The acomp() function works well on data sets with no missing data. For
example:
win.acomp <- acomp(win.cast, parts=2:6)
win.acomp
Filterer Gatherer Grazer Predator Shredder
[1,] 0.06670000 0.6000000 0.06670000 0.2444000 0.02220000
[2,] 0.06120612 0.5714571 0.06120612 0.2653265 0.04080408
[3,] 0.04350000 0.6232000 0.10140000 0.2174000 0.01450000
[4,] 0.04400000 0.5934000 0.06590000 0.2637000 0.03300000
[5,] 0.05000000 0.4333000 0.06670000 0.3667000 0.08330000
[6,] 0.01610000 0.6290000 0.03230000 0.2903000 0.03230000
attr(,"class")
[1] acomp
When the data set has one missing value, acomp() produces output but
changes the missing value from MAR (Missing At Random) to MNAR (Missing Not
At Random) when the missing variable is in quotes ...
burns.cast
sampdate Filterer Gatherer Grazer Predator Shredder
1 2000-07-18 0.0550 0.5596 0.0734 0.2294 0.0826
2 2003-07-08 0.0734 0.6147 0.0183 0.2294 0.0642
3 2005-07-13 0.1161 0.5714 0.0357 0.1696 0.1071
4 2006-06-28 0.1000 0.4667 0.1500 0.1333 0.1500
5 2010-09-14 0.0778 0.6111 0.0444 0.1889 0.0778
6 2011-07-13 0.0879 0.5714 0.0659 0.2747 NA
7 2012-07-11 0.1042 0.5313 0.0625 0.2396 0.0625
8 2013-07-11 0.0723 0.5542 0.0602 0.2651 0.0482
burns.acomp <- acomp(burns.cast, parts=2:6, MAR='NA')
burns.acomp
Filterer Gatherer Grazer Predator Shredder
[1,] 0.05500000 0.5596000 0.07340000 0.2294000 0.08260000
[2,] 0.07340000 0.6147000 0.01830000 0.2294000 0.06420000
[3,] 0.11611161 0.5714571 0.03570357 0.1696170 0.10711071
[4,] 0.10000000 0.4667000 0.15000000 0.1333000 0.15000000
[5,] 0.07780000 0.6111000 0.04440000 0.1889000 0.07780000
[6,] 0.08790879 0.5714571 0.06590659 0.2747275 MNAR
[7,] 0.10418958 0.5312469 0.06249375 0.2395760 0.06249375
[8,] 0.07230000 0.5542000 0.06020000 0.2651000 0.04820000
attr(,"class")
[1] acomp
... or BDL (Below Detection Limits) when the missing variable is without
quotes:
burns.acomp <- acomp(burns.cast, parts=2:6, MAR=NA)
burns.acomp
Filterer Gatherer Grazer Predator Shredder
[1,] 0.05500000 0.5596000 0.07340000 0.2294000 0.08260000
[2,] 0.07340000 0.6147000 0.01830000 0.2294000 0.06420000
[3,] 0.11611161 0.5714571 0.03570357 0.1696170 0.10711071
[4,] 0.10000000 0.4667000 0.15000000 0.1333000 0.15000000
[5,] 0.07780000 0.6111000 0.04440000 0.1889000 0.07780000
[6,] 0.08790879 0.5714571 0.06590659 0.2747275 BDL
[7,] 0.10418958 0.5312469 0.06249375 0.2395760 0.06249375
[8,] 0.07230000 0.5542000 0.06020000 0.2651000 0.04820000
attr(,"class")
[1] acomp
The dput() output for burns.cast is attached.
How should the acomp() command be formated when there are values missing
at random?
Rich
structure(list(sampdate = structure(c(11156, 12241, 12977, 13327,
14866, 15168, 15532, 15897), class = "Date"), Filterer = c(0.055,
0.0734, 0.1161, 0.1, 0.0778, 0.0879, 0.1042, 0.0723), Gatherer = c(0.5596,
0.6147, 0.5714, 0.4667, 0.6111, 0.5714, 0.5313, 0.5542), Grazer = c(0.0734,
0.0183, 0.0357, 0.15, 0.0444, 0.0659, 0.0625, 0.0602), Predator = c(0.2294,
0.2294, 0.1696, 0.1333, 0.1889, 0.2747, 0.2396, 0.2651), Shredder = c(0.0826,
0.0642, 0.1071, 0.15, 0.0778, NA, 0.0625, 0.0482)), .Names = c("sampdate",
"Filterer", "Gatherer", "Grazer", "Predator", "Shredder"), row.names = c(NA,
-8L), class = "data.frame")
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
