Performs a parametric test of whether the mean difference between two columns of paired censored data equals 0. Assumes that the paired differences follow a gaussian (normal) distribution.

`cen_paired(xd, xc, yd, yc, alternative = "two.sided", printstat = TRUE)`

- xd
The first column of data values plus detection limits

- xc
The column of censoring indicators, where 1 (or

`TRUE`

) indicates a detection limit in the xd column, and 0 (or`FALSE`

) indicates a detected value in xd.- yd
The second column of data values plus detection limits, or a single number representing a standard / guideline value.

- yc
The column of censoring indicators for yd, where 1 (or

`TRUE`

) indicates a detection limit in the yd column, and 0 (or`FALSE`

) indicates a detected value in`yd`

. Not needed if`yd`

is a single standard number.- alternative
The usual notation for the alternate hypothesis. Default is

`“two.sided”`

. Options are`“greater”`

or`“less”`

.- printstat
Logical

`TRUE`

/`FALSE`

option of whether to print the resulting statistics in the console window, or not. Default is`TRUE.`

A list of statistics containing the following components:

`n`

Number of observations`Z`

The value of the test statistic`p.value`

the p-value of the test`Mean difference`

the mean difference between`xd`

and`yd`

You may also test for whether the mean of the `xd`

data exceeds a standard by entering the single number for the standard as `yd`

. In that case no `yc`

is required.

Helsel, D.R., 2011. Statistics for Censored Environmental Data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.

```
data(atrazine)
cen_paired(atrazine$June,atrazine$JuneCen,atrazine$Sept,atrazine$SeptCen)
#> Censored paired test for mean(atrazine$June - atrazine$Sept) equals 0.
#> alternative hypothesis: true mean difference does not equal 0.
#>
#> n = 24 Z= -1.0924 p-value = 0.2747
#> Mean difference = -3.927
# Comparing standard/guieline value
cen_paired(atrazine$June, atrazine$JuneCen, 0.01, alternative = "greater")
#> Censored paired test for mean(atrazine$June) equals 0.01
#> alternative hypothesis: true mean atrazine$June exceeds 0.01.
#>
#> n = 24 Z= 2.1004 p-value = 0.01785
#> Mean atrazine$June = 0.04231
```