Lag the time-series slots in a Data object by a specified number of time-steps

Lag the time-series slots in a Data object by a specified number of time-steps

Usage

Lag_Data(Data, Data_Lag = 0, msg = FALSE)

Arguments

Data: An object of class Data
Data_Lag: Either a numeric vector of length 1 with a positive number specifying the number of time-steps to lag all time-series data, or a named list with numeric values (length 1). See details for more information.
msg: Logical. Display the messages?

Value

An object of class Data with time-series slots lagged.

Details

By default, all simulated data in the forward projections are provided up to the previous time-step. That is, in projection year t, the simulated data are up to and including t-1. This function will lag the time-series values by the specified value. For example, Data_Lag=5 will mean in projection time-step t the data will be up to and including t-6.

Note: The Data@Year slot is not lagged by this function. Many built-in MPs use the length of Data@Year to determine the number of years of data for smoothing over recent years etc. This may not be appropriate so check the MP is behaving as you expect if you use Lag_Data.

Examples

# Lag all time-series slots by 2 time-steps (usually years)
Data <- Example_datafile
Lagged_1 <- Lag_Data(Data, 2)
length(Data@Year)
#> [1] 13
length(Lagged_1@Year)
#> [1] 13
length(Data@Cat[1,])
#> [1] 13
length(Lagged_1@Cat[1,])
#> [1] 11
length(Data@Ind[1,])
#> [1] 13
length(Lagged_1@Ind[1,])
#> [1] 11

# Lag CAA by 5 and Ind by 3 time-steps
Lagged_2 <- Lag_Data(Data, Data_Lag=list(CAA=5, Ind=3))
length(Lagged_2@Year)
#> [1] 13
length(Lagged_2@Cat[1,])
#> [1] 13
dim(Data@CAA[1,,])
#> [1] 13 16
dim(Lagged_2@CAA[1,,])
#> [1]  8 16

length(Data@Ind[1,])
#> [1] 13
length(Lagged_2@Ind[1,])
#> [1] 10