Imputes values for missing detection limits for left-censored data. From smwrQW package.
dlimit(values, censor.codes, default = 1e-25)
the numeric values.
logical or character, TRUE
or "<" indicates
left-censored at value and FALSE
, " " or "" indicates a quantified
value. Character codes "E" and "J" are censored at the current detection
limit. Other characters are ignored and generate a warning.
the default detection limit value to assign to an uncensored value that is less than the currently imputed detection limit value.
A vector of detection limits matched with each of the input values.
Tim Cohn, written communication, 5 Nov 2002 states that there are several problems with this approach:
1) for large data sets, it may be difficult for the user to group the observations into subsets as in the example above. If the observations are not grouped into subsets or the subsets do not have the censored observations as the first records, incorrect detection limits may be applied to the uncensored data.
2) for the case of multiple constituents, it may be impossible to place the censored observation within each subset at the top of the subset. Consider constituents Y and Z, each analyzed at Lab B, and each with one censored observation. The censored observations occur on different dates. In this case its not possible to have both observations as the first line in the subset from Lab B. (Note: the user could work around this problem by doing seperate runs for each constituent.)
3) When a subset of observations is completely uncensored, the detection limit from another subset will be applied. Consider the example above, but with the 10 observations from Lab B being completely uncensored. In this case, the detection limit from Lab A will be used for all 20 observations.
4) When all of the observations are uncensored (no '<' signs for a given constituent), there is no way for the user to specify the detection limit(s). In this case, a default value of 1.E-25 is used for all observations (consistent with ESTIMATOR).
Despite these problems, the approach is satisfactory for most applications: "the estimates are not very sensitive to the precise value of the censoring threshold for the above-threshold values."
Runkel, R.L., Crawford, C.G., and Cohn, T.A., 2004, Load estimator (LOADEST) a FORTRAN program for estiamting constituent laods in streams and rivers: U.S. Gelogical Survey Techniques and Methods 4-A5, 69.
# NOT RUN {
## The actual detection limits are 2,2,2,1,1,1.
dlimit(c(2,2,3,1,1,2), c(" ", "<", " ", " ", "<", " "))
# }
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