Poisson Regression

Fits a count-response regression model via glm(family = poisson()), with optional stepwise selection.

Usage

reg_poisson(
  formula,
  data,
  step = FALSE,
  direction = c("both", "forward", "backward"),
  ...
)

Arguments

formula

A formula, e.g. y ~ x1 + x2.

data

A data frame containing the variables in formula.

step

Logical. Whether to perform stepwise selection (TRUE/FALSE). Default is FALSE.

direction

Character: "both" (default), "forward", or "backward".

...

Additional arguments passed to lm() and step().

Value

A named list analogous to reg_lm(), plus:

dispersion

Dispersion ratio (should be near 1).

residuals

Tibble with observed, fitted, Pearson residual.

Details

Performs an overdispersion check: dispersion > 1.5 suggests NB may be preferable.

Examples

set.seed(42)
x1 = rnorm(100)
mu = exp(1 + 0.3*x1)
y = rpois(100, mu)
res = reg_poisson(y ~ x1, data = data.frame(y, x1))
res$coefficient
#> # A tibble: 2 × 7
#>   term        estimate std.error statistic  p.value conf.low conf.high
#>   <chr>          <dbl>     <dbl>     <dbl>    <dbl>    <dbl>     <dbl>
#> 1 (Intercept)    0.848    0.0687     12.3  5.32e-35    0.712     0.985
#> 2 x1             0.423    0.0664      6.37 1.92e-10    0.291     0.554