HDmethods

Methods used for modeling height-diameter relationship

Usage

loglogFunction(
  data,
  weight = NULL,
  method,
  bayesian,
  useCache,
  chains,
  thin,
  iter,
  warmup,
  ...
)

michaelisFunction(
  data,
  weight = NULL,
  bayesian,
  useCache,
  chains,
  thin,
  iter,
  warmup,
  ...
)

weibullFunction(
  data,
  weight = NULL,
  bayesian,
  useCache,
  chains,
  thin,
  iter,
  warmup,
  ...
)

Arguments

data: Dataset with the informations of height (H) and diameter (D)
weight: (optional) Vector indicating observation weights in the model.
method: In the case of the loglogFunction, the model is to be chosen between log1, log2 or log3.
bayesian: a logical. If FALSE (by default) the model is estimated using a frequentist framework (lm or nls). If TRUE, the model is estimated in a Bayesian framework using the brms package.
useCache: a logical. If bayesian = TRUE, determine wether to use the cache when building a Bayesian model (see Details).
chains: (only relevant if bayesian = TRUE): Number of Markov chains (defaults to 3), see brms::brm()
thin: (only relevant if bayesian = TRUE): Thinning rate, see brms::brm()
iter: (only relevant if bayesian = TRUE): number of total iterations per chain (including warmup; defaults to 5000), see brms::brm()
warmup: (only relevant if bayesian = TRUE): number of warmup (aka burnin) iterations (defaults to 1000), see brms::brm()
...: Further arguments passed to brm(), e.g: prior, cores, etc. See brms::brm()

Value

All the functions give an output similar to the one given by stats::lm(), obtained for michaelisFunction and weibullFunction from minpack.lm::nlsLM).

Result of a model (lm object if bayesian = FALSE, brm object if bayesian = TRUE)

Result of a model (nlsM object if bayesian = FALSE, brm object if bayesian = TRUE)

Details

These functions model the relationship between tree height (H) and diameter (D). loglogFunction Compute two types of log model (log and log2) to predict H from D. The model can be:

log 1: $log(H) = a+ b*log(D)$ (equivalent to a power model)
log 2: $log(H) = a+ b*log(D) + c*log(D)^2$

michaelisFunction Construct a Michaelis Menten model of the form: $$H = (A * D) / (B + D)$$ (A and B are the model parameters to be estimated)

weibullFunction Construct a three parameter Weibull model of the form: $$H = a*(1-exp(-(D/b)^c))$$ (a, b, c are the model parameters to be estimated)

References

Michaelis, L., & Menten, M. L. (1913). Die kinetik der invertinwirkung. Biochem. z, 49(333-369), 352. Weibull, W. (1951). Wide applicability. Journal of applied mechanics, 103. Baskerville, G. L. (1972). Use of logarithmic regression in the estimation of plant biomass. Canadian Journal of Forest Research, 2(1), 49-53.

Author