ggplotMLL_maxB: Plot estimated maximum biomass by age
In PredictiveEcology/LandR: Landscape Ecosystem Modelling in R

ggplotMLL_maxB

R Documentation

Plot estimated maximum biomass by age

Description

Plots a maximum biomass estimated at maximum 'cover' (or dominance) levels as a function of age.

Usage

ggplotMLL_maxB(
  mll,
  data,
  maxCover = 1L,
  xCovar = "age",
  plotTitle = NULL,
  nonLinModelQuoted,
  linModelQuoted,
  averageCovariates = TRUE,
  observedAge = FALSE,
  plotCIs = TRUE
)

Arguments

`mll`	a named list with outputs of an `bbmle::mle2` call (the fitted non-linear model), from which coefficient values will be extracted. If several model outputs are provided all fitted models will be plotted, with plot labels corresponding to list names.
`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`). If `mll` is a list data is assumed to be the same for the two models.
`maxCover`	numeric. Value indicating maximum cover/dominance.
`xCovar`	the variable shown in the x axis. Defaults to `age`.
`plotTitle`	character. Passed to `title` in `ggplot2::labs()`.
`nonLinModelQuoted`	a named list of non-linear equations as a `call` (quoted expression) passed to `mle2(minuslog1)`. See `bbmle::mle2()`. Accepts equations with three parameters 'A', 'p' and 'k'. List names and length must the same as in `mll`.
`linModelQuoted`	A named list of lists 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`. List names and length must the same as in `mll`.
`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?