Symbolize an MCMCglmm fit (Gaussian, v0.9 first slice) — symbolize.MCMCglmm • symbolizer

Builds a symbolized_model from an MCMCglmm fit. v0.9 covers the Gaussian conditional submodel (identity link) with optional ~ g random intercepts. Other families and MCMCglmm's flexible covariance structures return capability errors via capability_check().

Usage

# S3 method for class 'MCMCglmm'
symbolize(fit, symbols = NULL, units = NULL, context = NULL, data = NULL, ...)

Arguments

fit: A fitted statistical model object.
symbols: Optional named character vector mapping variable names to user-supplied LaTeX symbols, e.g. c(body_mass = "W_i", temperature = "T_i").
units: Optional named character vector mapping variable names to units, e.g. c(body_mass = "g", temperature = "C").
context: Optional short character description of the model, e.g. "avian body-size location-scale model".
data: The data frame used to fit. Required: MCMCglmm does not keep the data on the fitted object.
...: Reserved for method-specific extra arguments.

Value

A symbolized_model object.

Details

MCMCglmm does NOT keep the data frame on the fitted object, so data is a mandatory argument here.

Credible intervals

MCMCglmm is Bayesian. The CI band in fixed_effects and interpretation is a 95% credible interval (l-95% CI / u-95% CI from summary(fit)$solutions). ci_method is set to "credible".

References

Hadfield, J. D. (2010). MCMC Methods for Multi-Response Generalized Linear Mixed Models: The MCMCglmm R Package. Journal of Statistical Software, 33(2), 1-22.