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)
}