ss_predict | R Documentation |
Make predictions across rows in a dataset that may contain multiple species. The model associated with each species is used to predict values for the response variable, as well as it's prediction interval. Necessary bias-corrections are made for species with models that have a transformed response variable.
ss_predict( data, models, ref_table, level = 0.95, species = "species", predictor = "diameter", cf = "correctn_factor", geom_mean = "response_geom_mean" )
data |
Dataframe with columns containing the species and variables of
interest. Species names in the |
models |
A named list of each species' linear regression models.
|
ref_table |
Dataframe containing information to correct bias introduced
in models with a transformed response variable. It should include columns
for |
level |
Level of confidence for the prediction interval. Defaults to
|
species |
Column name of the species variable in |
predictor |
Column name of the predictor variable in |
cf |
Column name of the bias correction factor in |
geom_mean |
Column name of the geometric mean of response variable in
|
Dataframe of input data
with columns appended:
Predicted value for the response variable.
Lower
bound of the prediction interval, based on the input argument level
.
Upper bound of the prediction interval, based on the input
argument level
.
ss_simulate()
to run ss_predict()
on simulated data.
Other single-species model functions:
ss_modelfit_multi()
,
ss_modelfit()
,
ss_modelselect_multi()
,
ss_modelselect()
,
ss_simulate()
# first select best-fit model data(urbantrees) Alb_sam <- urbantrees[urbantrees$species == 'Albizia saman', ] # we use one species as an example results <- ss_modelselect_multi(Alb_sam, response = 'height', predictor = 'diameter') # generate data for subsequent predictions newdata <- generate_x(Alb_sam, response = "height", predictor = "diameter") # run function predictions <- ss_predict(newdata, models = results$ss_models, ref_table = results$ss_models_info, predictor = "predictor") head(predictions)
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