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
or aes_string
. Only needs to be set
at the layer level if you are overriding the plot defaults.distribution
function.FALSE
(the default), removes missing values with
a warning. If TRUE
silently removes missing values.NA
, the default, includes if any aesthetics are mapped.
FALSE
never includes, and TRUE
always includes.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. borders
.layer
. This can
include aesthetics whose values you want to set, not map. See
layer
for more details.Calculation for quantile-quantile plot.
stat_qq
understands the following aesthetics (required aesthetics are in bold):
sample
x
y
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)))