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#' @title Lags
#'
#' @description Compute the lags for the mat.y time series matrix
#'
#' @param mat.y The matrix of time series
#' @param q The lag chosen
#'
#' @return A list of original (dependent) and lagged (independent) time series matrix
#' @export
#' @importFrom dplyr "%>%"
#' @examples
#' data(example_data)
#' list.lags <- Lags(mat.y = example_data, q = 2)
Lags <- function(mat.y,
q) {
#get the dimensions
p <-
dim(mat.y)[1]
n <- dim(mat.y)[2]
#optional: keep the rownames dates of the data frame with final matching
my.dates <-
rownames(mat.y)[(q + 1):p]
#matrix conversion
mat.y <-
data.matrix(mat.y)
#create an empty matrix
mat.y.lag <-
matrix(data = NA, nrow =
(p - q), ncol =
(n * (q + 1)))
for (i in 0:q) {
mat.y.lag[, (n * i + 1):(n * (i + 1))] <-
mat.y[(q - i + 1):(p - i),]
}
mat.y <- data.matrix(mat.y.lag[, 1:n])
mat.y.lag <- data.matrix(mat.y.lag[, (n + 1):dim(mat.y.lag)[2]])
return(list(
mat.y = mat.y,
mat.y.lag = mat.y.lag,
my.dates = my.dates
))
}
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