Re: [R] data summary and some automated t.tests.

2009-05-17 Thread David Freedman 

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 lig...@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,

Re: [R] data summary and some automated t.tests.

2009-05-16 Thread Uwe Ligges 



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,

Re: [R] data summary and some automated t.tests.

2009-05-16 Thread stephen sefick 
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 lig...@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,

[R] data summary and some automated t.tests.

2009-05-15 Thread stephen sefick 
I would like to preform a t.test to each of the measured variables
(sand.silt etc.) with a mean and sd for each of the treatments (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,