[R] bootstrap confidence intervals, non iid

[Kay Cichini]

hello,

i need to calculate ci's for each of 4 groups within a dataset, to be able
to infere about differences in the variable "similarity". the problem is
that data within groups is dependent, as assigned by the blocking-factor
"site". my guess was to use a block bootstrap but samples within in these
blocks / sites are not of same length. i was not able to find a method to
deal with this -
 
i'd really appreciate any advise with this!

thanks,
kay

my data:
##########################################################
similarity<-data.frame(list(structure(list(stage = structure(c(1L, 1L, 1L,
1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 
3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 4L, 4L, 4L, 
4L, 4L), .Label = c("A", "B", "C", "D"), class = "factor"), site =
structure(c(1L, 
1L, 1L, 1L, 2L, 2L, 2L, 3L, 3L, 3L, 4L, 4L, 4L, 4L, 5L, 6L, 6L, 
6L, 6L, 7L, 7L, 7L, 8L, 8L, 8L, 8L, 9L, 9L, 9L, 9L, 10L, 10L, 
10L, 10L, 11L, 11L, 12L, 12L, 12L, 13L, 13L, 13L, 14L, 14L, 14L, 
14L, 15L, 15L, 15L, 15L, 16L, 16L, 16L, 17L, 17L, 17L, 17L, 18L, 
18L, 19L, 19L, 19L, 19L, 20L, 20L, 20L, 20L, 21L, 21L, 21L, 21L, 
22L, 22L, 22L, 22L, 23L, 23L, 23L, 24L, 24L, 24L, 24L, 25L, 25L, 
25L, 25L, 26L, 26L, 26L, 26L, 27L, 27L, 27L, 27L, 28L, 28L, 28L, 
28L, 29L, 29L, 29L, 30L, 30L, 30L, 30L, 31L, 31L, 32L, 32L, 32L, 
32L, 33L, 33L, 33L, 33L, 34L, 34L, 34L, 34L, 35L, 35L, 35L, 35L, 
36L, 36L, 36L, 36L, 37L, 37L, 38L, 38L, 38L, 38L, 39L, 39L, 39L
), .Label = c("A11", "A12", "A14", "A15", "A16", "A17", "A18", 
"A19", "A20", "A5", "A7", "A8", "B1", "B12", "B13", "B14", "B15", 
"B17", "B18", "B2", "B4", "B7", "B8", "B9", "C1", "C10", "C11", 
"C15", "C17", "C18", "C19", "C2", "C20", "C3", "C4", "C6", "D1", 
"D4", "D7"), class = "factor"), MH.Index = c(0.392156863, 0.602434077, 
0.576923077, 0.647482014, 0.989010989, 0.857142857, 1, 1, 1, 
0, 1, 0.378378378, 0.839087948, 0.252915554, 1, 0.22556391, 0.510366826, 
0.476190476, 0.555819477, 0.961538462, 0.666666667, 0.089285714, 
0.923076923, 0.571428571, 0, 0.923076923, 0.617647059, 0.599423631, 
0, 0.727272727, 0.998112812, 0, 0, 0, 1, 0.565656566, 0.75, 0.923076923, 
0.654545455, 0.14084507, 0.617647059, 0.315789474, 0.179347826, 
0.583468021, 0.165525114, 0.817438692, 0.455551457, 0.49548886, 
0.556127703, 0.707431246, 0.506757551, 0.689655172, 0.241433511, 
0.379232506, 0.241935484, 0, 0.30848329, 0.530973451, 0.148148148, 
0, 0.976744186, 0.550218341, 0.542168675, 0.769230769, 0.153310105, 
0, 0, 0.380569406, 0.742174733, 0.222222222, 0.046925432, 0, 
0.068076328, 0.772727273, 0.830039526, 0.503458415, 0.863910822, 
0.39401263, 0.081818182, 0.368421053, 0.088607595, 0, 0.575499851, 
0.605657238, 0.714854232, 0.855881172, 0.815689401, 0.552207228, 
0.81708081, 0.583228133, 0.334466349, 0.259477365, 0.194711538, 
0.278916707, 0.636304805, 0.593715432, 0.661016949, 0.626865672, 
0.420219245, 0.453535143, 0.471243706, 0.462427746, 0.56980057, 
0.453821155, 0.052828527, 0.926829268, 0.51988266, 0.472200264, 
0.351219512, 0.290030211, 0.765258974, 0.564894108, 0.789699571, 
0.863378215, 0.525181559, 0.803061458, 0.260164645, 0.477265792, 
0.265889379, 0.317791411, 0.107623318, 0.279181709, 0.471953363, 
0.463724265, 0.241966696, 0.403647213, 0.693087992, 0.494259925, 
0.68904453, 0.39329147, 0.498161213, 0.376225983, 0.407001046, 
0.825016633, 0.718991658, 0.662995912)), .Names = c("stage", 
"site", "MH.Index"), class = "data.frame", row.names = c(NA, 
-136L))))
##########################################################
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