predictSolute.loadInterp: Make flux or concentration predictions from a loadInterp...

In USGS-R/loadflex: Models and Tools for Watershed Flux Estimates

Make flux or concentration predictions from a loadInterp model.

Description

Makes instantaneous predictions (at the temporal resolution of newdata) from a fitted loadInterp model. See predictSolute for details. For loadInterp models in particular,

Usage

## S3 method for class 'loadInterp'
predictSolute(load.model, flux.or.conc = c("flux",
  "conc"), newdata, date = TRUE, count = TRUE, se.fit = FALSE,
  se.pred = FALSE, interval = c("none", "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 loadInterp object.
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	data.frame, optional. Predictor data. Because loadInterp models interpolate in time among the observations to which they have been "fitted", newdata is usually simply a one-column data.frame of dates or date-times. Column names should match those given in the loadInterp 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. Must be FALSE because se.fit is unavailable for loadInterp models.
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 specifying the type of interval to include, if any. Prediction intervals are only available when 'agg.by=="unit"', and confidence intervals are not available for any loadInterp predictions. If "prediction", the interval bounds will be returned in columns named "lwr.pred" and "upr.pred". Prediction bounds describe the expected distribution of observations at each prediction point. The extent of the
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 most 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.

Details

loadInterps are currently assumed to have normally distributed residuals. An unwitting user might violate this assumption without being notified by the code, so be careful! This assumption is mainly relevant to the calculation of prediction intervals. Also, where other models such as loadReg and loadLm will retransform predictions back into linear space, loadInterps will not.

Value

Returns a vector or data.frame of predictions preditions. The result is a vector if interval is "none" and all of se.fit, se.pred, date, and count are FALSE; otherwise, the result is a data.frame.

See Also

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

USGS-R/loadflex documentation built on July 26, 2023, 9:54 p.m.