coefficient_plot: Produce a coefficient table and plot

In finalfit: Quickly Create Elegant Regression Results Tables and Plots when Modelling

Produce a coefficient table and plot

Description

Produce a coefficient and plot from a lm() model.

Usage

coefficient_plot(
  .data,
  dependent,
  explanatory,
  random_effect = NULL,
  factorlist = NULL,
  lmfit = NULL,
  confint_type = "default",
  remove_ref = FALSE,
  breaks = NULL,
  column_space = c(-0.5, -0.1, 0.5),
  dependent_label = NULL,
  prefix = "",
  suffix = ": Coefficient, 95% CI, p-value)",
  table_text_size = 4,
  title_text_size = 13,
  plot_opts = NULL,
  table_opts = NULL,
  ...
)

Arguments

.data	Dataframe.
dependent	Character vector of length 1: name of depdendent variable (must be numeric/continuous).
explanatory	Character vector of any length: name(s) of explanatory variables.
random_effect	Character vector of length 1, name of random effect variable.
factorlist	Option to provide output directly from summary_factorlist().
lmfit	Option to provide output directly from lmmulti() and lmmixed().
confint_type	For for lmer models, one of c("default", "Wald", "profile", "boot") Note "default" == "Wald".
remove_ref	Logical. Remove reference level for factors.
breaks	Manually specify x-axis breaks in format c(0.1, 1, 10).
column_space	Adjust table column spacing.
dependent_label	Main label for plot.
prefix	Plots are titled by default with the dependent variable. This adds text before that label.
suffix	Plots are titled with the dependent variable. This adds text after that label.
table_text_size	Alter font size of table text.
title_text_size	Alter font size of title text.
plot_opts	A list of arguments to be appended to the ggplot call by "+".
table_opts	A list of arguments to be appended to the ggplot table call by "+".
...	Other parameters.

Value

Returns a table and plot produced in ggplot2.

See Also

Other finalfit plot functions: ff_plot(), hr_plot(), or_plot(), surv_plot()

Examples

library(finalfit)
library(ggplot2)

# Coefficient plot
explanatory = c("age.factor", "sex.factor", "obstruct.factor", "perfor.factor")
dependent = "nodes"
colon_s %>%
  coefficient_plot(dependent, explanatory)

colon_s %>%
  coefficient_plot(dependent, explanatory, table_text_size=4, title_text_size=14,
    plot_opts=list(xlab("Beta, 95% CI"), theme(axis.title = element_text(size=12))))

finalfit documentation built on Jan. 14, 2023, 5:07 p.m.