Plots empirical quantiles of a variable against theoretical quantiles of a comparison distribution.
qqfun(
x,
distribution = "norm",
ylab = deparse(substitute(x)),
xlab = paste(distribution, "quantiles"),
main = NULL,
las = par("las"),
envelope = 0.95,
labels = FALSE,
col = palette()[4],
lcol = palette()[2],
xlim = NULL,
ylim = NULL,
lwd = 1,
pch = 1,
bg = palette()[4],
cex = 0.4,
line = c("quartiles", "robust", "none"),
...
)
NULL
. These functions are used only for their side effect (to make a graph).
vector of numeric values.
root name of comparison distribution -- e.g., norm
for the
normal distribution; t
for the t-distribution.
label for vertical (empirical quantiles) axis.
label for horizontal (comparison quantiles) axis.
label for plot.
if 0
, ticks labels are drawn parallel to the
axis; set to 1
for horizontal labels (see par
).
confidence level for point-wise confidence envelope, or
FALSE
for no envelope.
vector of point labels for interactive point identification,
or FALSE
for no labels.
color for points; the default is the fourth entry
in the current color palette (see palette
and par
).
color for lines; the default is the second entry as above.
the x limits (x1, x2) of the plot. Note that x1 > x2 is allowed and leads to a reversed axis.
the y limits of the plot.
line width; default is 1
(see par
).
Confidence envelopes are drawn at half this line width.
plotting character for points; default is 1
(a circle, see par
).
background color of points.
factor for expanding the size of plotted symbols; the default is
.4
.
"quartiles"
to pass a line through the quartile-pairs, or
"robust"
for a robust-regression line; the latter uses the rlm
function in the MASS
package. Specifying line = "none"
suppresses the line.
arguments such as df
to be passed to the appropriate quantile function.
John Fox, Jing Hua Zhao
Draws theoretical quantile-comparison plots for variables and for studentized residuals from a linear model. A comparison line is drawn on the plot either through the quartiles of the two distributions, or by robust regression.
Any distribution for which quantile and density functions exist in R (with prefixes
q
and d
, respectively) may be used.
Studentized residuals are plotted against the appropriate t-distribution.
This is adapted from qq.plot of package car with different values for points and lines, more options, more transparent code and examples in the current setting. Another similar but sophisticated function is qqmath of package lattice.
Davison, A. C. (2003) Statistical Models. Cambridge University Press.
Leemis, L. M., J. T. Mcqueston (2008) Univariate distribution relationships. The American Statistician 62:45-53
qqnorm
, qqunif
, gcontrol2
if (FALSE) {
p <- runif(100)
alpha <- 1/log(10)
qqfun(p,dist="unif")
qqfun(-log10(p),dist="exp",rate=alpha,pch=21)
#library(car)
#qq.plot(p,dist="unif")
#qq.plot(-log10(p),dist="exp",rate=alpha)
#library(lattice)
#qqmath(~ -log10(p), distribution = function(p) qexp(p,rate=alpha))
}
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