Computes a distribution-free confidence interval for a difference of population medians in a paired-samples design. This function also computes the standard error of each median and the covariance between the two estimated medians.
ci.median.ps(alpha, y1, y2)alpha level for 1-alpha confidence
vector of scores for measurement 1
vector of scores for measurement 2 (paired with y1)
Returns a 1-row matrix. The columns are:
Median1 - estimated median for measurement 1
Median2 - estimated median for measurement 2
Median1-Median2 - estimated difference of medians
SE1 - standard error of median 1
SE2 - standard error of median 2
COV - covariance of the two estimated medians
LL - lower limit of the confidence interval
UL - upper limit of the confidence interval
Bonett DG, Price RM (2020). “Interval estimation for linear functions of medians in within-subjects and mixed designs.” British Journal of Mathematical and Statistical Psychology, 73(2), 333--346. ISSN 0007-1102, doi:10.1111/bmsp.12171 .
y1 <- c(21, 4, 9, 12, 35, 18, 10, 22, 24, 1, 6, 8, 13, 16, 19)
y2 <- c(67, 28, 30, 28, 52, 40, 25, 37, 44, 10, 14, 20, 28, 40, 51)
ci.median.ps(.05, y1, y2)
#> Median1 Median2 Median1-Median2 SE LL UL SE1 SE2
#> 13 30 -17 3.362289 -23.58996 -10.41004 3.085608 4.509735
#> COV
#> 9.276849
# Should return:
# Median1 Median2 Median1-Median2 SE LL UL
# 13 30 -17 3.362289 -23.58996 -10.41004
# SE1 SE2 COV
# 3.085608 4.509735 9.276849