A full sspm formula contains calls to the smoothing terms smooth_time(), smooth_space(), smooth_space_time().

smooth_time(
data_frame,
boundaries,
time,
type = "ICAR",
k = NULL,
bs = "re",
xt = NA,
is_spm = FALSE,
...
)

smooth_space(
data_frame,
boundaries,
time,
type = "ICAR",
k = NULL,
bs = "mrf",
xt = NULL,
is_spm = FALSE,
...
)

smooth_space_time(
data_frame,
boundaries,
time,
type = "ICAR",
k = c(NA, 30),
bs = c("re", "mrf"),
xt = list(NA, NULL),
is_spm = FALSE,
...
)

smooth_lag(
var,
data_frame,
boundaries,
time,
type = "LINPRED",
k = 5,
m = 1,
...
)

# S4 method for sf,sspm_discrete_boundary
smooth_time(
data_frame,
boundaries,
time,
type = "ICAR",
k = NULL,
bs = "re",
xt = NA,
is_spm = FALSE,
...
)

# S4 method for sf,sspm_discrete_boundary
smooth_space(
data_frame,
boundaries,
time,
type = "ICAR",
k = NULL,
bs = "mrf",
xt = NULL,
is_spm = FALSE,
...
)

# S4 method for sf,sspm_discrete_boundary
smooth_space_time(
data_frame,
boundaries,
time,
type = "ICAR",
k = c(NA, 30),
bs = c("re", "mrf"),
xt = list(NA, NULL),
is_spm = FALSE,
...
)

# S4 method for ANY,sf,sspm_discrete_boundary
smooth_lag(
var,
data_frame,
boundaries,
time,
type = "LINPRED",
k = 5,
m = 1,
...
)

## Arguments

data_frame

[sf data.frame] The data.

boundaries

[sspm_boundary] An object of class sspm_discrete_boundary.

time

[character] The time column.

type

[character] Type of smooth, currently only "ICAR" is supported.

k

[numeric] Size of the smooths and/or size of the lag.

bs

a two letter character string indicating the (penalized) smoothing basis to use. (eg "tp" for thin plate regression spline, "cr" for cubic regression spline). see smooth.terms for an over view of what is available.

xt

Any extra information required to set up a particular basis. Used e.g. to set large data set handling behaviour for "tp" basis. If xt\$sumConv exists and is FALSE then the summation convention for matrix arguments is turned off.

is_spm

Whether or not an SPM is being fitted (used internally)

...

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).

var

[symbol] Variable (only for smooth_lag).

m

The order of the penalty for this term (e.g. 2 for normal cubic spline penalty with 2nd derivatives when using default t.p.r.s basis). NA signals autoinitialization. Only some smooth classes use this. The "ps" class can use a 2 item array giving the basis and penalty order separately.

## Value

A list of 2 lists:

• args, contains the arguments to be passed on to the mgcv smooths

• vars, contains variables relevant to the evaluation of the smooth.

## Examples

if (FALSE) {
# Not meant to be used directly
smooth_time(borealis_data, bounds_voronoi, time = "year")
}