quickplots: Quickly plot common visualizations for meta-analyses

In malcolmbarrett/tidymeta: Tidy and Plot Meta Analyses

Description

forest_plot() presents study and summary estimates. influence_plot() shows the forest plot of senstivity analyses using senstivity(). cumulative_plot() shows the forest plot for cumulative(). funnel_plot() plots standard errors against the summary esitimate to assess publication bias.

Usage

forest_plot(x, estimate = estimate, study = study, size = weight,
  shape = type, col = type, xmin = conf.low, xmax = conf.high,
  group = NULL, alpha = 0.75, height = 0, ...)

influence_plot(x, estimate = l1o_estimate, study = study, size = 4,
  shape = 15, col = type, xmin = l1o_conf.low, xmax = l1o_conf.high,
  group = NULL, alpha = 0.75, height = 0, sum_lines = TRUE, ...)

cumulative_plot(x, estimate = cumul_estimate, study = study, size = 4,
  shape = 15, col = type, xmin = cumul_conf.low, xmax = cumul_conf.high,
  group = NULL, alpha = 0.75, height = 0, sum_lines = TRUE, ...)

funnel_plot(x, estimate = estimate, std.error = std.error, size = 3,
  shape = NULL, col = NULL, alpha = 0.75, reverse_y = TRUE,
  log_summary = FALSE, ...)

Arguments

x	a tidied meta-analysis
estimate	variable name of point estimates
study	variable name of study labels
size	point size; either an aesthetic variable or a specific shape.
shape	shape of the points; either an aesthetic variable or a specific shape.
col	color of the points and lines; either an aesthetic variable or a specific color.
xmin	lower confidence interval variable name
xmax	upper confidence interval variable name
group	a grouping variable
alpha	transparancy level
height	line height for error bars
...	additional arguments
sum_lines	logical. Should vertical lines demarcating the summary estimate and confidence intervals be included?
std.error	variable name of standard error variable
reverse_y	logical. Should the y-axis be reversed?
log_summary	logical. Should the estimate and confidence intervals be log-transformed?

Value

a ggplot2 object

Examples

library(dplyr)

ma <- iud_cxca %>%
  group_by(group) %>%
  meta_analysis(yi = lnes, sei = selnes, slab = study_name)

forest_plot(ma)

funnel_plot(ma)

ma %>%
  sensitivity() %>%
  influence_plot()

ma %>%
  cumulative() %>%
  cumulative_plot()

malcolmbarrett/tidymeta documentation built on May 30, 2019, 11:42 a.m.