A quantile-quantile plot
A quantile-quantile plot
geom_qq(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)stat_qq(mapping = NULL, data = NULL, geom = "point", position = "identity", ..., distribution = stats::qnorm, dparams = list(), na.rm = FALSE, show.legend = NA, inherit.aes = TRUE)
- Set of aesthetic mappings created by
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.
- The data to be displayed in this layer. There are three
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.
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.
- The geometric object to use display the data
- Position adjustment, either as a string, or the result of a call to a position adjustment function.
- other arguments passed on to
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.
- Distribution function to use, if x not specified
- Additional parameters passed on to
FALSE, the default, missing values are removed with a warning. If
TRUE, missing values are silently removed.
- logical. Should this layer be included in the legends?
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.
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) ggplot(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)))