Create a fitted loadComp object.

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.

Usage

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"),
  n.iter = 100,
  MSE.method = "parametric"
)

Arguments

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.

Value

A fitted loadComp model.

See also

Other load.model.inits: loadInterp(), loadLm(), loadModel(), loadReg2()