Skip to contents

Forest plot of a small set of estimates

Source: R/plot.R
mlumr_forest.Rd

A one-call forest plot for comparing a handful of estimates supplied as a data frame, e.g. several methods (naive / STC / ML-UMR) or several covariate profiles. Rows are drawn top-to-bottom in the order given. Returns a ggplot object, so further ggplot2 layers compose with +. This keeps the method-comparison forests in the vignettes to a single line instead of a hand-built ggplot() stack.

Usage

mlumr_forest(
  data,
  ref_line = NULL,
  log_x = FALSE,
  x = NULL,
  title = NULL,
  subtitle = NULL,
  color = "#3B6B9A",
  clip = TRUE,
  ...
)

Arguments

data

A data frame with one row per estimate. Columns are matched flexibly (first match wins): the row label from label / method / Method / Comparison (else the first character/factor column); the point estimate from est / estimate / mean; the interval bounds from lo/hi, q2.5/q97.5, ci_lower/ci_upper, conf.low/conf.high, or lower/upper.

ref_line

Null-effect reference line. By default it is read from the effect column when the label is one the package produces (1 for a ratio measure, 0 for a difference); otherwise 1 when log_x = TRUE and 0 otherwise. Pass it explicitly for a measure this does not name. Kept inside the clipping window.

log_x

Draw the x axis on a log10 scale (for ratio measures).

x, title, subtitle

Axis label and titles (passed to ggplot2::labs()).

color

Point and interval color.

clip

Logical; if TRUE (default), when one or two intervals are far wider than the rest the x axis is clipped to the bulk of the estimates and the over-wide interval's clipped end(s) are drawn with an arrow, so a single very uncertain estimate does not compress all the others into a sliver.

...

Unused.

Value

A ggplot object.

Details

All rows share one axis, so they must be on one effect scale: a frame with an effect column naming more than one measure is rejected rather than drawn against a single reference that cannot be right for both.

See also

marginal_effects(), naive(), stc()

Examples

if (FALSE) { # \dontrun{
forest_df <- data.frame(
  label = c("Naive", "STC", "ML-UMR SPFA"),
  est = c(res_naive$estimate, res_stc$estimate, me$mean),
  lo = c(res_naive$ci_lower, res_stc$ci_lower, me$q2.5),
  hi = c(res_naive$ci_upper, res_stc$ci_upper, me$q97.5)
)
mlumr_forest(forest_df, ref_line = 0, x = "Log odds ratio")
} # }