gf_qq()
an gf_qqstep()
both create quantile-quantile plots. They
differ in how they display the qq-plot.
gf_qq()
uses points and gf_qqstep()
plots a step function
through these points.
gf_qq(object = NULL, gformula = NULL, data = NULL, group,
distribution = stats::qnorm, dparams = list(), xlab, ylab, title,
subtitle, caption, geom = "point", stat = "qq",
position = "identity", show.legend = NA, show.help = NULL,
inherit = TRUE, environment = parent.frame(), ...)gf_qqline(object = NULL, gformula = NULL, data = NULL, group,
distribution = stats::qnorm, dparams = list(), linetype = "dashed",
alpha = 0.7, xlab, ylab, title, subtitle, caption, geom = "line",
stat = "qqline", position = "identity", show.legend = NA,
show.help = NULL, inherit = TRUE, environment = parent.frame(),
...)
gf_qqstep(object = NULL, gformula = NULL, data = NULL, group,
distribution = stats::qnorm, dparams = list(), xlab, ylab, title,
subtitle, caption, geom = "step", stat = "qq",
position = "identity", show.legend = NA, show.help = NULL,
inherit = TRUE, environment = parent.frame(), ...)
When chaining, this holds an object produced in the earlier portions of the chain. Most users can safely ignore this argument. See details and examples.
A formula with shape ~ sample
. Facets can be
added using |
.
The data to be displayed in this layer. There are three options:
If NULL
, the default, the data is inherited from the plot
data as specified in the call to ggplot()
.
A data.frame
, or other object, will override the plot
data. All objects will be fortified to produce a data frame. See
fortify()
for which variables will be created.
A function
will be called with a single argument,
the plot data. The return value must be a data.frame.
, and
will be used as the layer data.
Used for grouping.
Distribution function to use, if x not specified
Additional parameters passed on to distribution
function.
Label for x-axis. See also gf_labs()
.
Label for y-axis. See also gf_labs()
.
Title, sub-title, and caption for the plot.
See also gf_labs()
.
Title, sub-title, and caption for the plot.
See also gf_labs()
.
Title, sub-title, and caption for the plot.
See also gf_labs()
.
Use to override the default connection between
geom_histogram
/geom_freqpoly
and stat_bin
.
Use to override the default connection between
geom_histogram
/geom_freqpoly
and stat_bin
.
Position adjustment, either as a string, or the result of a call to a position adjustment function.
logical. Should this layer be included in the legends?
NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.
It can also be a named logical vector to finely select the aesthetics to
display.
If TRUE
, display some minimal help.
A logical indicating whether default attributes are inherited.
An environment in which to look for variables not found in data
.
Additional arguments. Typically these are
(a) ggplot2 aesthetics to be set with attribute = value
,
(b) ggplot2 aesthetics to be mapped with attribute = ~ expression
, or
(c) attributes of the layer as a whole, which are set with attribute = value
.
A linetype (numeric or "dashed", "dotted", etc.) or a formula used for mapping linetype.
Opacity (0 = invisible, 1 = opaque).
a gg object
Positional attributes (a.k.a, aesthetics) are specified using the formula in gformula
.
Setting and mapping of additional attributes can be done through the
use of additional arguments.
Attributes can be set can be set using arguments of the form attribute = value
or
mapped using arguments of the form attribute = ~ expression
.
In formulas of the form A | B
, B
will be used to form facets using
facet_wrap()
or facet_grid()
.
This provides an alternative to
gf_facet_wrap()
and
gf_facet_grid()
that is terser and may feel more familiar to users
of lattice.
Evaluation of the ggplot2 code occurs in the environment of gformula
.
This will typically do the right thing when formulas are created on the fly, but might not
be the right thing if formulas created in one environment are used to create plots
in another.
# NOT RUN {
gf_qq( ~ rnorm(100))
gf_qq( ~ Sepal.Length | Species, data = iris) %>% gf_qqline()
gf_qq( ~ Sepal.Length | Species, data = iris) %>% gf_qqline(tail = 0.10)
gf_qq( ~ Sepal.Length, color = ~ Species, data = iris) %>%
gf_qqstep( ~ Sepal.Length, color = ~ Species, data = iris)
# }
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