The multcomp package has a nice compact letter display function built in, but I'm using the non-parametric multiple comparison package "nparcomp" which does not appear to have a similar feature. I've noticed there are a couple packages such as multcompView and rcompanion that have CLD functions, but I'm not sure how to get my nparcomp summary to cooperate with those tools. Maybe someone here can help me? Here's an example summary of an nparcomp Tukey test:
library(nparcomp)
pristineraw.tukey <- mctp(positif.prop.total ~ dose.log, data = pristineraw, type = "Tukey", conf.level = 0.95, asy.method = "fisher", info = FALSE)
pristineraw.tukey
$Data.Info
Sample Size Effect Lower Upper
1 -4 4 0.7812500 0.65095081 0.8724403
2 -2.95860731484178 2 0.8229167 0.68706660 0.9077133
3 -1.99567862621736 4 0.6145833 0.49050216 0.7253656
4 -0.999565922520681 4 0.4166667 0.33069961 0.5080188
5 4.34272768626649e-05 4 0.1562500 0.08581288 0.2675807
6 1.0000043429231 2 0.2083333 0.12491776 0.3266579
$Contrast
1 2 3 4 5 6
2 - 1 -1 1 0 0 0 0
3 - 1 -1 0 1 0 0 0
4 - 1 -1 0 0 1 0 0
5 - 1 -1 0 0 0 1 0
6 - 1 -1 0 0 0 0 1
3 - 2 0 -1 1 0 0 0
4 - 2 0 -1 0 1 0 0
5 - 2 0 -1 0 0 1 0
6 - 2 0 -1 0 0 0 1
4 - 3 0 0 -1 1 0 0
5 - 3 0 0 -1 0 1 0
6 - 3 0 0 -1 0 0 1
5 - 4 0 0 0 -1 1 0
6 - 4 0 0 0 -1 0 1
6 - 5 0 0 0 0 -1 1
$Analysis
Estimator Lower Upper Statistic p.Value
2 - 1 0.042 -0.431 0.496 0.343 0.99761714
3 - 1 -0.167 -0.586 0.323 -1.381 0.69088411
4 - 1 -0.365 -0.648 0.007 -4.062 0.05318202
5 - 1 -0.625 -0.867 -0.144 -5.151 0.02076608
6 - 1 -0.573 -0.838 -0.090 -4.801 0.02785983
3 - 2 -0.208 -0.609 0.277 -1.763 0.50620162
4 - 2 -0.406 -0.688 -0.019 -4.320 0.04250191
5 - 2 -0.667 -0.894 -0.164 -5.205 0.02026988
6 - 2 -0.615 -0.866 -0.115 -4.930 0.02523067
4 - 3 -0.198 -0.583 0.260 -1.775 0.50151321
5 - 3 -0.458 -0.746 -0.026 -4.365 0.04027067
6 - 3 -0.406 -0.712 0.028 -3.880 0.06346250
5 - 4 -0.260 -0.561 0.101 -2.997 0.14893258
6 - 4 -0.208 -0.559 0.206 -2.078 0.37679610
6 - 5 0.052 -0.380 0.466 0.476 0.99043710
$Analysis.Inf
Estimator Lower Upper Statistic p.Value
2 - 1 0.04166667 -0.4310816 0.496466660 0.3426000 0.99761714
3 - 1 -0.16666667 -0.5861671 0.323305915 -1.3807046 0.69088411
4 - 1 -0.36458333 -0.6475061 0.006668684 -4.0618961 0.05318202
5 - 1 -0.62500000 -0.8671346 -0.143918870 -5.1509655 0.02076608
6 - 1 -0.57291667 -0.8375693 -0.090485809 -4.8010534 0.02785983
3 - 2 -0.20833333 -0.6088821 0.276867328 -1.7626807 0.50620162
4 - 2 -0.40625000 -0.6877026 -0.018637527 -4.3195377 0.04250191
5 - 2 -0.66666667 -0.8944430 -0.164222955 -5.2046137 0.02026988
6 - 2 -0.61458333 -0.8659606 -0.115293472 -4.9298694 0.02523067
4 - 3 -0.19791667 -0.5834144 0.260362074 -1.7746828 0.50151321
5 - 3 -0.45833333 -0.7460603 -0.026382368 -4.3654031 0.04027067
6 - 3 -0.40625000 -0.7115636 0.028113118 -3.8797113 0.06346250
5 - 4 -0.26041667 -0.5608547 0.100626889 -2.9973930 0.14893258
6 - 4 -0.20833333 -0.5594223 0.206138515 -2.0776563 0.37679610
6 - 5 0.05208333 -0.3804685 0.465937204 0.4758687 0.99043710
$Overall
Quantile p.Value
1 4.132777 0.02026988
$input
$input$formula
positif.prop.total ~ dose.log
$input$data
dose positif negatif dead totalNb positif.prop.total dose.log
1 0e+00 17 20 0 37 0.45945946 -4.000000e+00
2 0e+00 23 16 0 39 0.58974359 -4.000000e+00
3 0e+00 18 15 0 33 0.54545455 -4.000000e+00
4 0e+00 14 14 1 28 0.50000000 -4.000000e+00
5 1e-03 19 19 1 38 0.50000000 -2.958607e+00
6 1e-03 20 14 4 34 0.58823529 -2.958607e+00
7 1e-02 22 16 0 38 0.57894737 -1.995679e+00
8 1e-02 18 19 0 37 0.48648649 -1.995679e+00
9 1e-02 15 22 2 37 0.40540541 -1.995679e+00
10 1e-02 11 20 4 31 0.35483871 -1.995679e+00
11 1e-01 12 20 0 32 0.37500000 -9.995659e-01
12 1e-01 12 17 4 29 0.41379310 -9.995659e-01
13 1e-01 8 26 3 34 0.23529412 -9.995659e-01
14 1e-01 5 18 11 23 0.21739130 -9.995659e-01
15 1e+00 3 16 10 19 0.15789474 4.342728e-05
16 1e+00 1 16 5 17 0.05882353 4.342728e-05
17 1e+00 2 24 9 26 0.07692308 4.342728e-05
18 1e+00 7 23 6 30 0.23333333 4.342728e-05
19 1e+01 3 10 8 13 0.23076923 1.000004e+00
20 1e+01 2 20 8 22 0.09090909 1.000004e+00
$input$type
[1] "Tukey"
$input$conf.level
[1] 0.95
$input$alternative
[1] "two.sided"
$input$asy.method
[1] "fisher"
$input$plot.simci
[1] FALSE
$input$control
NULL
$input$info
[1] FALSE
$input$rounds
[1] 3
$input$contrast.matrix
NULL
$input$correlation
[1] FALSE
$input$effect
[1] "unweighted"
$input$const
[1] 0.5875441
$text.Output
[1] "True differences of relative effects are not equal to 0"
$text.output.W
[1] "Global Pseudo Ranks"
$connames
[1] "2 - 1" "3 - 1" "4 - 1" "5 - 1" "6 - 1" "3 - 2" "4 - 2" "5 - 2" "6 - 2"
[10] "4 - 3" "5 - 3" "6 - 3" "5 - 4" "6 - 4" "6 - 5"
$AsyMethod
[1] "Fisher with 5 DF"
attr(,"class")
[1] "mctp"