Predict using a fitted SPM model on the whole data or on new data

```
# S4 method for sspm_fit
predict(
object,
new_data = NULL,
biomass = NULL,
aggregate = FALSE,
interval = FALSE,
next_ts = FALSE,
type = "response"
)
# S4 method for sspm_dataset
predict(
object,
new_data = NULL,
discrete = TRUE,
type = "response",
interval = FALSE
)
```

- object
**[sspm_fit]**Fit object to predict from.- new_data
**[data.frame]**New data to predict with.- biomass
**[character]**Biomass variable.- aggregate
**[logical]**For biomass predictions only, whether to aggregate the data to the boundary level. Default to FALSE.- interval
**[logical]**Whether or not to calculate confidence, and when possible, prediction intervals.- next_ts
**[logical]**For biomass, predict next timestep.- type
When this has the value

`"link"`

(default) the linear predictor (possibly with associated standard errors) is returned. When`type="terms"`

each component of the linear predictor is returned seperately (possibly with standard errors): this includes parametric model components, followed by each smooth component, but excludes any offset and any intercept.`type="iterms"`

is the same, except that any standard errors returned for smooth components will include the uncertainty about the intercept/overall mean. When`type="response"`

predictions on the scale of the response are returned (possibly with approximate standard errors). When`type="lpmatrix"`

then a matrix is returned which yields the values of the linear predictor (minus any offset) when postmultiplied by the parameter vector (in this case`se.fit`

is ignored). The latter option is most useful for getting variance estimates for quantities derived from the model: for example integrated quantities, or derivatives of smooths. A linear predictor matrix can also be used to implement approximate prediction outside`R`

(see example code, below).- discrete
**[logical]**If`new_data`

is NULL, whether to predict based on a discrete prediction matrix (default to TRUE).

A `dataframe`

of predictions.

```
if (FALSE) {
# Predictions for a model fit (usually, productivity)
predict(sspm_model_fit)
# To get biomass predictions, provide the variable name
predict(sspm_model_fit, biomass = "weight_per_km2_borealis")
# To get the next timestep predictions
predict(sspm_model_fit, biomass = "weight_per_km2_borealis", next_ts = TRUE)
}
```