Prepare data for model plotting

.MLLMaxBplotData(
  mll,
  nonLinModelQuoted,
  linModelQuoted,
  maxCover,
  data,
  averageCovariates = TRUE,
  observedAge = FALSE,
  plotCIs = TRUE
)

Arguments

mll: outputs of an bbmle::mle2 call (the fitted non-linear model), from which coefficient values will be extracted.
nonLinModelQuoted: The non-linear equation as a call (quoted expression) passed to mle2(minuslog1). See ?mle. Accepts equations with three parameters 'A', 'p' and 'k'.
linModelQuoted: A list of linear equations/modes relating each parameter ('A', 'p' and 'k') with a set of covariates. A call (quoted expression) passed to mle2(..., parameters). Note that for the purpose of tree growth, the linear equation determining 'A' should include a 'cover' predictor indicating the tree cover or dominance in the stand. Should be scaled between 0 and maxCover.
maxCover: numeric. Value indicating maximum cover/dominance.
data: data for estimation of maximum biomass. Should contain at least an 'age' column. Note that other covariates will be averaged and 'cover' values will be replaced with the maximum cover value (maxCover).
averageCovariates: should covariates other than age/cover be averaged for biomass predictions? If not, for each age (at maximum cover) there will be as many predictions as other covariate values. If observedAge == TRUE and averageCovariates == FALSE then the original data is used, with cover changed to maxCover.
observedAge: should observed age values be used, or should these be generated as round(seq(min(age), max(age)*1.5, length.out = 100), 0)? If observedAge == TRUE and averageCovariates == FALSE then the original data is used, with cover changed to maxCover.
plotCIs: should confidence intervals be calculated and plotted?

Prepare data for model plotting

Arguments

See also