From an MGCV model, create a dataframe (tibble) of values to predict across, including
all combinations of categorical predictors in the model, and incrementing across
the plot_by variable, and using median values of other numeric predictors.
Used by predict_gam() – see there for more details
Usage
create_prediction_df(
model,
plot_by = "doy",
across_increment = 1,
quant_trimming = 0.01,
verbose = TRUE
)Arguments
- model
A GAM model object from mgcv, or a named list of fitted mgcv models
- plot_by
A single string specifying the variable to plot across. Defaults to "doy".
- across_increment
A numeric value specifying the increment for the plot_by variable. Defaults to 1.
- quant_trimming
A numeric value between 0 and 1 defining quantile-based trimming – this provides a buffer between the outmost observations and the outmost plot predictions. Defaults to 0.01; increase if plot predictions at the edges are misleading.
- verbose
Provide context? Logical, defaults to TRUE