Predict from a fitted ffc_gam model

Usage

# S3 method for class 'ffc_gam'
predict(object, newdata, type = "link", se.fit = FALSE, ...)

Arguments

object: a fitted gam object as produced by gam().
newdata: A data frame or list containing the values of the model covariates at which predictions are required. If this is not provided then predictions corresponding to the original data are returned. If newdata is provided then it should contain all the variables needed for prediction: a warning is generated if not. See details for use with link{linear.functional.terms}.
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).
se.fit: when this is TRUE (not default) standard error estimates are returned for each prediction.
...: ignored

Value

If type == "lpmatrix" then a matrix is returned which will give a vector of linear predictor values (minus any offest) at the supplied covariate values, when applied to the model coefficient vector. Otherwise, if se.fit == TRUE then a 2 item list is returned with items (both arrays) fit and se.fit containing predictions and associated standard error estimates, otherwise an array of predictions is returned. The dimensions of the returned arrays depends on whether type is "terms" or not: if it is then the array is 2 dimensional with each term in the linear predictor separate, otherwise the array is 1 dimensional and contains the linear predictor/predicted values (or corresponding s.e.s). The linear predictor returned termwise will not include the offset or the intercept.

Details

This function returns predictions from models fitted with ffc_gam(). Data passed to newdata will first be correctly augmented to include any basis functions whose coefficients were estimated as time-varying, so the user need only supply data that includes variables that were used in the original data that was supplied to ffc_gam()

Author

Nicholas J Clark

Examples

# Fit a model and generate predictions
mod <- ffc_gam(
  deaths ~ offset(log(population)) + sex +
    fts(age, k = 8, bs = "cr", time_k = 10),
  time = "year",
  data = qld_mortality,
  family = poisson(),
  engine = "bam"
)
preds <- predict(mod, type = "response")

# Distributional regression prediction
library(mgcv)
set.seed(42)
n <- 40
sim_data <- data.frame(
  time = 1:n,
  x = rnorm(n),
  y = rnorm(n)
)
dist_mod <- ffc_gam(
  list(y ~ fts(x, k = 4), ~ fts(x, k = 3)),
  family = gaulss(),
  data = sim_data,
  time = "time"
)
# Returns expected values (location parameter) for distributional families
dist_preds <- predict(dist_mod, type = "response")