Test of a monotonic trend in means for an ordered between-subjects factor

Source: R/statpsych1.R

Computes simultaneous confidence intervals for all adjacent pairwise comparisons of population means using estimated group means, estimated group standard deviations, and samples sizes as input. Equal variances are not assumed. A Satterthwaite adjustment to the degrees of freedom is used to improve the accuracy of the confidence intervals. If one or more lower limits are greater than 0 and no upper limit is less than 0, then conclude that the population means are monotonic decreasing. If one or more upper limits are less than 0 and no lower limits are greater than 0, then conclude that the population means are monotonic increasing. Reject the hypothesis of a monotonic trend if any lower limit is greater than 0 and any upper limit is less than 0.

test.mono.mean.bs(alpha, m, sd, n)

Arguments

alpha

alpha level for simultaneous 1-alpha confidence

m

vector of estimated group means

sd

vector of estimated group standard deviations

n

vector of sample sizes

Value

Returns a matrix with the number of rows equal to the number of adjacent pairwise comparisons. The columns are:

  • Estimate - estimated mean difference

  • SE - standard error

  • LL - one-sided lower limit of the confidence interval

  • UL - one-sided upper limit of the confidence interval

Examples

m <- c(12.86, 24.57, 36.29, 53.21)
sd <- c(13.185, 12.995, 14.773, 15.145)
n <- c(20, 20, 20, 20)
test.mono.mean.bs(.05, m, sd, n)
#>      Estimate       SE        LL         UL
#>  1 2   -11.71 4.139530 -22.07803 -1.3419744
#>  2 3   -11.72 4.399497 -22.74731 -0.6926939
#>  3 4   -16.92 4.730817 -28.76921 -5.0707936

# Should return:
#     Estimate       SE        LL         UL
# 1 2   -11.71 4.139530 -22.07803 -1.3419744
# 2 3   -11.72 4.399497 -22.74731 -0.6926939
# 3 4   -16.92 4.730817 -28.76921 -5.0707936