stats (version 3.6.2)

# 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 default
qqnorm(y, ylim, main = "Normal Q-Q Plot",
xlab = "Theoretical Quantiles", ylab = "Sample Quantiles",
plot.it = TRUE, datax = FALSE, …)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 `NA`s.

## References

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

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

## Examples

Run this code
``````# NOT RUN {
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),
probs = c(0.1, 0.6), col = 2)
mtext("qqline(*, dist = qchisq(., df=3), prob = c(0.1, 0.6))")
## (Note that the above uses ppoints() with a = 1/2, giving the
## probability points for quantile type 5: so theoretically, using
## qqline(qtype = 5) might be preferable.)
# }
``````

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