simulate.greta_model: Simulate Responses From 'greta_model' Object

In greta: Simple and Scalable Statistical Modelling in R

Simulate Responses From greta_model Object

Description

Simulate values of all named greta arrays associated with a greta model from the model priors, including the response variable.

Usage

## S3 method for class 'greta_model'
simulate(object, nsim = 1, seed = NULL, precision = c("double", "single"), ...)

Arguments

object	a greta_model() object
nsim	positive integer scalar - the number of responses to simulate
seed	an optional seed to be used in set.seed immediately before the simulation so as to generate a reproducible sample
precision	the floating point precision to use when calculating values.
...	optional additional arguments, none are used at present

Details

This is essentially a wrapper around calculate() that finds all relevant greta arrays. See that function for more functionality, including simulation conditional on fixed values or posterior samples.

To simulate values of the response variable, it must be both a named object (in the calling environment) and be a greta array. If you don't see it showing up in the output, you may need to use as_data to convert it to a greta array before defining the model.

Value

A named list of vectors, matrices or arrays containing independent samples of the greta arrays associated with the model. The number of samples will be prepended as the first dimension of the greta array, so that a vector of samples is returned for each scalar greta array, and a matrix is returned for each vector greta array, etc.

Examples

## Not run: 
# build a greta model
n <- 10
y <- rnorm(n)
y <- as_data(y)

library(greta)
sd <- lognormal(1, 2)
mu <- normal(0, 1, dim = n)
distribution(y) <- normal(mu, sd)
m <- model(mu, sd)

# simulate one random draw of y, mu and sd from the model prior:
sims <- simulate(m)

# 100 simulations of y, mu and sd
sims <- simulate(m, nsim = 100)

## End(Not run)
# nolint start

greta documentation built on Sept. 8, 2022, 5:10 p.m.