Logistic Regression

Fits a binary logistic regression model via glm(family = binomial()), with optional stepwise selection.

Usage

reg_logistic(
  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:

odds_ratio

Tibble with odds ratios and 95% CIs.

residuals

Tibble of observed (0/1), fitted probability, Pearson residual.

Details

Same stepwise logic as reg_lm, but uses GLM with the binomial family. A Hosmer-Lemeshow goodness-of-fit test is included in diagnostics.

Examples

set.seed(42)
x1 = rnorm(100)
logit = 0.5 + x1
prob = 1 / (1 + exp(-logit))
y = rbinom(100, 1, prob)
res = reg_logistic(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)    0.285     0.226      1.26 0.207       -0.163     0.734
#> 2 x1             1.07      0.270      3.98 0.0000682    0.539     1.61