Optimize rectangular hyperbolic light response curve in one window

Source: R/LRC_base.R

Optimize rectangular hyperbolic light response curve in one window

LRC_fitLRC(
  dsDay,
  E0,
  sdE0,
  RRefNight,
  controlGLPart = partGLControl(),
  lastGoodParameters = rep(NA_real_, 7L)
)

Arguments

dsDay

data.frame with columns NEE, Rg, Temp_C, VPD, and no NAs in NEE

E0

temperature sensitivity of respiration

sdE0

standard deviation of E_0.n

RRefNight

basal respiration estimated from night time data

controlGLPart

further default parameters (see partGLControl())

lastGoodParameters

numeric vector returned by last reasonable fit

Value

A list with the following components:

  • thetaOpt : vector of optimized parameters, including the fixed ones and E0

  • iOpt : vector of positions of parameters that are optimized, including E0, which has been optimized prior to this function

  • thetaInitialGuess : initial guess from data

  • covParms : matrix of the covariance matrix of parameters, including E0

  • convergence : code specifying convergence problems

  • 0 : good convergence

  • 1-1000 : see optim()

  • 1001 : too few bootstraps converged

  • 1002 : fitted parameters were outside reasonable bounds

  • 1003 : too few valid records in window

  • 1004 : near zero covariance in bootstrap indicating bad fit

  • 1005 : covariance from curvature of fit yielded negative variances indicating bad fit

  • 1006 : prediction of highest PAR in window was far from saturation indicating insufficient data to constrain LRC

  • 1010 : no temperature-respiration relationship found

  • 1011 : too few valid records in window (from different location: partGLFitLRCOneWindow)

Details

Optimization is performed for three initial parameter sets that differ by beta0 (* 1.3, * 0.8). From those three, the optimization result is selected that yielded the lowest misfit.

Starting values are:

  • k = 0,

  • beta = interpercentileRange(0.03, 0.97) of respiration,

  • alpha = 0.1,

  • R_ref from nightTime estimate.

  • E0 is fixed to the night-time estimate, but varies for estimating parameter uncertainty.