| qqunif {gap} | R Documentation |
This function produces Q-Q plot for a random variable following uniform distribution with or without using log-scale. Note that the log-scale is by default for type "exp", which is a plot based on exponential order statistics. This appears to be more appropriate than the commonly used procedure whereby the expected value of uniform order statistics is directly log-transformed.
qqunif( u, type = "unif", logscale = TRUE, base = 10, col = palette()[4], lcol = palette()[2], ci = FALSE, alpha = 0.05, ... )
u |
a vector of uniformly distributed random variables. |
type |
string option to specify distribution: "unif"=uniform, "exp"=exponential. |
logscale |
to use logscale. |
base |
the base of the log function. |
col |
color for points. |
lcol |
color for the diagonal line. |
ci |
logical option to show confidence interval. |
alpha |
1-confidence level, e.g., 0.05. |
... |
other options as appropriae for the qqplot function. |
The returned value is a list with components of a qqplot:
expected value for uniform order statistics or its -log(,base) counterpart
observed value or its -log(,base) counterpart
Jing Hua Zhao
Balakrishnan N, Nevzorov VB. A Primer on Statistical Distributions. Wiley 2003.
Casella G, Berger RL. Statistical Inference, Second Edition. Duxbury 2002.
Davison AC. Statistical Models. Cambridge University Press 2003.
## Not run:
# Q-Q Plot for 1000 U(0,1) r.v., marking those <= 1e-5
u_obs <- runif(1000)
r <- qqunif(u_obs,pch=21,bg="blue",bty="n")
u_exp <- r$y
hits <- u_exp >= 2.30103
points(r$x[hits],u_exp[hits],pch=21,bg="green")
legend("topleft",sprintf("GC.lambda=\
## End(Not run)