Tree height predictions

The function predicts height from diameter based on a fitted model. As the predict() function for brms models takes ~10 minutes to run, predictions are calculated using the coefficients from the models directly.

Usage

predictHeight(D, model, err = FALSE, plot = NULL)

Arguments

D

a n x m matrix containing tree diameters (in cm), where n is the number of trees and m is the number of Monte Carlo simulations (m = 1 if no error propagation).

model

The output of the modelHD() function.

err

If TRUE, An error is taken randomly from a normal distribution with a mean of zero and a standard deviation equaled to the residual standard error of the model (RSE). Only used for the Monte Carlo approach (see AGBmonteCarlo()), otherwise it should be let as FALSE, the default case.

plot

(optional) Plot ID, must be either one value, or a vector of the same length as D. This argument is used to build stand-specific HD models.

Value

Returns a vector of total tree height (in m).

Details

In the case where the error is FALSE and the model is a log-log model, we use the Baskerville correction, a bias correction factor used to get unbiased backtransformation values.

See also

minpack.lm::nlsLM()

Author

Arthur BAILLY