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)
)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).
Value
A list of class "post_hoc_lm" with:
- summary
data.frame of coefficient estimates and CIs
- lm_fit
the underlying
lmobject- 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