predictSolute: Make flux or concentration predictions from a load model.
In McDowellLab/loadflex: Models and Tools for Watershed Flux Estimates

Description Usage Arguments Details Value See Also

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.

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

`load.model`	A load model object, typically inheriting from loadModel and always implementing the loadModelInterface.
`flux.or.conc`	character. Should the predictions be reported as flux rates or concentrations?
`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.
`interval`	character. One of "none", "confidence" or "prediction". If "confidence" or "prediction", the interval bounds will be returned in columns named "lwr" and "upr". 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 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, see `linToLog`.
`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 values in the se.pred column will be larger than those in the se.fit column, because 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).
`date`	logical. If TRUE, the output data.frame will include a column named "date" containing the dates of the predictions.
`attach.units`	logical. Should the units be attached to columns in the resulting data.frame?
`agg.by`	character Time period to aggregate results by.
`...`	Additional arguments passed to class-specific implementations of the `predictSolute` generic function.

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.

If interval=="none" and both se.fit and se.pred are FALSE, a vector of predictions. Otherwise, a data.frame with a column called "fit" containing the predictions for the solute. Values associated with interval, se.fit, and se.pred are additional columns with names noted in those argument descriptions.

Other loadModelInterface: estimateMSE, getFittedModel, getFittingData, getFittingFunction, getMetadata, simulateSolute, summarizeModel, validLoadModelInterface

Other predictSolute: predictSolute.loadComp, predictSolute.loadInterp, predictSolute.loadLm, predictSolute.loadModel, predictSolute.loadReg2