qqnorm {stats} | R Documentation |
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, ...) ## Default S3 method: 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 = deparse1(substitute(x)), ylab = deparse1(substitute(y)), ...)
x |
The first sample for |
y |
The second or only data sample. |
xlab, ylab, main |
plot labels. The |
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 |
ylim, ... |
graphical parameters. |
For qqnorm
and qqplot
, a list with components
x |
The x coordinates of the points that were/would be plotted |
y |
The original |
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.
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.)