stats (version 3.3)

qqnorm: Quantile-Quantile Plots

Description

qqnorm is a generic function the default method of which produces a normal QQ plot of the values in y. qqline adds a line to a theoretical, by default normal, quantile-quantile plot which passes through the probs quantiles, by default the first and third quartiles.

qqplot produces a QQ plot of two datasets.

Graphical parameters may be given as arguments to qqnorm, qqplot and qqline.

Usage

qqnorm(y, ...)
## S3 method for class 'default':
qqnorm(y, ylim, main = "Normal Q-Q Plot",
       xlab = "Theoretical Quantiles", ylab = "Sample Quantiles",
       plot.it = TRUE, datax = FALSE, \dots)
qqline(y, datax = FALSE, distribution = qnorm,
       probs = c(0.25, 0.75), qtype = 7, ...)
qqplot(x, y, plot.it = TRUE, xlab = deparse(substitute(x)),
       ylab = deparse(substitute(y)), ...)

Arguments

x

The first sample for qqplot.

y

The second or only data sample.

xlab, ylab, main

plot labels. The xlab and ylab refer to the y and x axes respectively if datax = TRUE.

plot.it

logical. Should the result be plotted?

datax

logical. Should data values be on the x-axis?

distribution

quantile function for reference theoretical distribution.

probs

numeric vector of length two, representing probabilities. Corresponding quantile pairs define the line drawn.

qtype

the type of quantile computation used in quantile.

ylim, ...

graphical parameters.

Value

• For qqnorm and qqplot, a list with components
• x
  The x coordinates of the points that were/would be plotted
• y
  The original y vector, i.e., the corresponding y coordinates including NAs.

concept

normal probability plot

References

Becker, R. A., Chambers, J. M. and Wilks, A. R. (1988) The New S Language. Wadsworth & Brooks/Cole.

See Also

ppoints, used by qqnorm to generate approximations to expected order statistics for a normal distribution.

Examples

require(graphics)

y <- rt(200, df = 5)
qqnorm(y); qqline(y, col = 2)
qqplot(y, rt(300, df = 5))

qqnorm(precip, ylab = "Precipitation [in/yr] for 70 US cities")

## "QQ-Chisquare" : --------------------------
y <- rchisq(500, df = 3)
## Q-Q plot for Chi^2 data against true theoretical distribution:
qqplot(qchisq(ppoints(500), df = 3), y,
       main = expression("Q-Q plot for" ~~ {chi^2}[nu == 3]))
qqline(y, distribution = function(p) qchisq(p, df = 3),
       prob = c(0.1, 0.6), col = 2)
mtext("qqline(*, dist = qchisq(., df=3), prob = c(0.1, 0.6))")