add_fitted_sims.lm: Generate fitted simulations from a lm model

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

Generate fitted simulations from a lm model

Description

Generate simulations from a lm model incorporating either error in fitted 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 'lm'
add_fitted_sims(newdata, mod, n_sims = 1000, seed = NULL, ...)

Arguments

newdata	a data.frame of new data to predict
mod	A lm model to simulate from.
n_sims	number of simulation samples to construct
seed	numeric, optional argument to set seed for simulations
...	Unused dots for compatibility with generic functions.

Value

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

See Also

Other lm: add_predicted_sims.lm()

Examples

clotting <- data.frame(
   u = c(5,10,15,20,30,40,60,NA,100),
   lot1 = c(118,58,42,35,27,25,21,19,18),
   lot2 = c(69,35,26,21,18,16,13,12,12))

mod <- lm(lot1 ~ log(u) + lot2, data = clotting)

sims_fit <- add_fitted_sims(clotting, mod)

head(sims_fit)

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