build/R4.0.2-win64/forestplot/examples/forestplot_example.R

In kylehamilton/MAJOR: MAJOR: Meta-Analysis for JAMOVI

#############################################
# Simple examples of how to do a forestplot #
#############################################

ask <- par(ask = TRUE)

# A basic example, create some fake data
row_names <- list(list("test = 1", expression(test >= 2)))
test_data <- data.frame(
  coef = c(1.59, 1.24),
  low = c(1.4, 0.78),
  high = c(1.8, 1.55)
)
forestplot(row_names,
  test_data$coef,
  test_data$low,
  test_data$high,
  zero = 1,
  cex  = 2,
  lineheight = "auto",
  xlab = "Lab axis txt"
)

# Print two plots side by side using the grid
# package's layout option for viewports
grid.newpage()
pushViewport(viewport(layout = grid.layout(1, 2)))
pushViewport(viewport(layout.pos.col = 1))
forestplot(row_names,
  test_data$coef,
  test_data$low,
  test_data$high,
  zero = 1,
  cex  = 2,
  lineheight = "auto",
  xlab = "Lab axis txt",
  new_page = FALSE
)
popViewport()
pushViewport(viewport(layout.pos.col = 2))
forestplot(row_names,
  test_data$coef,
  test_data$low,
  test_data$high,
  zero = 1,
  cex  = 2,
  lineheight = "auto",
  xlab = "Lab axis txt",
  new_page = FALSE
)
popViewport(2)


# An advanced test
test_data <- data.frame(
  coef1 = c(1, 1.59, 1.3, 1.24),
  coef2 = c(1, 1.7, 1.4, 1.04),
  low1 = c(1, 1.3, 1.1, 0.99),
  low2 = c(1, 1.6, 1.2, 0.7),
  high1 = c(1, 1.94, 1.6, 1.55),
  high2 = c(1, 1.8, 1.55, 1.33)
)

col_no <- grep("coef", colnames(test_data))
row_names <- list(
  list("Category 1", "Category 2", "Category 3", expression(Category >= 4)),
  list(
    "ref",
    substitute(
      expression(bar(x) == val),
      list(val = round(rowMeans(test_data[2, col_no]), 2))
    ),
    substitute(
      expression(bar(x) == val),
      list(val = round(rowMeans(test_data[3, col_no]), 2))
    ),
    substitute(
      expression(bar(x) == val),
      list(val = round(rowMeans(test_data[4, col_no]), 2))
    )
  )
)

coef <- with(test_data, cbind(coef1, coef2))
low <- with(test_data, cbind(low1, low2))
high <- with(test_data, cbind(high1, high2))
forestplot(row_names, coef, low, high,
  title = "Cool study",
  zero = c(0.98, 1.02),
  grid = structure(c(2^-.5, 2^.5),
    gp = gpar(col = "steelblue", lty = 2)
  ),
  boxsize = 0.25,
  col = fpColors(
    box = c("royalblue", "gold"),
    line = c("darkblue", "orange"),
    summary = c("darkblue", "red")
  ),
  xlab = "The estimates",
  new_page = TRUE,
  legend = c("Treatment", "Placebo"),
  legend_args = fpLegend(
    pos = list("topright"),
    title = "Group",
    r = unit(.1, "snpc"),
    gp = gpar(col = "#CCCCCC", lwd = 1.5)
  )
)

# An example of how the exponential works
test_data <- data.frame(
  coef = c(2.45, 0.43),
  low = c(1.5, 0.25),
  high = c(4, 0.75),
  boxsize = c(0.5, 0.5)
)
row_names <- cbind(
  c("Name", "Variable A", "Variable B"),
  c("HR", test_data$coef)
)
test_data <- rbind(rep(NA, 3), test_data)

forestplot(
  labeltext = row_names,
  test_data[, c("coef", "low", "high")],
  is.summary = c(TRUE, FALSE, FALSE),
  boxsize = test_data$boxsize,
  zero = 1,
  xlog = TRUE,
  col = fpColors(lines = "red", box = "darkred")
)

# An example using shapes_gp
forestplot(
  labeltext = cbind(Author = c("Smith et al", "Smooth et al", "Al et al")),
  mean = cbind(1:3, 1.5:3.5),
  lower = cbind(0:2, 0.5:2.5),
  upper = cbind(4:6, 5.5:7.5),
  is.summary = c(FALSE, FALSE, TRUE),
  shapes_gp = fpShapesGp(
    default = gpar(lineend = "square", linejoin = "mitre", lwd = 3, col = "pink"),
    box = gpar(fill = "black", col = "red"), # only one parameter
    lines = list( # as many parameters as CI
      gpar(lwd = 10), gpar(lwd = 5),
      gpar(), gpar(),
      gpar(lwd = 2), gpar(lwd = 1)
    ),
    summary = list( # as many parameters as band per label
      gpar(fill = "violet", col = "gray", lwd = 10),
      gpar(fill = "orange", col = "gray", lwd = 10)
    )
  ),
  vertices = TRUE
)

par(ask = ask)
# See vignette for a more detailed description
# vignette("forestplot",  package="forestplot")