Dear Kendra,
I am not sure I understand your question exactly. Maybe the R code you
already have would help.
Below is a sample of the data as returned by dput() so that anyone can
follow or extend this reply.
First, I would create a variable distinguishing all levels, for which
the mean is to be calculated:
env$site.horizon<- paste(env$site, env$horizon, sep= '.')
Now, aggregate(), as you write, may be used to calculate mean
abundances for every site/horizon category (level). Note that only
samples where om == 0 are used for the calculation:
Means<- aggregate(spe[env$om == 0,], by= list(env$site.horizon[env$om
== 0]), FUN= mean)
The following just makes rownames from the first column, so that one
may use them in a name subscript to select desired rows.
Means.dtf<- data.frame(Means, row.names=1)
This dataframe now contains the mean values for every species and every
site/horizon combination,
and may be extended by the name subscript to the dimensions of the
original species dataframe:
Means.extended<- Means.dtf[env$site.horizon,]
This is probably what you want to divide the species counts with:
spe.divided<- spe / Means.extended
NaN (0/0) and Inf (0/anything) values will emerge, these can be changed
to something meaningful:
spe.div[is.na(spe.div)]<- 0 # or anything you want
spe.div[spe.div==Inf]<- 0 # or anything you want
Hope that helps,
Vit
... and the data:
env<- structure(list(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
), .Label = "A7", class = "factor"), om = c(2L, 2L, 3L, 3L, 1L,
1L, 2L, 2L, 3L, 3L, 1L, 1L, 2L, 2L, 3L, 3L, 1L, 1L, 0L, 0L, 0L,
0L, 0L), horizon = c(1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L,
1L, 2L, 1L, 2L, 1L, 2L, 1L, 2L, 2L, 1L, 2L, 1L, 2L), site.horizon =
c("A7.1",
"A7.2", "A7.1", "A7.2", "A7.1", "A7.2", "A7.1", "A7.2", "A7.1",
"A7.2", "A7.1", "A7.2", "A7.1", "A7.2", "A7.1", "A7.2", "A7.1",
"A7.2", "A7.2", "A7.1", "A7.2", "A7.1", "A7.2")), .Names = c("site",
"om", "horizon", "site.horizon"), row.names = c("A7001", "A7002",
"A7003", "A7004", "A7007", "A7008", "A7009", "A7010", "A7011",
"A7012", "A7015", "A7016", "A7019", "A7020", "A7021", "A7022",
"A7023", "A7024", "A7026", "A7027", "A7028", "A7029", "A7030"
), class = "data.frame")
spe<- structure(list(Otu00209 = c(4L, 0L, 8L, 1L, 9L, 1L, 31L, 3L,
3L, 6L, 13L, 1L, 7L, 3L, 13L, 0L, 23L, 4L, 0L, 0L, 2L, 17L, 13L
), Otu00358 = c(95L, 8L, 156L, 34L, 9L, 8L, 9L, 1L, 1L, 1L, 4L,
0L, 0L, 2L, 3L, 8L, 3L, 10L, 0L, 0L, 1L, 8L, 0L), Otu00359 = c(1L,
0L, 0L, 0L, 6L, 0L, 0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 1L, 0L, 1L, 0L), Otu00388 = c(0L, 0L, 4L, 1L, 3L, 0L,
0L, 1L, 0L, 3L, 9L, 1L, 2L, 27L, 0L, 1L, 0L, 1L, 1L, 0L, 6L,
2L, 0L), Otu00389 = c(0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Otu00533 = c(7L,
0L, 3L, 0L, 21L, 3L, 0L, 1L, 6L, 13L, 0L, 1L, 2L, 2L, 1L, 0L,
2L, 2L, 5L, 0L, 0L, 2L, 1L), Otu00608 = c(1L, 1L, 3L, 4L, 0L,
0L, 9L, 1L, 2L, 0L, 0L, 32L, 13L, 1L, 58L, 3L, 27L, 6L, 0L, 0L,
0L, 3L, 0L), Otu00618 = c(0L, 0L, 0L, 91L, 0L, 10L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L), Otu00669 =
c(1L,
0L, 0L, 0L, 8L, 0L, 4L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L), Otu00695 = c(0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Otu00696 = c(4L, 0L, 3L, 1L, 1L, 0L, 1L, 2L, 0L, 2L, 3L,
0L, 2L, 0L, 0L, 0L, 1L, 3L, 1L, 7L, 1L, 19L, 0L), Otu00706 = c(0L,
0L, 0L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 1L, 0L, 0L, 0L, 0L, 0L, 0L,
0L, 0L, 0L, 0L, 2L, 0L), Otu00725 = c(0L, 0L, 0L, 3L, 1L, 0L,
0L, 0L, 0L, 0L, 1L, 0L, 2L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L,
0L), Otu00761 = c(0L, 4L, 0L, 1L, 0L, 1L, 0L, 0L, 0L, 0L, 1L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L)), .Names =
c("Otu00209",
"Otu00358", "Otu00359", "Otu00388", "Otu00389", "Otu00533", "Otu00608",
"Otu00618", "Otu00669", "Otu00695", "Otu00696", "Otu00706", "Otu00725",
"Otu00761"), class = "data.frame", row.names = c("A7001", "A7002",
"A7003", "A7004", "A7007", "A7008", "A7009", "A7010", "A7011",
"A7012", "A7015", "A7016", "A7019", "A7020", "A7021", "A7022",
"A7023", "A7024", "A7026", "A7027", "A7028", "A7029", "A7030"
))
Dne 2013-10-28 22:27, Mitchell, Kendra napsal:
▾ Quoted text (110 lines)
I have a large species matrix with counts that I want to express as
ratio to the reference plots. I want to use a heatmap to display
change in species as a ratio to the reference
the layout is samples by rows and species by columns, it's currently
a data.frame with row.name=sample name, header=species name, here's
part of the table for one site (I have 36 sites).
Otu00209 Otu00358 Otu00359 Otu00388
Otu00389 Otu00533 Otu00608 Otu00618
Otu00669 Otu00695 Otu00696 Otu00706
Otu00725 Otu00761
A7001 4 95 1 0 0 7 1 0
1 0 4 0 0 0
A7002 0 8 0 0 0 0 1 0
0 0 0 0 0 4
A7003 8 156 0 4 0 3 3 0
0 0 3 0 0 0
A7004 1 34 0 1 0 0 4 91
0 0 1 0 3 1
A7007 9 9 6 3 0 21 0 0
8 0 1 2 1 0
A7008 1 8 0 0 0 3 0 10
0 0 0 0 0 1
A7009 31 9 0 0 0 0 9 0
4 0 1 0 0 0
A7010 3 1 1 1 0 1 1 0
0 0 2 0 0 0
A7011 3 1 1 0 0 6 2 0
1 0 0 0 0 0
A7012 6 1 0 3 0 13 0 0
0 0 2 0 0 0
A7015 13 4 0 9 1 0 0 0
0 1 3 1 1 1
A7016 1 0 0 1 0 1 32 0
0 0 0 0 0 0
A7019 7 0 0 2 0 2 13 0
0 0 2 0 2 0
A7020 3 2 0 27 0 2 1 0
0 0 0 0 0 0
A7021 13 3 0 0 0 1 58 0
0 0 0 0 0 0
A7022 0 8 0 1 0 0 3 0
0 0 0 0 0 0
A7023 23 3 0 0 0 2 27 0
1 0 1 0 0 0
A7024 4 10 0 1 0 2 6 0
0 0 3 0 0 0
A7026 0 0 0 1 0 5 0 0
0 0 1 0 0 0
A7027 0 0 1 0 0 0 0 0
0 0 7 0 0 0
A7028 2 1 0 6 0 0 0 0
0 0 1 0 0 0
A7029 17 8 1 2 0 2 3 0
0 0 19 2 0 0
A7030 13 0 0 0 0 1 0 0
0 0 0 0 0 1
Environmental data is in a separate dataframe, here is one site as an
example:
fung_sample site om horizon
A7001 A7 2 1
A7002 A7 2 2
A7003 A7 3 1
A7004 A7 3 2
A7007 A7 1 1
A7008 A7 1 2
A7009 A7 2 1
A7010 A7 2 2
A7011 A7 3 1
A7012 A7 3 2
A7015 A7 1 1
A7016 A7 1 2
A7019 A7 2 1
A7020 A7 2 2
A7021 A7 3 1
A7022 A7 3 2
A7023 A7 1 1
A7024 A7 1 2
A7026 A7 0 2
A7027 A7 0 1
A7028 A7 0 2
A7029 A7 0 1
A7030 A7 0 2
So I need to accomplish a few of things,
1. average the species counts for each horizon for the reference
samples (om=0) ## I was able to do this by subsetting the reference
into their own matrix then using reshape to get the mean per
site/horizon, but don't know if a separate matrix is really what I
want
2. add a constant to each species to prevent errors from dividing
by zero
3. divide species counts in horizon 1 by the average reference for
horizon 1
I've looked at tapply which seems to be the direction that I need to
go, but I'm not getting how.
Thanks
Kendra
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Vit Syrovatka
Department of Botany and Zoology
Masaryk University
Kotlarska 2
CZ-611 37 Brno, Czech Republic
Tel.: +420-532146342
E-mail: syrovat at sci.muni.cz