Hi David,
thank yuou so much for helping me!
Il giorno 01/mag/2013, alle ore 10:16, David Carlson <dcarl...@tamu.edu> ha
scritto:
> You need to clarify what you are trying to achieve and fix some errors in
> your code. First, thanks for giving us reproducible data.
>
i tried to fix the errors and uploaded a new link to data and code [1]
Thanks for your advice!
i'll try to describe the dataset :
in the csv are stored information recorded by an underwater towed camera
[imagename, temp, sal, depth_m]
plus 3 fields added later by an image analyst
[idcode, count, subs]
so each ROW in the data is composed by
- idcode (unique identifier for specie)
- count (how many individuals of species 'J' are found in image 'X' )
- temp (temperature)
- sal (salinity)
- depth_m (depth in meters)
- subs (substrate complexity, integer number describing the seafloor texture
[hard <-> soft bottom] )
The csv looks like :
idcode count temp sal depth_m subs
16001 136 4.308 32.828 63.46 47
..
10010 1 4.342 32.865 83.58 35
> Once you have read the file, you seem to be attempting to remove cases with
> missing values, but you check for missing values of "count" twice and you
> never check "depth." The whole line can be replaced with
>
> dd <- na.omit(mat)
my mistake sorry about that.
fixed in the code
>
> Now you have data with complete cases. In your next step you create a
> distance matrix that includes "idcode" as a variable! Although it is
> numeric, it is really a categorical variable. That suggests you need to read
> up on R and cluster analysis. It is very likely that you want to exclude
> this variable from the distance matrix and possibly the "count" variable as
> well.
big mistake here, idcode is my "categorical value"
the one i'm trying in grouping into classes
fixed in the code, i now running the code including the count [ dd1 ]
and without including count [ dd2 ]
the count should express the "density for each species" with particular
environmental parameters associated (i think it was important, it isn't?)
>
> What does one row of data represent? You have 8036 complete cases
> representing data on 100 species. There are great differences in the number
> of rows for each species (idcode) ranging from 1 to 1066.
- trying to clem up the dataset
should i remove the records for the idcode that are not well represented
(IDcode with a low number of records)
so to have a subset of representative species ?
- idcodelist = [id_1,
, id_N]
with count(id_i) >= X
note :
in the data each record refer to a single species identified in an image,
this means that there are multiple records for the same image (one record for
each species identified in a single image)
in the database i have an unique [imagename] and position [lon lat] for each
image, should i include this information in my csv ?
so that it looks like :
idcode count temp sal depth_m subs lon lat
imagename
16001 136 4.308 32.828 63.46 47 x1 y1
image_year_day_h_m_ms_1
18005 15 4.308 32.828 63.46 47 x1 y1
image_year_day_h_m_ms_1
..
10010 5 4.342 31.925 82.18 35 xN yN
image_year_day_h_m_ms_N
13010 1 4.342 31.925 82.18 35 xN yN
image_year_day_h_m_ms_N
and group my data by [imagename] adding a field for each representative species
where to store the relative count ?
the example below should look like :
count_id_1 count_id_2
count_id_5
count_id_9
idcode_N-1 idcode_N temp
sal depth_m subs lon lat imagename
136 0 15 0
0 0 4.308 32.828 63.46
47 x1 y1 image_year_day_h_m_ms_1
..
0 5 0 0
1 0 4.342 31.925 82.18
35 xN yN image_year_day_h_m_ms_N
where :
count_id_1 is the count for the species with idcode 16001 in the image Xi
count_id_5 //
16005 //
count_id_2 //
10010 //
count_id_N-1 //
13010 //
thank you for any further advice,
Massimo.
[1] http://nbviewer.ipython.org/5497996
>
> -------------------------------------
> David L Carlson
> Associate Professor of Anthropology
> Texas A&M University
> College Station, TX 77840-4352
>
> -----Original Message-----
> From: r-help-boun...@r-project.org [mailto:r-help-boun...@r-project.org] On
> Behalf Of epi
> Sent: Tuesday, April 30, 2013 8:06 PM
> To: r-help@r-project.org
> Subject: [R] help understanding hierarchical clustering
>
> Hi All,
>
> i've problem to understand how to work with R to generate a hierarchical
> clustering my data are in a csv and looks like :
>
> idcode,count,temp,sal,depth_m,subs
> 16001,136,4.308,32.828,63.46,47
> 16001,109,4.31,32.829,63.09,49
> 16001,107,4.302,32.822,62.54,47
> 16001,87,4.318,32.834,62.54,48
> 16002,82,4.312,32.832,63.28,49
> 16002,77,4.325,32.828,65.65,46
> 16002,77,4.302,32.821,62.36,47
> 16002,71,4.299,32.832,65.84,37
> 16002,70,4.302,32.821,62.54,49
>
> where idcode is a specie identification number and the other fields are
> environmental parameters.
>
> library(vegan)
> mat<-read.csv("http://epi.whoi.edu/ipython/results/mdistefano/pg_site1.csv";,
> header=T)
> dd <- mat[!is.na(mat$idcode) &
> !is.na(mat$temp) &
> !is.na(mat$sal) &
> !is.na(mat$count) &
> !is.na(mat$count) &
> !is.na(mat$subs),]
> distmat<-vegdist(dd)
> clusa<-hclust(distmat,"average")
> print(clusa)
> Call:
> hclust(d = distmat, method = "average")
>
> Cluster method : average
> Distance : bray
> Number of objects: 8036
> print(dend1 <- as.dendrogram(clusa))
> 'dendrogram' with 2 branches and 8036 members total, at height
> 0.3194225
> dend2 <- cut(dend1, h=0.07)
>
>
> a complete run with plots is available here :
>
> http://nbviewer.ipython.org/5492912
>
> i'm trying try to group together the species (idcode's) that are sharing
> similar environmental parameters
>
> like (looking at the plots) i should be able to retrieve the list of idcode
> for each branch at "cut-level" X
>
> in the example :
>
>
> X = 0.07
>
> branches1 : [idcodeA, .. .. ,idcodeJ]
> ..
> ..
> branche6 : [idcodeB, .. .. , idcodeK]
>
>
>
> Many thanks for your precious help!!!
>
> Massimo.
>
>
>
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>
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>
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