stat_qq(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)geom_qq(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
aes_. If specified and
inherit.aes = TRUE(the default), it is combined with the default mapping at the top level of the plot. You must supply
mappingif there is no plot mapping.
NULL, the default, the data is inherited from the plot data as specified in the call to
data.frame, or other object, will override the plot data. All objects will be fortified to produce a data frame. See
fortifyfor which variables will be created. A
functionwill 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.
layer. These are often aesthetics, used to set an aesthetic to a fixed value, like
color = "red"or
size = 3. They may also be parameters to the paired geom/stat.
FALSE(the default), removes missing values with a warning. If
TRUEsilently removes missing values.
NA, the default, includes if any aesthetics are mapped.
FALSEnever includes, and
FALSE, overrides the default aesthetics, rather than combining with them. This is most useful for helper functions that define both data and aesthetics and shouldn't inherit behaviour from the default plot specification, e.g.
Calculation for quantile-quantile plot.
stat_qq understands the following aesthetics (required aesthetics are in bold):
df <- data.frame(y = rt(200, df = 5)) p <- ggplot(df, aes(sample = y)) p + stat_qq()
p + geom_point(stat = "qq")
# Use fitdistr from MASS to estimate distribution params params <- as.list(MASS::fitdistr(df$y, "t")$estimate)Warning message: NaNs produced Warning message: NaNs producedggplot(df, aes(sample = y)) + stat_qq(distribution = qt, dparams = params["df"])
# Using to explore the distribution of a variable ggplot(mtcars) + stat_qq(aes(sample = mpg))
ggplot(mtcars) + stat_qq(aes(sample = mpg, colour = factor(cyl)))