forestplot (version 1.8)

forestplot: Draws a forest plot

Description

The forestplot is based on the rmeta-package`s forestplot function. This function resolves some limitations of the original functions such as:

• Adding expressions: Allows use of expressions, e.g. expression(beta)

• Multiple bands: Using multiple confidence bands for the same label

• Autosize: Adapts to viewport (graph) size

Usage

forestplot(...)
# S3 method for default
forestplot(labeltext, mean, lower, upper, align,
  is.summary = FALSE, graph.pos = "right", hrzl_lines, clip = c(-Inf,
  Inf), xlab = "", zero = ifelse(xlog, 1, 0), graphwidth = "auto",
  colgap, lineheight = "auto", line.margin, col = fpColors(),
  txt_gp = fpTxtGp(), xlog = FALSE, xticks, xticks.digits = 2,
  grid = FALSE, lwd.xaxis, lwd.zero, lwd.ci, lty.ci = 1, ci.vertices,
  ci.vertices.height = 0.1, boxsize, mar = unit(rep(5, times = 4),
  "mm"), title, legend, legend_args = fpLegend(),
  new_page = getOption("forestplot_new_page", TRUE),
  fn.ci_norm = fpDrawNormalCI, fn.ci_sum = fpDrawSummaryCI, fn.legend,
  ...)

Arguments

...

Passed on to the fn.ci_norm and fn.ci_sum arguments

labeltext

A list, matrix, vector or expression with the names of each row. The list should be wrapped in m x n number to resemble a matrix: list(list("rowname 1 col 1", "rowname 2 col 1"), list("r1c2", expression(beta)). You can also provide a matrix although this cannot have expressions by design: matrix(c("rowname 1 col 1", "rowname 2 col 1", "r1c2", "beta"), ncol=2). Use NA:s for blank spaces and if you provide a full column with NA then that column is a empty column that adds some space. Note: If you do not provide the mean/lower/upper arguments the function expects the label text to be a matrix containing the labeltext in the rownames and then columns for mean, lower, and upper.

mean

A vector or a matrix with the averages. You can also provide a 2D/3D matrix that is automatically converted to the lower/upper parameters. The values should be in exponentiated form if they follow this interpretation, e.g. use exp(mean) if you have the output from a logistic regression

lower

The lower bound of the confidence interval for the forestplot, needs to be the same format as the mean, i.e. matrix/vector of equal columns & length

upper

The upper bound of the confidence interval for the forestplot, needs to be the same format as the mean, i.e. matrix/vector of equal columns \& length

align

Vector giving alignment (l,r,c) for the table columns

is.summary

A vector indicating by TRUE/FALSE if the value is a summary value which means that it will have a different font-style

graph.pos

The position of the graph element within the table of text. The position can be 1-(ncol(labeltext) + 1). You can also choose set the positin to "left" or "right".

hrzl_lines

Add horizontal lines to graph. Can either be TRUE or a list of gpar. See line section below for details.

clip

Lower and upper limits for clipping confidence intervals to arrows

xlab

x-axis label

zero

x-axis coordinate for zero line. If you provide a vector of length 2 it will print a rectangle instead of just a line. If you provide NA the line is supressed.

graphwidth

Width of confidence interval graph, see unit for details on how to utilize mm etc. The default is auto, that is it uses up whatever space that is left after adjusting for text size and legend

colgap

Sets the gap between columns, defaults to 6 mm but for relative widths. Note that the value should be in unit(,"npc").

lineheight

Height of the graph. By default this is auto and adjustes to the space that is left after adjusting for x-axis size and legend. Sometimes it might be desireable to set the line height to a certain height, for instance if you have several forestplots you may want to standardize their line height, then you set this variable to a certain height, note this should be provided as a unit object. A good option is to set the line height to unit(2, "cm"). A third option is to set line height to "lines" and then you get 50 % more than what the text height is as your line height

line.margin

Set the margin between rows, provided in numeric or unit formar. When having multiple confidence lines per row setting the correct margin in order to visually separate rows

col

Set the colors for all the elements. See fpColors for details

txt_gp

Set the fonts etc for all text elements. See fpTxtGp for details

xlog

If TRUE, x-axis tick marks are to follow a logarithmic scale, e.g. for logistic regressoin (OR), survival estimates (HR), poisson regression etc. Note: This is an intentional break with the original forestplot function as I've found that exponentiated ticks/clips/zero effect are more difficult to for non-statisticians and there are sometimes issues with rounding the tick marks properly.

xticks

Optional user-specified x-axis tick marks. Specify NULL to use the defaults, numeric(0) to omit the x-axis. By adding a labels-attribute, attr(my_ticks, "labels") <- ... you can dictate the outputted text at each tick. If you specify a boolean vector then ticks indicated with FALSE wont be printed. Note that the labels have to be the same length as the main variable.

xticks.digits

The number of digits to allow in the x-axis if this is created by default

grid

If you want a discrete gray dashed grid at the level of the ticks you can set this parameter to TRUE. If you set the parameter to a vector of values lines will be drawn at the corresponding positions. If you want to specify the gpar of the lines then either directly pass a gpar object or set the gp attribute e.g. attr(line_vector, "gp") <- gpar(lty=2, col = "red")

lwd.xaxis

lwd for the xaxis, see gpar

lwd.zero

lwd for the vertical line that gives the no-effect line, see gpar

lwd.ci

lwd for the confidence bands, see gpar

lty.ci

lty for the confidence bands, see gpar

ci.vertices

Set this to TRUE if you want the ends of the confidence intervals to be shaped as a T. This is set default to TRUE if you have any other line type than 1 since there is a risk of a dash occurring at the very end, i.e. showing incorrectly narrow confidence interval.

ci.vertices.height

The height hoft the vertices. Defaults to npc units corresponding to 10% of the row height. Note that the arrows correspond to the vertices heights.

boxsize

Override the default box size based on precision

mar

A numerical vector of the form c(bottom, left, top, right) of the type unit

title

The title of the plot if any

legend

Legend corresponding to the number of bars

legend_args

The legend arguments as returned by the fpLegend function.

new_page

If you want the plot to appear on a new blank page then set this to TRUE, by default it is TRUE. If you want to change this behavior for all plots then set the options(forestplot_new_page = FALSE)

fn.ci_norm

You can specify exactly how the line with the box is drawn for the normal (i.e. non-summary) confidence interval by changing this parameter to your own function or some of the alternatives provided in the package. It defaults to the box function fpDrawNormalCI

fn.ci_sum

Same as previous argument but for the summary outputs and it defaults to fpDrawSummaryCI.

fn.legend

What type of function should be used for drawing the legends, this can be a list if you want different functions. It defaults to a box if you have anything else than a single function or the number of columns in the mean argument

Value

NULL

Multiple bands

Using multiple bands, i.e. multiple lines, per variable can be interesting when you want to compare different outcomes. E.g. if you want to compare survival specific to heart disease to overall survival for smoking it may be useful to have two bands on top of eachother. Another useful implementation is to show crude and adjusted estimates as separate bands.

Horizontal lines

The argument hrzl_lines can be either TRUE or a list with gpar elements:

• TRUEA line will be added based upon the is.summary rows. If the first line is a summary it

• gparThe same as above but the lines will be formatted according to the gpar element

• listThe list must either be numbered, i.e. list("2" = gpar(lty=1)), or have the same length as the NROW(mean) + 1. If the list is numbered the numbers should not exceed the NROW(mean) + 1. The no. 1 row designates the top, i.e. the line above the first row, all other correspond to the row below. Each element in the list needs to be TRUE, NULL, or gpar element. The TRUE defaults to a standard line, the NULL skips a line, while gpar corresponds to the fully customized line. Apart from allowing standard gpar line descriptions, lty, lwd, col, and more you can also specify gpar(columns = c(1:3, 5)) if you for instance want the line to skip a column.

Known issues

The x-axis does not entirely respect the margin. Autosizing boxes is not always the best option, try to set these manually as much as possible.

API-changes from <span class="pkg">rmeta</span>-package`s <code><a rd-options="rmeta" href="/link/forestplot?package=forestplot&version=1.8&to=rmeta" data-mini-rdoc="rmeta::forestplot">forestplot</a></code>

• xlog: The xlog outputs the axis in log() format but the input data should be in antilog/exp format

• col: The corresponding function is fpColors for this package

Details

See vignette("forestplot") for details.

See Also

Other forestplot functions: fpColors, fpDrawNormalCI, fpLegend

Examples

# NOT RUN {
#############################################
# 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"))


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