post_hoc_lm: Fit a post-hoc linear model on estimated parameters

In tulpa: Templated Unified Library for Posterior Approximation in Bayesian Hierarchical Models

Fit a post-hoc linear model on estimated parameters

Description

Useful for exploring drivers of occupancy/detection/abundance variation after model fitting. Fits a weighted linear model and optionally generates bootstrap confidence intervals.

Usage

post_hoc_lm(
  formula,
  data,
  weights = NULL,
  n_boot = 1000L,
  probs = c(0.025, 0.975)
)

## S3 method for class 'post_hoc_lm'
print(x, ...)

Arguments

formula	Model formula (e.g., psi_hat ~ trait1 + trait2).
data	A data.frame with response and predictors.
weights	Optional weights (e.g., inverse of standard errors).
n_boot	Number of bootstrap replicates for CI (default 1000, 0 to skip).
probs	Quantile probabilities for bootstrap CI (default 0.025, 0.975).
x	A post_hoc_lm result.
...	Ignored.

Value

A list of class "post_hoc_lm" with:

summary

data.frame of coefficient estimates and CIs

lm_fit

the underlying lm object

boot_coefs

matrix of bootstrap coefficient samples (if n_boot > 0)

R2

R-squared from the fitted model

Examples

# Explore drivers of per-site estimates after fitting a model.
site <- data.frame(
  psi_hat = c(0.2, 0.5, 0.8, 0.4, 0.6, 0.3),
  se      = c(0.05, 0.04, 0.06, 0.05, 0.03, 0.05),
  trait   = c(1.0, 2.5, 3.8, 1.9, 3.1, 1.2)
)
fit <- post_hoc_lm(psi_hat ~ trait, data = site,
                   weights = 1 / site$se^2, n_boot = 200L)
fit

tulpa documentation built on Oct. 19, 2026, 5:09 p.m.