Fit data to linear mixed-effects model with 'species' specified as the random effect,
using the lme4::lmer
function under the hood. The full list of allometric equations that are
considered can be found in ?eqns_info
and data(eqns_info)
.
mix_modelselect(
data,
species = "species",
response = "height",
predictor = "diameter"
)
Dataframe that contains the variables of interest. Each row is a measurement for an individual tree.
Column name of the species variable in data
. Defaults to species
.
Column name of the response variable.
Defaults to height
.
Column name of the predictor variable.
Defaults to diameter
.
A list of 5 elements:
A model selection table of all the types of mixed-effects models considered,
ranked in order of ascending Aikake's Information Criterion corrected for small sample sizes (AICc).
Model details can be found in ?eqns_info
and data(eqns_info)
.
The best-fit model object.
The conditional and marginal pseudo-\(R^2\) of the best-fit model.
Correction factor used to adjust predicted values if response variable is transformed (incorporated into reported parameters).
Warning messages, if any, spit from the models. These usually indicate failure of model convergence.
Other mixed-effects model functions:
mix_predict()
,
mix_simulate()
data(urbantrees)
if (FALSE) {
mix_modelselect(data = urbantrees,
species = "species",
response = "height", predictor = "diameter")
}