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Tidy a model object for forest plotting

Source: R/tidy_forest_model.R
tidy_forest_model.Rd

Uses broom::tidy() to convert a fitted model into forest-plot data.

Usage

tidy_forest_model(
  model,
  conf.int = TRUE,
  conf.level = 0.95,
  exponentiate = FALSE,
  intercept = FALSE,
  sort_terms = c("none", "descending", "ascending")
)

Arguments

model

A fitted model object supported by broom::tidy().

conf.int

Logical; if TRUE, request confidence intervals from broom::tidy().

conf.level

Confidence level for intervals.

exponentiate

Logical; passed through to broom::tidy().

intercept

Logical; if FALSE, drop the intercept term.

sort_terms

How to sort rows: "none", "descending", or "ascending".

Value

A standardized coefficient data frame ready for ggforestplot().

Examples

if (requireNamespace("broom", quietly = TRUE)) {
  fit <- lm(mpg ~ wt + hp + qsec, data = mtcars)
  tidy_forest_model(fit)

  set.seed(123)
  logit_data <- data.frame(
    age = rnorm(250, mean = 62, sd = 8),
    bmi = rnorm(250, mean = 28, sd = 4),
    treatment = factor(rbinom(250, 1, 0.45), labels = c("Control", "Treatment"))
  )
  linpred <- -9 + 0.09 * logit_data$age + 0.11 * logit_data$bmi +
    0.9 * (logit_data$treatment == "Treatment")
  logit_data$event <- rbinom(250, 1, plogis(linpred))
  logit_fit <- glm(event ~ age + bmi + treatment, data = logit_data, family = binomial())

  tidy_forest_model(logit_fit, exponentiate = TRUE)
}
#>                 term estimate conf.low conf.high              label group
#> 1                age 1.103890 1.061512  1.151411                age  <NA>
#> 2                bmi 1.118858 1.043220  1.204389                bmi  <NA>
#> 3 treatmentTreatment 3.133149 1.782915  5.613038 treatmentTreatment  <NA>
#>   grouping separate_groups    n events      p.value
#> 1     <NA>            <NA> <NA>   <NA> 1.719452e-06
#> 2     <NA>            <NA> <NA>   <NA> 2.093412e-03
#> 3     <NA>            <NA> <NA>   <NA> 9.087778e-05