forestmodel (version 0.3.3)

forest_model: Produce a forest plot based on a regression model

Description

Produce a forest plot based on a regression model

Usage

forest_model(model, panels = default_forest_panels(model, factor_separate_line
  = factor_separate_line), covariates = NULL, exponentiate = NULL,
  funcs = NULL, factor_separate_line = FALSE, format_options = list(colour
  = "black", shape = 15, text_size = 5, banded = TRUE),
  theme = theme_forest(), limits = NULL, breaks = NULL,
  return_data = FALSE, recalculate_width = TRUE,
  recalculate_height = TRUE)

Arguments

model

regression model produced by lm, glm, coxph

panels

list with details of the panels that make up the plot (See Details)

covariates

a character vector optionally listing the variables to include in the plot (defaults to all variables)

exponentiate

whether the numbers on the x scale should be exponentiated for plotting

funcs

optional list of functions required for formatting panels$display

factor_separate_line

whether to show the factor variable name on a separate line

format_options

formatting options as a list including colour of the point estimate and error bars, shape of the point estimate, banded whether to show light grey bands behind alternate rows, text_size size of text in

theme

theme to apply to the plot

limits

limits of the forest plot on the X-axis (taken as the range of the data by default)

breaks

breaks to appear on the X-axis (note these will be exponentiated if exponentiate == TRUE)

return_data

return the data to produce the plot as well as the plot itself

recalculate_width

TRUE to recalculate panel widths using the current device or the desired plot width in inches

recalculate_height

TRUE to shrink text size using the current device or the desired plot height in inches

Value

• A ggplot ready for display or saving, or (with return_data == TRUE, a list with the parameters to call panel_forest_plot in the element plot_data and the ggplot itself in the element plot)

Details

This function takes the model output from one of the common model functions in R (e.g. lm, glm, coxph). The panels parameter is a list of lists each of which have an element width and, optionally, item, display, display_na, heading, hjust and fontface. item can be "forest" for the forest plot (exactly one required) or "vline" for a vertical line. display indicates which column to display as text. It can be a quoted variable name or a formula. The column display can include the standard ones produced by tidy and in addition variable (the term in the model; for factors this is the bare variable without the level), level (the level of factors), reference (TRUE for the reference level of a factor). For coxph models, there will also be n_events for the number of events in the group with that level of the factor and person_time for the person-time in that group. The function trans is definded to be the transformation between the coefficients and the scales (e.g. exp). Other functions not in base R can be provided as a list with the parameter funcs. display_na allows for an alternative display for NA terms within estimate.

Examples

library("survival")
library("dplyr")
pretty_lung <- lung %>%
  transmute(time,
            status,
            Age = age,
            Sex = factor(sex, labels = c("Male", "Female")),
            ECOG = factor(lung$ph.ecog),
            `Meal Cal` = meal.cal)

print(forest_model(coxph(Surv(time, status) ~ ., pretty_lung)))

# Example with custom panels

panels <- list(list(width = 0.03),
  list(width = 0.1, display = ~variable, fontface = "bold", heading = "Variable"),
  list(width = 0.1, display = ~level),
  list(width = 0.05, display = ~n, hjust = 1, heading = "N"),
  list(width = 0.05, display = ~n_events, width = 0.05, hjust = 1, heading = "Events"),
  list(width = 0.05,
    display = ~replace(sprintf("%0.1f", person_time/365.25), is.na(person_time), ""),
    heading = "Person-\nYears", hjust = 1),
  list(width = 0.03, item = "vline", hjust = 0.5),
  list(width = 0.55, item = "forest", hjust = 0.5, heading = "Hazard ratio", linetype = "dashed",
    line_x = 0),
  list(width = 0.03, item = "vline", hjust = 0.5),
  list(width = 0.12, display = ~ifelse(reference, "Reference", sprintf("%0.2f (%0.2f, %0.2f)",
    trans(estimate), trans(conf.low), trans(conf.high))), display_na = NA),
  list(width = 0.05,
    display = ~ifelse(reference, "", format.pval(p.value, digits = 1, eps = 0.001)),
    display_na = NA, hjust = 1, heading = "p"),
  list(width = 0.03)
)
forest_model(coxph(Surv(time, status) ~ ., pretty_lung), panels)

data_for_lm <- data_frame(x = rnorm(100, 4),
                          y = rnorm(100, 3, 0.5),
                          z = rnorm(100, 2, 2),
                          outcome = 3 * x - 2 * y + 4 * z + rnorm(100, 0, 0.1))

print(forest_model(lm(outcome ~ ., data_for_lm)))

data_for_logistic <- data_for_lm %>% mutate(
  outcome = (0.5 * (x - 4) * (y - 3) * (z - 2) + rnorm(100, 0, 0.05)) > 0.5
)

print(forest_model(glm(outcome ~ ., binomial(), data_for_logistic)))