| stat_qq {ggplot2} | R Documentation |
Calculation for quantile-quantile plot
stat_qq(mapping = NULL, data = NULL, geom = "point", position = "identity",
distribution = qnorm, dparams = list(), na.rm = FALSE, ...)
mapping |
mapping between variables and aesthetics generated by aes |
data |
dataset used in this layer, if not specified uses plot dataset |
geom |
geometric used by this layer |
position |
position adjustment used by this layer |
distribution |
NULL |
dparams |
Parameters for distribution function |
na.rm |
NULL |
... |
Other arguments passed to distribution function |
This page describes stat\_qq, see layer and qplot for how to create a complete plot from individual components.
A layer
The following aesthetics can be used with stat\_qq. Aesthetics are mapped to variables in the data with the aes function: stat\_qq(aes(x = var))
sample: NULL (required)
x: x position
y: y position
Hadley Wickham, http://had.co.nz/
## Not run: # 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(MASS::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)) ## End(Not run)