add_predicted_sims.psem: Generate predicted simulations from a psem model

In jebyrnes/sinterval: Generate Simulated Values from Models with Different Intervals of Uncertainty

Generate predicted simulations from a psem model

Description

Generate simulations from a psem model incorporating either error in predicted error. Simulations explore the possible space of what a model might predict rather than an interval for use in comparison to Bayesian posteriors for non-Bayesian models. The output format and functions draw inspiration from the tidybayes::tidybayes() library and merTools::predictInterval()

Usage

## S3 method for class 'psem'
add_predicted_sims(
  newdata,
  mod,
  n_sims = 1000,
  seed = NULL,
  type = c("predict", "fit"),
  ...
)

Arguments

newdata	a data.frame of new data to predict
mod	An piecewiseSEM::psem() object to simulate from.
n_sims	number of simulation samples to construct
seed	numeric, optional argument to set seed for simulations
type	Character defining if we are looking at fit or predict intervals.
...	Unused dots for compatibility with generic functions.

Value

A tibble::tibble() with information about simulate values.

See Also

Other psem: add_fitted_sims.psem()

Examples

## Not run: 

library(piecewiseSEM)
data(keeley)
mod <- psem(
 lm(abiotic ~ distance, data=keeley),
 lm(rich ~ abiotic + hetero, data=keeley),
 lm(hetero ~ distance, data=keeley),
 data = keeley)

newdat <- data.frame(distance=c(30, 50))

new_predicted_sims <- newdat %>%
 add_predicted_sims(mod)

head(new_predicted_sims)

## End(Not run)

jebyrnes/sinterval documentation built on March 18, 2023, 5:05 a.m.