AutoSTR}}
#' @param object Result of STR decomposition.
#' @author Alexander Dokumentov
#' @examples
#' \donttest{
#' fit <- AutoSTR(log(grocery))
#' comp <- components(fit)
#' plot(comp)
#' }
#' @export
components <- function(object) {
# Set up matrix for components
l <- length(object$input$data)
n <- length(object$output$predictors) + 2 # predictors, original data and random component
m <- matrix(0, l, n)
# Add data to first column and random to last column
m[, 1] <- as.vector(object$input$data)
m[, ncol(m)] <- as.vector(object$output$random$data)
names <- rep("", ncol(m))
names[c(1, ncol(m))] <- c("Data", "Random")
# Add remaining components
for (i in seq_along(object$output$predictors)) {
m[, i + 1] <- object$output$predictors[[i]]$data
names[i + 1] <- object$input$predictors[[i]]$name
}
colnames(m) <- names
# Return result with ts attributes
m <- ts(m)
if ("ts" %in% class(object$input$data)) {
tsp(m) <- tsp(object$input$data)
}
return(m)
}
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