This function is a wrapper around lag (note that not all
arguments are supported). The default value for the lag is the mean of the
series.

```
spm_lag(sspm_object, vars, n = 1, default = "mean", ...)
# S4 method for sspm
spm_lag(sspm_object, vars, n = 1, default = "mean", ...)
# S4 method for sspm_fit
spm_lag(sspm_object, vars, n = 1, default = "mean", ...)
```

## Arguments

- sspm_object
**[sspm_dataset]** An object of class
sspm_dataset.

- vars
**[character]** Names of the variables to lag.

- n
Positive integer of length 1, giving the number of positions to
lead or lag by

- default
Value used for non-existent rows. Defaults to `NA`

.

- ...
a list of variables that are the covariates that this
smooth is a function of. Transformations whose form depends on
the values of the data are best avoided here: e.g. `s(log(x))`

is fine, but `s(I(x/sd(x)))`

is not (see `predict.gam`

).

## Value

Updated `sspm_object`

.

## Examples

```
if (FALSE) {
sspm_model <- sspm_model %>%
spm_lag(vars = c("weight_per_km2_borealis_with_catch",
"weight_per_km2_all_predators"),
n = 1)
}
```