Create a greta_model object representing a statistical model (using model), and plot a graphical representation of the model. Statistical inference can be performed on greta_model objects with mcmc

model(..., precision = c("double", "single"), compile = TRUE)

# S3 method for greta_model
print(x, ...)

# S3 method for greta_model
plot(x, y, colour = "#996bc7", ...)

Arguments

for model: greta_array objects to be tracked by the model (i.e. those for which samples will be retained during mcmc). If not provided, all of the non-data greta_array objects defined in the calling environment will be tracked. For print and plot:further arguments passed to or from other methods (currently ignored).

precision

the floating point precision to use when evaluating this model. Switching from "double" (the default) to "single" may decrease the computation time but increase the risk of numerical instability during sampling.

compile

whether to apply XLA JIT compilation to the TensorFlow graph representing the model. This may slow down model definition, and speed up model evaluation.

x

a greta_model object

y

unused default argument

colour

base colour used for plotting. Defaults to greta colours in violet.

Value

model - a greta_model object.

plot - a DiagrammeR::grViz object, with the DiagrammeR::dgr_graph object used to create it as an attribute "dgr_graph".

Details

model() takes greta arrays as arguments, and defines a statistical model by finding all of the other greta arrays on which they depend, or which depend on them. Further arguments to model can be used to configure the TensorFlow graph representing the model, to tweak performance.

The plot method produces a visual representation of the defined model. It uses the DiagrammeR package, which must be installed first. Here's a key to the plots:

Examples

# NOT RUN {
# define a simple model
mu <- variable()
sigma <- normal(0, 3, truncation = c(0, Inf))
x <- rnorm(10)
distribution(x) <- normal(mu, sigma)

m <- model(mu, sigma)

plot(m)
# }