Prepare data for model plotting
.MLLMaxBPartialPlotData(
mll,
nonLinModelQuoted,
linModelQuoted,
targetCovar = "cover",
fixMaxCover = TRUE,
maxCover = 1,
data,
fun = "mean",
plotCIs = TRUE
)outputs of an bbmle::mle2 call (the fitted non-linear model),
from which coefficient values will be extracted.
The non-linear equation as a call
(quoted expression) passed to mle2(minuslog1). See ?mle.
Accepts equations with three parameters 'A', 'p' and 'k'.
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.
the covariate for which variation in maxB values will be shown.
Defaults to showing how maxB values change with "cover". All other covariates except
"age" are averaged. Age values are generated as round(seq(min(age), max(age)*1.5, length.out = 100), 0).
When targetCovar != "cover", "cover" will be fixed at maxCover. See fixMaxCover.
logical. If TRUE and targetCovar != "cover", cover is
not averaged and is fixed to maxCover.
numeric. Value indicating maximum cover/dominance.
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).
The function to apply when summarizing other variables. By default,
the all other variables except age are averaged ("mean"). Other options are:
"median", "min", "max".
should confidence intervals be calculated and plotted?