qwCoalesce: Replace missing values

In USGS-R/smwrQW: Tools for censored data analysis

Replace missing values

Description

Constructs a vector with as few missing values as possible from a selected sequence of vectors.

Usage

qwCoalesce(..., means = FALSE, lt.tol = 0.1)

Arguments

...	any number of objects of class "qw." Missing values are permitted.
means	logical, if TRUE, then compute the mean of all nonmissing values in the data sepcified in .... if FALSE, then select the first nonmissing value the sequence spcified in ....
lt.tol	an arbitrary tolerance metric to drop interval-censoring. See mean.qw for details.

Value

For means set to FALSE, an object of class "qw" in which each element is determined by selecting the first non-missing value in the order in which they are specified in the argument list. The first step is to construct a matrix from all arguments. The output is initially set to column 1; for any missing values in the column, the data from column 2 are used and so on until all columns have been searched or all missing values replaced. The metadata are set from the corresponding column.

For means set to FALSE, an object of class "qw" computed from the mean of the nonmissing values in each row of the data specified in .... The metadata, except for reporting limit information, are set from the first entry in ....

Note

This function is most useful for creating a column in a dataset from related columns that represent different methods. For example, a single column of alkalinity may be desired when there are multiple columns of alkalinity determined by various methods.

USGS-R/smwrQW documentation built on Oct. 11, 2022, 6:13 a.m.