Make flux or concentration predictions from a load model.

A function in the loadModelInterface. Uses a load model and a predictor dataset (which may differ from the original model-fitting dataset) to make predictions for loads or concentrations at the time points in the new dataset.

Usage

predictSolute(
  load.model,
  flux.or.conc = c("flux", "conc"),
  newdata = getFittingData(load.model),
  date = TRUE,
  count = !identical(agg.by, "unit"),
  se.fit = FALSE,
  se.pred = FALSE,
  interval = c("none", "confidence", "prediction"),
  level = 0.95,
  lin.or.log = c("linear", "log"),
  agg.by = c("unit", "day", "month", "water year", "calendar year", "total", "[custom]"),
  na.rm = FALSE,
  attach.units = FALSE,
  ...
)

Arguments

load.model: A load model object, probably a loadInterp, loadReg2, loadComp, or loadLm. The object should typically inherit from the loadModel class and must always implement the loadModelInterface.
flux.or.conc: character. Should the predictions be reported as flux rates or concentrations? If the output is a data.frame, the column name for flux predictions will be "fit" when `agg.by='unit'` and "Flux_Rate" otherwise; the column name for concentration predictions will be "fit" when `agg.by='unit'` and "Conc" otherwise.
newdata: An optional data.frame of predictor observations. The column names in this data.frame must match those specified in the load model's metadata. If newdata is not supplied, the original fitting data will be used.
date: logical. If TRUE, the output data.frame will include a column containing the dates or grouping variables of the predictions. For agg.by=="unit", the resulting column will be "date"; for agg.by=="water year", the column will be "water.year", and so on.
count: logical. If TRUE, and if agg.by!='unit', the output data.frame will include a column named 'count' containing the number of unit predictions going into each aggregated prediction (row).
se.fit: logical. If TRUE, the output data.frame will include a column named "se.fit" describing the standard error of the model fit for each row of predictors.
se.pred: logical. If TRUE, the output data.frame will include a column named "se.pred" describing the standard error of the prediction for each row of predictors. The se.pred values are standard errors of prediction (SEPs) and take into account not only the parameter uncertainty associated with the model coefficients (also covered by se.fit), but also the random error associated with any given observation (the epsilon term in a typical regression model).
interval: character. One of "none", "confidence", or "prediction". If not "none", the interval bounds will be returned in columns named "lwr.fit" and "upr.fit" (for confidence intervals) or "lwr.pred" and "upr.pred" (for prediction intervals). Confidence intervals describe confidence in the model prediction for the mean value given a set of predictors, whereas prediction bounds describe the expected distribution of observations at that prediction point.
level: numeric. Fraction of density distribution to include within the confidence or prediction interval
lin.or.log: character. Either "linear" or "log" to say whether the predictions should be converted to log space or not. If converted to log space, a bias correction will be applied to regression model predictions; see linToLog.
agg.by: character. The date interval or other grouping variable to aggregate results by. To do no aggregation, use the default of `agg.by='unit'`. If agg.by is one of "day", "month", "water year", or "calendar year", the dates vector will be split into periods corresponding to those intervals, and the flux or concentration will be computed for each period. If agg.by="total", dates will be ignored and the entire vector preds will be aggregated, weighting each point prediction equally. If agg.by is set to one or more custom values not in the list of default values, the values should be names of columns in `newdata`, and predictions will be grouped according to the values in those columns.
na.rm: logical. Should NA values be removed before aggregation (TRUE), or should NA be returned for intervals that contain one or more NA predictions (FALSE)?
attach.units: logical. Should the units be attached to columns in the resulting data.frame?
...: Additional arguments passed to class-specific implementations of the predictSolute generic function.

Value

If interval=="none" and all of dates, se.fit, se.pred, and count are FALSE, returns a vector of predictions. Otherwise, returns a data.frame. If agg.by=="unit" then the data.frame will have a column called "flux" or "conc" containing the predictions for the solute, and optional columns associated with datetimes, interval, se.fit, and se.pred are additional columns with names noted in those argument descriptions. If `agg.by!="unit"`, the returned names of the grouping column or columns will reflect the selected value of `agg.by` (e.g., 'month').

Details

This is the S3 generic predictSolute(), for which specific methods should be implemented for each load model class (e.g., loadModel. Unlike rloadest::predLoad() and predConc(), and more like most other predict functions in R, this function makes no attempt to aggregate the results.

Make flux or concentration predictions from a load model.

Usage

Arguments

Value

Details

See also