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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
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,
...)
Passed on to the fn.ci_norm and
fn.ci_sum arguments
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.
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
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
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
Vector giving alignment (l,r,c) for the table columns
A vector indicating by TRUE/FALSE if
the value is a summary value which means that it will have a different
font-style
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".
Add horizontal lines to graph. Can either be TRUE or a list
of gpar. See line section below for details.
Lower and upper limits for clipping confidence intervals to arrows
x-axis label
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.
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
Sets the gap between columns, defaults to 6 mm but for relative widths.
Note that the value should be in unit(,"npc").
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
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
Set the colors for all the elements. See fpColors for
details
Set the fonts etc for all text elements. See fpTxtGp
for details
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.
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.
The number of digits to allow in the x-axis if this is created by default
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 for the xaxis, see gpar
lwd for the vertical line that gives the no-effect line, see gpar
lwd for the confidence bands, see gpar
lty for the confidence bands, see gpar
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.
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.
Override the default box size based on precision
A numerical vector of the form c(bottom, left, top, right) of
the type unit
The title of the plot if any
Legend corresponding to the number of bars
The legend arguments as returned by the fpLegend function.
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)
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
Same as previous argument but for the summary outputs
and it defaults to fpDrawSummaryCI.
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
NULL
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.
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.
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.
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
See vignette("forestplot") for details.
Other forestplot functions: fpColors,
fpDrawNormalCI, fpLegend
# 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")
# }
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