`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`

.

```
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)), …)

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.

For `qqnorm`

and `qqplot`

, a list with components

The x coordinates of the points that were/would be plotted

The original `y`

vector, i.e., the corresponding y
coordinates *including NAs*.

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.

# 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), prob = c(0.1, 0.6), col = 2) mtext("qqline(*, dist = qchisq(., df=3), prob = c(0.1, 0.6))") # }