Generate fitted simulations from a lm model

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()

# S3 method for 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)
#> # A tibble: 6 × 5
#>       u  lot1  lot2 .sim  lot1_fit
#>   <dbl> <dbl> <dbl> <chr>    <dbl>
#> 1     5   118    69 1         117.
#> 2     5   118    69 2         117.
#> 3     5   118    69 3         118.
#> 4     5   118    69 4         117.
#> 5     5   118    69 5         116.
#> 6     5   118    69 6         117.