# Lag the time-series slots in a `Data`

object by a specified number of time-steps

Source: `R/Data_Functions.R`

`Lag_Data.Rd`

Lag the time-series slots in a `Data`

object by a specified number of time-steps

## 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
```