estimateMSE.loadComp: Estimate uncertainty in a composite model.
In McDowellLab/loadflex: Models and Tools for Watershed Flux Estimates

Description Usage Arguments Details See Also

Calculates and returns the mean squared errors (MSEs) in the units and transformation space (e.g., log space) of the left-hand side of the model fit equation.

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## S3 method for class 'loadComp'
estimateMSE(load.model, n.iter = 100,
  method = "parametric", rho, ...)

`load.model`	A load model object, typically inheriting from loadModel and always implementing the loadModelInterface.
`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.
`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.
`rho`	The first-order autocorrelation coefficient to assume in simulateSolute(regression.model, interpolation.data). If missing, rho will be estimated from the interpolation data, but be warned that many data points are needed to make this estimation with precision.
`...`	Other arguments passed to inheriting methods for estimateMSE

This method is leave-one-out cross validation (LOOCV) for the interpolation and involves repeated resampling of the coefficients from which the regression predictions and residuals are calculated.

Other estimateMSE: estimateMSE.loadInterp, estimateMSE

McDowellLab/loadflex documentation built on May 8, 2019, 9:48 a.m.