hc.ppoints {NADA}

Helsel-Cohn style plotting positions

Description

Helsel-Cohn style plotting positions for multiply-censored data.

Usage

    hc.ppoints(obs, censored, na.action)
    hc.ppoints.uncen(obs, censored, cn, na.action)
    hc.ppoints.cen(obs, censored, cn, na.action)

Arguments

obs

A numeric vector of observations. This includes both censored and uncensored observations.

censored

A logical vector indicating TRUE where an observation in v is censored (a less-than value) and FALSE otherwise.

cn

An optional argument for internal-code use only. cn = a Cohn Numbers list (quantities described by Helsel and Cohn (1988) in their formulation of the problem).

na.action

A function which indicates what should happen when the data contain NAs. The default is set by the na.action setting of options, and is na.omit if that is unset. Another possible value is NULL, no action.

Details

The function computes Wiebull-type plotting positions of data containing mixed uncensored and censored data. The formula was first described by Hirsch and Stedinger (1897) and latter reformulated by Helsel and Cohn (1988). It assumes that censoring is left-censoring (less-thans). A detailed discussion of the formulation is in Lee and Helsel (in press).

Note that if the input vector ‘censored’ is of zero length, then the plotting positions are calculated using ppoints. Otherwise, hc.ppoints.uncen and hc.ppoints.cen are used.

hc.ppoints.uncen calculates plotting positions for uncensored data only.

hc.ppoints.cen calculates plotting positions for censored data only.

Value

hc.ppoints returns a numeric vector of plotting positions which correspond to the observations in the input vector 'obs'.

hc.ppoints.uncen returns a numeric vector of plotting positions which correspond to the uncensored observations in the input vector 'obs'.

hc.ppoints.cen returns a numeric vector of plotting positions which correspond to the censored observations in the input vector 'obs'.

Author(s)

R. Lopaka Lee <[e-mail]>

Dennis Helsel <[e-mail]>

References

Lee and Helsel (in press), Statistical analysis of environmental data containing multiple detection limits: S-language software for linear regression on order statistics, Computers in Geoscience vol. X, pp. X-X

Dennis R. Helsel and Timothy A. Cohn (1988), Estimation of descriptive statistics for multiply censored water quality data, Water Resources Research vol. 24, no. 12, pp.1997-2004

Robert M. Hirsch and Jery R. Stedinger (1987), Plotting positions for historical floods and their precision. Water Resources Research, vol. 23, no. 4, pp. 715-727.

See Also

ros, splitQual

Examples

    obs      = c(0.5,    0.5,   1.0,  1.5,   5.0,    10,   100)
    censored = c(TRUE, FALSE, FALSE, TRUE, FALSE, FALSE, FALSE)

    hc.ppoints(obs, censored)