Fitting height-diameter models

This function fits and compares (optional) height-diameter models.

Usage

modelHD(
  D,
  H,
  method = NULL,
  useWeight = FALSE,
  drawGraph = FALSE,
  plot = NULL,
  bayesian = FALSE,
  useCache = FALSE,
  chains = 3,
  thin = 5,
  iter = 5000,
  warmup = 500,
  ...
)

Arguments

D

Vector with diameter measurements (in cm). NA values are accepted but a minimum of 10 valid entries (i.e. having a corresponding height in H) is required.

H

Vector with total height measurements (in m). NA values are accepted but a minimum of 10 valid entries (i.e. having a corresponding diameter in D) is required.

method

Method used to fit the relationship. To be chosen between:

log1, log2
- log 1: \((log(H) = a+ b*log(D))\) (equivalent to a power model)
- log 2: \((log(H) = a+ b*log(D) + c*log(D)^2)\)
weibull: \(H = a*(1-exp(-(D/b)^c))\)
michaelis: \(H = (A * D)/(B + D)\)

If NULL, all the methods will be compared.

useWeight

If weight is TRUE, model weights will be \((D^2)*H\) (i.e. weights are proportional to tree volume, so that larger trees have a stronger influence during the construction of the model).

drawGraph

If TRUE, a graphic will illustrate the relationship between H and D. Only if argument plot is null.

plot

(optional) a vector of character containing the plot ID's of the trees (linked to D and H). Must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models.

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

If plot is NULL or has a single value, a single list is returned. If there is more than one plot, multiple embedded lists are returned with plots as the list names.

If model is not null (model comparison), returns a list :

input: list of the data used to construct the model (list(H, D))
model: outputs of the model (same outputs as given by stats::lm(), stats::nls())
residuals: Residuals of the model
method: Name of the method used to construct the model
predicted: Predicted height values
RSE: Residual Standard Error of the model
RSElog: Residual Standard Error of the log model (NULL if other model)
fitPlot: a ggplot object containing the model fitting plot
weighted: a logical indicating whether weights were used during the fit

If the parameter model is null, the function return a plot with all the methods for comparison, the function also returns a data.frame with:

method: The method that had been used to construct the plot
RSE: Residual Standard Error of the model
RSElog: Residual Standard Error of the log model (NULL if other model)
Average_bias: The average bias for the model

Details

All the back transformations for log-log models are done using the Baskerville correction (\(0.5 * RSE^2\), where RSE is the Residual Standard Error).

If useCache = TRUE and this is the first time the model is being built, the model will be saved as a .rds file in the defined cache path (see createCache()). If useCache = TRUE and the model has already been built using the user cache, the model will be loaded and updated to avoid wasting time re-compiling it. If useCache = NULL, the cache is first cleared before building the model.

Author

Maxime REJOU-MECHAIN, Arthur PERE, Ariane TANGUY, Arthur Bailly

Examples


# Load a data set
data(NouraguesHD)

# Fit H-D models for the Nouragues dataset
 HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, drawGraph = TRUE) 

#> To build a HD model you must use the parameter 'method' in this function

# For a selected model
HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H,
                   method = "log2", drawGraph = TRUE)


# Using weights
HDmodel <- modelHD(
  D = NouraguesHD$D, H = NouraguesHD$H,
  method = "log2", useWeight = TRUE,
  drawGraph = TRUE)


# With multiple stands (plots)
HDmodel <- modelHD(
  D = NouraguesHD$D, H = NouraguesHD$H,
  method = "log2", useWeight = TRUE, 
  plot = NouraguesHD$plotId, drawGraph = TRUE)



### Using log2 bayesian model
if (FALSE) HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, 
  method = "log2", bayesian = TRUE, useCache = TRUE)
plot(HDmodel$model)  # \dontrun{}
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf
#> Warning: no non-missing arguments to min; returning Inf
#> Warning: no non-missing arguments to max; returning -Inf

#> Error in plot.window(...): need finite 'xlim' values
  
### Using weibull bayesian model (time consuming)
# As the algorithm is likely to find numerous local minima,
# defining priors is strongly recommended (see "Some tricks" part in the vignette)
# Also, since  model parameters and chain iterations are strongly correlated,
# an increase of 'thin', 'iter' and 'warmup' may be required.
if (FALSE) HDmodel <- modelHD(D = NouraguesHD$D, H = NouraguesHD$H, 
 method = "weibull", bayesian = TRUE, useCache = TRUE,
 thin = 20, iter = 12000, warmup = 2000,
 prior = c(brms::set_prior(prior = "uniform(0,80)",
                           lb = 0, ub = 80, class = "b", nlpar = "a"),
           brms::set_prior(prior = "uniform(0,100)",
                           lb = 0, ub = 100, class = "b", nlpar = "b"),
           brms::set_prior(prior = "uniform(0.1,0.9)",
                           lb = 0.1, ub = 0.9, class = "b", nlpar = "c")))  # \dontrun{}