add_fitted_sims.glm: Generate fitted simulations from a glm model

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

Generate fitted simulations from a glm model

Description

Generate simulations from a glm 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 'glm'
add_fitted_sims(newdata, mod, n_sims = 1000, seed = NULL, weights = 1, ...)

Arguments

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

Value

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

See Also

Other glm: add_predicted_sims.glm()

Examples

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

mod <- glm(lot1 ~ log(u) + lot2, data = clotting, family = Gamma)

sims_fit <- add_fitted_sims(clotting, mod)

head(sims_fit)

# Binomial
# example from Venables and Ripley (2002, pp. 190-2.)
ldose <- rep(0:5, 2)
numdead <- c(1, 4, 9, 13, 18, 20, 0, 2, 6, 10, 12, 16)
sex <- factor(rep(c("M", "F"), c(6, 6)))
SF <- cbind(numdead, numalive = 20-numdead)
budworm.lg <- glm(SF ~ sex*ldose, family = binomial)

dat <- data.frame(sex = factor(c("M", "F", "M", "F")),
                  ldose = c(0,0,5,5))

sims_fit_b <- add_fitted_sims(dat, budworm.lg)

head(sims_fit_b)

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