Performs a nonparametric Wilcoxon signed-rank test of whether the median difference between two columns of paired censored data equals 0. Uses the Pratt adjustment for pairs of equal or indistinguishable values.
cen_signedranktest(xd, xc, yd, yc, alternative = "two.sided", printstat = TRUE)
The first column of data values plus detection limits
The column of censoring indicators for xd
, where 1 (or TRUE
) indicates a detection limit in the xd column, and 0 (or FALSE
) indicates a detected value in xd.
The second column of data values plus detection limits, or a single number representing a standard / guideline value.
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.
The usual notation for the alternate hypothesis. Default is “two.sided”
. Options are “greater”
or “less”
.
Logical TRUE
/FALSE
option of whether to print the resulting statistics in the console window, or not. Default is TRUE.
Prints a list of Wilcoxon Signed-Rank test with Pratt correction for ties statistics containing the following components:
n
Number of samples
Z
The value of the test statistic
p.value
the p-value of the test
Helsel, D.R., 2011. Statistics for censored environmental data using Minitab and R, 2nd ed. John Wiley & Sons, USA, N.J.
Page, E.B., 1963. Ordered Hypotheses for Multiple Treatments: A Significance Test for Linear Ranks. Journal of the American Statistical Association 58, 216–230. doi: 10.2307/2282965
Pratt, J.W., 1959. Remarks on Zeros and Ties in the Wilcoxon Signed Rank Procedures. Journal of the American Statistical Association 54, 655–667. doi: 10.2307/2282543
data(atrazine)
cen_signedranktest(atrazine$June,atrazine$JuneCen,atrazine$Sept,atrazine$SeptCen)
#> Censored signed-rank test for (x:atrazine$June - y:atrazine$Sept) equals 0
#> alternative hypothesis: true difference atrazine$June - atrazine$Sept does not equal 0
#>
#> Pratt correction for ties
#> n = 24, Z = -3.319, p.value = 0.0009033