loadComp | R Documentation |
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"), 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. |
A fitted loadComp model.
Other load.model.inits: loadInterp
,
loadLm
, loadModel
,
loadReg2
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