R: Q-Q plot for uniformly distributed random variable

qqunif {gap} | R Documentation

## Q-Q plot for uniformly distributed random variable

### Description

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.

### Usage

    qqunif(u,type="unif",logscale=TRUE,base=10,
           col=palette()[4],lcol=palette()[2],ci=FALSE,alpha=0.05,...)

### Arguments

`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

### Value

The returned value is a list with components of a qqplot:

`x` | expected value for uniform order statistics or its -log(,base) counterpart
`y` | observed value or its -log(,base) counterpart

### References

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.

### Author(s)

Jing Hua Zhao

### See Also

`qqfun`

### Examples

    ## 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=%.4f",gc.lambda(u_obs)))

    ## End(Not run)

[Package gap version 1.2.3-1 Index]