Creates the tabular input used by forest_reg() from uni_reg(),
multi_reg(), and optionally descriptive_table() objects. This
function is useful when users want to inspect, edit, or reuse the exact data
that will be passed to the forest plot. Stratified regression objects are
also supported when supplied one at a time; the returned data frame keeps the
characteristic rows once and places strata side by side as separate effect
columns for forest_reg().
Arguments
- uni
A
gtregressionobject fromuni_reg(). Ifmultiis supplied withoutuni, the multivariable object is plotted as a single effect column.- multi
Optional
gtregressionobject frommulti_reg().- desc
Optional descriptive table object from
descriptive_table().- digits
Number of digits used when formatting confidence intervals.
Value
A data frame with display columns, formatted effect-size columns,
standard-error columns, and plotting attributes used by forest_reg()
to draw confidence intervals.
Examples
birthwt_data <- data_birthwt |>
dplyr::mutate(
smoke = factor(smoke, levels = c(0, 1), labels = c("No", "Yes")),
ht = factor(ht, levels = c(0, 1), labels = c("No", "Yes")),
low = factor(low, levels = c(0, 1), labels = c("Normal BW", "Low BW"))
)
uni_or <- uni_reg(
birthwt_data,
outcome = "low",
exposures = c("age", "lwt", "smoke", "ht"),
approach = "logit"
)
multi_or <- multi_reg(
birthwt_data,
outcome = "low",
exposures = c("smoke", "ht"),
adjust_for = c("age", "lwt"),
approach = "logit"
)
forest_data <- forest_df(uni_or, multi_or)
head(forest_data)
#> Characteristic OR (95% CI)
#> 1 age 0.95 (0.89-1.01)
#> 2 lwt 0.99 (0.97-1.00)
#> 3 smoke
#> 4 No Ref.
#> 5 Yes 2.02 (1.08-3.78)
#> 6 ht
#> Adjusted OR (95% CI) se_uni
#> 1 0.031513184
#> 2 0.006169475
#> 3 NA
#> 4 Ref. NA
#> 5 1.96 (1.03-3.70) 0.319636414
#> 6 NA
#> se_adj
#> 1 NA
#> 2 NA
#> 3 NA
#> 4 NA
#> 5 0.3258718
#> 6 NA