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

Description Usage Arguments Value See Also

Makes instantaneous predictions (at the temporal resolution of newdata) from a fitted loadComp model. See predictSolute for details.

## S3 method for class 'loadComp'
predictSolute(load.model, flux.or.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]"), fit.reg = FALSE, fit.resid = FALSE,
  fit.resid.raw = FALSE, ...)

`load.model`	A loadComp object.
`flux.or.conc`	character. Should the predictions be reported as flux rates or concentrations?
`newdata`	`data.frame`, optional. Predictor data, including any predictor columns needed for the regression model and any needed for the interpolation model. Column names should match those given in the `loadComp` metadata. If `newdata` is not supplied, the original fitting data will be used.
`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.
`fit.reg`	logical. If TRUE, a column for the regression predictions before interpolation will be included in the data.frame that is returned. These will be in the same format (non-log, conc/flux) as the final predictions.
`fit.resid`	logical. If TRUE, a column for the residual corrections will be included in the data.frame that is returned. These will be in the same format (non-log, conc/flux) as the final predictions, even when the residuals were actually produced in log space and/or as relative residuals, so that fit = fit.reg + fit.resid.
`fit.resid.raw`	logical. If TRUE, a column for the residual corrections as returned from the interpolation model. These may be in log space and/or unitless, depending on the type of residual correction specified when the loadComp model was created.
`...`	Additional arguments passed to class-specific implementations of the `predictSolute` generic function.