loadComp: Create a fitted loadComp object.
In McDowellLab/loadflex: Models and Tools for Watershed Flux Estimates

Description Usage Arguments Value See Also

Generates a new model of class loadComp (loadComp-class). loadComps themselves contain two inner load models, one for regression and one for interpolation of the residuals of the regression predictions.

loadComp(reg.model, interp.format = c("flux", "conc"),
  abs.or.rel.resids = c("absolute", "relative"), use.log = TRUE,
  interp.data, interp.function = linearInterpolation, store = c("data",
  "fitting.function", "uncertainty"), n.iter = 100,
  MSE.method = "parametric")

`reg.model`	The model, usually a regression model, to whose predictions the residuals corrections should be added.
`interp.format`	character specifying the load format in which residuals should be interpolated
`abs.or.rel.resids`	Should residuals be computed as the difference ("absolute") or the ratio ("relative") of the observed and predicted values?
`use.log`	logical. Should residuals be computed in log space (TRUE) or linear space (FALSE)?
`interp.data`	the dataset, possibly differing from getFittingData(reg.model), from which regression residuals will be calculated and interpolated.
`interp.function`	a function accepting args dates.in, y.in, and dates.out and returning y.out. See interpolations for pre-defined options, or write your own having the same form.
`store`	One or more character strings specifying which information to write within the model. Options are 'data': the original fitting data; 'fitting.function': a fitting function that can produce a new loadComp object from new data (this currently uses the same new data for both regression calibration and interpolation); 'uncertainty': an estimate of uncertainty, which can take some time to compute but will permit creation of uncertainty intervals, etc. in the prediction and aggregation phases.
`n.iter`	The number of times to repeat the COMPLETE process of [simulate predictions from the regression model and do leave-one-out cross validation (for all interpolation data points)]. Each run through the process generates one estimate of the MSE, from which a mean and SD of the MSE estimates will be returned.
`MSE.method`	character. The method by which the model should be bootstrapped. "non-parametric": resample with replacement from the original fitting data, refit the model, and make new predictions. "parametric": resample the model coefficients based on the covariance matrix originally estimated for those coefficients, then make new predictions.