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\title{Calculation for quantile-quantile plot.}
stat_qq(mapping = NULL, data = NULL, geom = "point",
position = "identity", distribution = qnorm,
dparams = list(), na.rm = FALSE, ...)
\item{distribution}{Distribution function to use, if x
not specified}
\item{dparams}{Parameters for distribution function}
\item{...}{Other arguments passed to distribution
\item{na.rm}{If \code{FALSE} (the default), removes
missing values with a warning. If \code{TRUE} silently
removes missing values.}
\item{mapping}{The aesthetic mapping, usually constructed
with \code{\link{aes}} or \code{\link{aes_string}}. Only
needs to be set at the layer level if you are overriding
the plot defaults.}
\item{data}{A layer specific dataset - only needed if you
want to override the plot defaults.}
\item{geom}{The geometric object to use display the data}
\item{position}{The position adjustment to use for
overlappling points on this layer}
a data.frame with additional columns:
\item{sample}{sample quantiles}
\item{theoretical}{theoretical quantiles}
Calculation for quantile-quantile plot.
# From ?qqplot
y <- rt(200, df = 5)
qplot(sample = y, stat="qq")
# qplot is smart enough to use stat_qq if you use sample
qplot(sample = y)
qplot(sample = precip)
qplot(sample = y, dist = qt, dparams = list(df = 5))
df <- data.frame(y)
ggplot(df, aes(sample = y)) + stat_qq()
ggplot(df, aes(sample = y)) + geom_point(stat = "qq")
# Use fitdistr from MASS to estimate distribution params
params <- as.list(fitdistr(y, "t")$estimate)
ggplot(df, aes(sample = y)) + stat_qq(dist = qt, dparam = params)
# Using to explore the distribution of a variable
qplot(sample = mpg, data = mtcars)
qplot(sample = mpg, data = mtcars, colour = factor(cyl))
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