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data summary and some automated t.tests.

Sun, May 17, 2009 10:36 AM

You might want to try using a non-parametric test, such as wilcox.test.

How about some modification of the following:

d=data.frame(grp=rep(1:2,e=5),replicate(10,rnorm(100))); head(d)
lapply(d[,-1],function(.column)wilcox.test(.column~grp,data=d))

David Freedman

stephen sefick wrote:

Up and down are the treatments.  These are replicates within date for
percent cover of habiat.  This is habitat data for a stream
restoration - up is the unrestored and dn is the restored.  I have
looked at the density plots and they do not look gaussian - you are
absolutely right.  Even log(n+1) transformed they do not look
Gaussian.  Is there some other way that I would test for a difference
that you can think of?  My thoughts were to run a Permutation t.test,
but I am very new to permutations, and don't know if this applies.
The other thing that I was thinking was to use a npmanova (adonis in
vegan) to test if the centroids of the habitat classifications were
different.  I am in the process of working up my thesis data for
publication in a journal (there are other very interesting pieces to
the data set that I am working with, and this is one of the last
things that I need to wrap up before I can start editing/rewriting my
masters work).  Any thoughts would be greatly appreciated.
thanks,

Stephen Sefick

2009/5/16 Uwe Ligges <ligges at statistik.tu-dortmund.de>:


stephen sefick wrote:

I would like to preform a t.test to each of the measured variables
(sand.silt etc.)

I am a big fan of applying t.test()s, but in this case: Are you really
sure?
The integers and particularly boxplot(x) do not indicate very well that
the
variables are somehow close to Gaussian ...

with a mean and sd for each of the treatments

And what is the treatment???

Best,
Uwe Ligges

(up or
down), and out put this as a table.... ?I am having a hard time
starting- maybe it is to close to lunch. ?Any suggestions would be
greatly appreciated.

Stephen Sefick

x <- (structure(list(sample. = structure(c(1L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 2L, 3L, 4L, 5L, 6L, 1L, 7L, 8L, 9L, 10L, 11L,
12L, 13L, 14L, 2L, 3L, 4L, 5L, 6L, 25L, 28L, 29L, 30L, 31L, 32L,
33L, 34L, 35L, 26L, 25L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L,
26L, 27L, 25L, 28L, 29L, 30L, 31L, 32L, 33L, 34L, 35L, 26L, 15L,
17L, 18L, 19L, 20L, 21L, 22L, 23L, 24L, 16L, 15L, 17L, 18L, 19L,
20L, 21L, 22L, 23L, 24L, 16L, 36L, 39L, 40L, 41L, 42L, 43L, 44L,
45L, 46L, 37L, 36L, 39L, 40L, 41L, 42L, 43L, 44L, 45L, 46L, 37L,
38L), .Label = c("0805-r1", "0805-r10", "0805-r11", "0805-r12",
"0805-r13", "0805-r14", "0805-r2", "0805-r3", "0805-r4", "0805-r5",
"0805-r6", "0805-r7", "0805-r8", "0805-r9", "0805-u1", "0805-u10",
"0805-u2", "0805-u3", "0805-u4", "0805-u5", "0805-u6", "0805-u7",
"0805-u8", "0805-u9", "1005-r1", "1005-r10", "1005-r11", "1005-r2",
"1005-r3", "1005-r4", "1005-r5", "1005-r6", "1005-r7", "1005-r8",
"1005-r9", "1005-u1", "1005-u10", "1005-u11", "1005-u2", "1005-u3",
"1005-u4", "1005-u5", "1005-u6", "1005-u7", "1005-u8", "1005-u9"
), class = "factor"), date = structure(c(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, 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, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L), .Label =
c("10/1/05",
"8/29/05"), class = "factor"), Replicate = c(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, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 3L, 3L, 3L,
3L, 3L, 3L, 3L, 3L, 3L, 3L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L,
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 1L, 1L, 1L, 1L, 1L,
1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L
), site = 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, 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), .Label = c("dn", "up"
), class = "factor"), sand.silt = c(20L, 45L, 90L, 21L, 80L,
77L, 30L, 80L, 36L, 9L, 62L, 71L, 20L, 65L, 10L, 70L, 50L, 80L,
90L, 97L, 94L, 82L, 30L, 10L, 65L, 80L, 90L, 70L, 10L, 50L, 60L,
40L, 10L, 45L, 10L, 10L, 15L, 10L, 8L, 35L, 10L, 40L, 10L, 10L,
28L, 5L, 45L, 35L, 2L, 10L, 40L, 2L, 70L, 40L, 20L, 30L, 50L,
60L, 10L, 100L, 98L, 98L, 90L, 87L, 87L, 40L, 97L, 92L, 70L,
50L, 81L, 35L, 70L, 89L, 28L, 28L, 82L, 81L, 33L, 80L, 40L, 40L,
60L, 30L, 5L, 50L, 70L, 75L, 85L, 95L, 93L, 80L, 80L, 60L, 82L,
60L, 5L, 70L, 80L, 40L), gravel = c(8L, 45L, 7L, 5L, 10L, 5L,
35L, 7L, 45L, 60L, 0L, 0L, 5L, 8L, 25L, 0L, 45L, 15L, 0L, 1L,
2L, 5L, 6L, 15L, 10L, 5L, 3L, 10L, 20L, 0L, 20L, 31L, 20L, 35L,
70L, 30L, 60L, 60L, 70L, 50L, 70L, 40L, 50L, 30L, 48L, 85L, 20L,
30L, 20L, 60L, 30L, 8L, 10L, 30L, 30L, 10L, 0L, 0L, 10L, 0L,
0L, 0L, 2L, 8L, 8L, 30L, 0L, 3L, 15L, 29L, 11L, 60L, 15L, 8L,
60L, 25L, 8L, 9L, 42L, 1L, 50L, 40L, 10L, 60L, 60L, 30L, 10L,
10L, 0L, 0L, 0L, 2L, 2L, 0L, 1L, 25L, 10L, 10L, 10L, 50L), cobble =
c(5L,
2L, 1L, 5L, 0L, 3L, 10L, 2L, 4L, 3L, 1L, 0L, 3L, 14L, 50L, 0L,
1L, 1L, 0L, 0L, 0L, 2L, 0L, 5L, 0L, 0L, 2L, 5L, 3L, 0L, 0L, 0L,
0L, 0L, 0L, 30L, 5L, 2L, 1L, 0L, 0L, 0L, 5L, 35L, 3L, 0L, 0L,
0L, 40L, 0L, 0L, 5L, 0L, 0L, 10L, 5L, 0L, 0L, 10L, 0L, 0L, 0L,
0L, 1L, 1L, 30L, 0L, 0L, 0L, 10L, 4L, 3L, 2L, 0L, 2L, 0L, 0L,
0L, 20L, 0L, 0L, 0L, 0L, 0L, 20L, 0L, 10L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 10L, 0L, 0L, 0L), boulder.bedrock = c(60L, 0L,
0L, 45L, 0L, 0L, 0L, 0L, 0L, 8L, 10L, 0L, 35L, 5L, 8L, 0L, 0L,
0L, 0L, 0L, 0L, 10L, 60L, 70L, 0L, 0L, 0L, 5L, 55L, 0L, 0L, 0L,
40L, 0L, 0L, 0L, 0L, 15L, 0L, 0L, 10L, 0L, 20L, 10L, 0L, 0L,
0L, 0L, 20L, 0L, 0L, 60L, 0L, 0L, 20L, 0L, 10L, 0L, 50L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 10L, 0L, 0L, 0L, 0L, 0L, 4L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 5L, 0L, 0L, 5L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 75L, 10L, 0L, 0L), fine.root = c(5L, 7L,
0L, 10L, 2L, 6L, 5L, 4L, 3L, 7L, 0L, 0L, 7L, 4L, 6L, 1L, 4L,
2L, 2L, 2L, 3L, 1L, 0L, 1L, 20L, 5L, 3L, 5L, 10L, 2L, 0L, 6L,
10L, 10L, 15L, 0L, 0L, 5L, 15L, 0L, 10L, 10L, 0L, 5L, 8L, 5L,
0L, 20L, 0L, 8L, 0L, 0L, 7L, 0L, 0L, 15L, 0L, 0L, 0L, 0L, 2L,
0L, 2L, 0L, 2L, 0L, 3L, 3L, 4L, 5L, 0L, 0L, 8L, 2L, 2L, 3L, 0L,
1L, 0L, 10L, 0L, 0L, 0L, 0L, 0L, 12L, 0L, 0L, 10L, 0L, 0L, 5L,
12L, 0L, 0L, 0L, 0L, 10L, 5L, 5L), course.root = c(0L, 0L, 0L,
0L, 0L, 0L, 0L, 3L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 8L, 0L, 0L, 0L, 0L, 0L, 0L, 10L, 0L, 0L, 0L,
0L, 0L, 0L, 20L, 0L, 0L, 10L, 20L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 4L, 0L, 0L, 0L, 1L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L,
5L, 0L, 0L, 0L, 0L, 0L, 5L, 0L, 0L, 0L, 5L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), wood = c(2L, 0L, 0L, 1L, 0L, 2L, 1L, 0L, 2L, 1L,
20L, 25L, 0L, 0L, 0L, 30L, 0L, 0L, 5L, 0L, 0L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 0L, 2L, 8L, 0L, 0L, 0L, 5L, 2L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 10L, 0L, 10L, 8L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 3L, 0L, 0L, 0L, 5L, 2L, 0L, 2L, 0L, 0L, 0L, 0L, 2L, 0L,
1L, 0L, 0L, 25L, 5L, 0L, 0L, 5L, 10L, 10L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 30L, 8L, 5L, 0L, 0L, 0L, 0L), leaf = c(0L,
0L, 0L, 0L, 0L, 0L, 3L, 0L, 2L, 2L, 1L, 2L, 1L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 2L, 0L, 0L, 1L, 0L, 0L, 5L, 5L, 2L, 10L,
4L, 0L, 0L, 3L, 10L, 5L, 2L, 10L, 0L, 0L, 0L, 0L, 5L, 0L, 10L,
5L, 0L, 0L, 10L, 5L, 3L, 1L, 0L, 10L, 0L, 5L, 3L, 0L, 0L, 0L,
0L, 0L, 0L, 2L, 0L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 1L, 10L, 5L,
3L, 0L, 5L, 0L, 5L, 10L, 10L, 10L, 10L, 10L, 5L, 5L, 3L, 5L,
3L, 3L, 8L, 10L, 3L, 0L, 0L, 5L, 5L), leaf.sand = c(0L, 0L, 0L,
0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 2L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 10L, 0L, 2L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 3L, 3L, 0L, 10L, 20L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
2L, 0L, 0L, 0L, 2L, 1L, 0L, 0L, 0L, 2L, 2L, 0L, 3L, 0L, 1L, 0L,
0L, 10L, 0L, 0L, 0L, 0L, 0L, 0L, 2L, 2L, 2L, 2L, 2L, 0L, 3L,
0L, 0L, 0L, 0L), veg = c(0L, 1L, 2L, 13L, 8L, 7L, 15L, 3L, 6L,
10L, 0L, 2L, 2L, 4L, 1L, 0L, 0L, 0L, 3L, 0L, 3L, 0L, 0L, 0L,
5L, 4L, 2L, 5L, 30L, 25L, 10L, 10L, 2L, 10L, 0L, 10L, 10L, 5L,
8L, 3L, 10L, 10L, 0L, 5L, 12L, 10L, 5L, 10L, 8L, 15L, 20L, 20L,
20L, 6L, 20L, 20L, 10L, 15L, 13L, 0L, 0L, 2L, 0L, 1L, 0L, 0L,
0L, 1L, 0L, 3L, 0L, 2L, 2L, 0L, 1L, 0L, 0L, 0L, 5L, 0L, 0L, 0L,
10L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L), pool = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
6L, 0L, 0L, 0L, 0L, 3L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 4L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L)), .Names = c("sample.",
"date", "Replicate", "site", "sand.silt", "gravel", "cobble",
"boulder.bedrock", "fine.root", "course.root", "wood", "leaf",
"leaf.sand", "veg", "pool"), class = "data.frame", row.names = c(NA,
-100L)))



-- 
Stephen Sefick

Let's not spend our time and resources thinking about things that are
so little or so large that all they really do for us is puff us up and
make us feel like gods.  We are mammals, and have not exhausted the
annoying little problems of being mammals.

								-K. Mullis

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Thread (4 messages)

stephen sefick data summary and some automated t.tests. May 15 Uwe Ligges data summary and some automated t.tests. May 16 stephen sefick data summary and some automated t.tests. May 16 David Freedman data summary and some automated t.tests. May 17