stat_qq {animint2} | R Documentation |
Calculation for quantile-quantile plot.
Description
Calculation for quantile-quantile plot.
Usage
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
)
Arguments
mapping |
Set of aesthetic mappings created by |
data |
The data to be displayed in this layer. There are three options: If A A |
geom |
The geometric object to use display the data |
position |
Position adjustment, either as a string, or the result of a call to a position adjustment function. |
... |
other arguments passed on to |
distribution |
Distribution function to use, if x not specified |
dparams |
Additional parameters passed on to |
na.rm |
If |
show.legend |
logical. Should this layer be included in the legends?
|
inherit.aes |
If |
Aesthetics
stat_qq
understands the following aesthetics (required aesthetics are in bold):
sample
x
y
Computed variables
- sample
sample quantiles
- theoretical
theoretical quantiles
Examples
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)))