This entry contains the R data and codes used in the article "Comparing local calibration using random effects estimation and Bayesian calibrations. A case study with a mixed effect stem profile model". Stem profile models are complex models predicting the diameter of a tree at any position along the stem. These models are fitted on detailed datasets, which spatial representativity is poor. Local calibration brings strong improvements to their prediction capabilities. This study offers a good support for local calibration of allometric models for trees. The R code file 'R code for random effects estimation.R' contains the R code for estimating the random effect parameters for new data, in the example of the taper model used in the study (which has 3 random terms. The R data files contain the plot-level and tree-level measurements for each experimental site (Application Set site 1 and 2). The R code file 'Stan code ApSite 1.R' contains the STAN code for the Bayesian MCMC calibration of the taper model on trees sampled in the application site 1.

{"references": ["Bouriaud O, Stefan G, Saint-Andr\u00e9 L (2019) Comparing local calibration using random effects estimation and Bayesian calibrations. A case study with a mixed effect stem profile model. Annals of Forest Science."]}

Research co-funded by the Romanian National Authority for Scientific Research, CNCS – UEFISCDI, project PN-II-ID-PCE-2011-3-0781, and the EU H2020 DIABOLO (grant agreement No 633464).