R help - Jun 2005 - function for cumulative occurrence of elements

Hello, 

I have a data set with 9700 records, and 7 parameters. 

The data were collected for a survey of forest communities.  Sample plots 
(1009) and species (139) are included in this data set. I need to determine 
how species are accumulated as new plots are considered. Basically, I want 
to develop a species area curve. 

I've included the first 20 records from the data set.  Point represents the 
plot id. The other parameters are parts of the information statistic H'. 

Using "Table", I can construct a data set that lists the occurrence of
a
species at any Point (it produces a binary 0/1 data table). From there it 
get confusing, regarding the most efficient approach to determining the 
addition of new and or repeated species occurrences. 

ptcount <-  table(sppoint.freq$species, sppoint.freq$Point) 

 From here I've played around with colSums to calculate the number of
species
at each Point.  The difficulty is determining if a species is new or 
repeated.  Also since there are 1009 points a function is needed to screen 
every Point. 

Two goals are of interest: 1) the species accumulation curve, and 2) an 
accumulation curve when random Points are considered. 

Any help would be greatly appreciated. 

Thank you
Steve Friedman 


 Point        species frequency point.list point.prop   log.prop 
point.hprime
1      7   American elm         7         27 0.25925926 -1.3499267    
0.3499810
2      7          apple         2         27 0.07407407 -2.6026897    
0.1927918
3      7   black cherry         8         27 0.29629630 -1.2163953    
0.3604134
4      7      black oak         1         27 0.03703704 -3.2958369    
0.1220680
5      7    chokecherry         1         27 0.03703704 -3.2958369    
0.1220680
6      7         oak sp         1         27 0.03703704 -3.2958369    
0.1220680
7      7 pignut hickory         1         27 0.03703704 -3.2958369    
0.1220680
8      7      red maple         1         27 0.03703704 -3.2958369    
0.1220680
9      7      white oak         5         27 0.18518519 -1.6863990    
0.3122961
10     9   black spruce         2         27 0.07407407 -2.6026897    
0.1927918
11     9    blue spruce         2         27 0.07407407 -2.6026897    
0.1927918
12     9        missing        12         27 0.44444444 -0.8109302    
0.3604134
13     9  Norway spruce         8         27 0.29629630 -1.2163953    
0.3604134
14     9   white spruce         3         27 0.11111111 -2.1972246    
0.2441361
15    12          apple         2         27 0.07407407 -2.6026897    
0.1927918
16    12   black cherry         1         27 0.03703704 -3.2958369    
0.1220680
17    12   black locust         1         27 0.03703704 -3.2958369    
0.1220680
18    12   black walnut         1         27 0.03703704 -3.2958369    
0.1220680
19    12          lilac         3         27 0.11111111 -2.1972246    
0.2441361
20    12        missing         2         27 0.07407407 -2.6026897    
0.1927918

I'm not entirely sure what you want, but is it "9 5 3" for this
data? (9
"new" species occur at the first point, 5 "new" at the
second, and 3
"new" at the third).  If this is right, then to get "accumulation
curve
when random Points are considered", you can probably just index rows of 
dt appropriately.

 > dd <- read.table("clipboard", header=T)
 > dd[,1:3]
    Point        species frequency
1      7   American_elm         7
2      7          apple         2
3      7   black_cherry         8
4      7      black_oak         1
5      7    chokecherry         1
6      7         oak_sp         1
7      7 pignut_hickory         1
8      7      red_maple         1
9      7      white_oak         5
10     9   black_spruce         2
11     9    blue_spruce         2
12     9        missing        12
13     9  Norway_spruce         8
14     9   white_spruce         3
15    12          apple         2
16    12   black_cherry         1
17    12   black_locust         1
18    12   black_walnut         1
19    12          lilac         3
20    12        missing         2
 > # dt: table of which species occur at which "Points"
 > dt <- table(dd$Point, dd$species)
 > # doc: for each species, the index of the "Point" where
 > # it first occurs
 > doc <- apply(dt, 2, function(x) which(x==1)[1])
 > doc
   American_elm          apple   black_cherry   black_locust      black_oak
              1              1              1              3              1
   black_spruce   black_walnut    blue_spruce    chokecherry          lilac
              2              3              2              1              3
        missing  Norway_spruce         oak_sp pignut_hickory      red_maple
              2              2              1              1              1
      white_oak   white_spruce
              1              2
 > table(doc)
doc
1 2 3
9 5 3
 >

hope this helps,

Tony Plate

Steven K Friedman wrote:
> Hello, 
> 
> I have a data set with 9700 records, and 7 parameters. 
> 
> The data were collected for a survey of forest communities.  Sample plots 
> (1009) and species (139) are included in this data set. I need to determine
> how species are accumulated as new plots are considered. Basically, I want 
> to develop a species area curve. 
> 
> I've included the first 20 records from the data set.  Point represents
the
> plot id. The other parameters are parts of the information statistic
H'.
> 
> Using "Table", I can construct a data set that lists the
occurrence of a
> species at any Point (it produces a binary 0/1 data table). From there it 
> get confusing, regarding the most efficient approach to determining the 
> addition of new and or repeated species occurrences. 
> 
> ptcount <-  table(sppoint.freq$species, sppoint.freq$Point) 
> 
>  From here I've played around with colSums to calculate the number of
species
> at each Point.  The difficulty is determining if a species is new or 
> repeated.  Also since there are 1009 points a function is needed to screen 
> every Point. 
> 
> Two goals are of interest: 1) the species accumulation curve, and 2) an 
> accumulation curve when random Points are considered. 
> 
> Any help would be greatly appreciated. 
> 
> Thank you
> Steve Friedman 
> 
> 
>  Point        species frequency point.list point.prop   log.prop 
> point.hprime
> 1      7   American elm         7         27 0.25925926 -1.3499267    
> 0.3499810
> 2      7          apple         2         27 0.07407407 -2.6026897    
> 0.1927918
> 3      7   black cherry         8         27 0.29629630 -1.2163953    
> 0.3604134
> 4      7      black oak         1         27 0.03703704 -3.2958369    
> 0.1220680
> 5      7    chokecherry         1         27 0.03703704 -3.2958369    
> 0.1220680
> 6      7         oak sp         1         27 0.03703704 -3.2958369    
> 0.1220680
> 7      7 pignut hickory         1         27 0.03703704 -3.2958369    
> 0.1220680
> 8      7      red maple         1         27 0.03703704 -3.2958369    
> 0.1220680
> 9      7      white oak         5         27 0.18518519 -1.6863990    
> 0.3122961
> 10     9   black spruce         2         27 0.07407407 -2.6026897    
> 0.1927918
> 11     9    blue spruce         2         27 0.07407407 -2.6026897    
> 0.1927918
> 12     9        missing        12         27 0.44444444 -0.8109302    
> 0.3604134
> 13     9  Norway spruce         8         27 0.29629630 -1.2163953    
> 0.3604134
> 14     9   white spruce         3         27 0.11111111 -2.1972246    
> 0.2441361
> 15    12          apple         2         27 0.07407407 -2.6026897    
> 0.1927918
> 16    12   black cherry         1         27 0.03703704 -3.2958369    
> 0.1220680
> 17    12   black locust         1         27 0.03703704 -3.2958369    
> 0.1220680
> 18    12   black walnut         1         27 0.03703704 -3.2958369    
> 0.1220680
> 19    12          lilac         3         27 0.11111111 -2.1972246    
> 0.2441361
> 20    12        missing         2         27 0.07407407 -2.6026897    
> 0.1927918
> 
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
>

Hi Steven

Are you aware of the package "vegan" for community ecology? There is a
function in this package called specaccum, which calculates species
accumulation curves for you. Various methods can be specified, including
"random".

I must admit I have not used this particular function (yet!) but it seems
like it could be useful to you.

Regards
Karen

-------
Karen Kotschy
Centre for Water in the Environment
University of the Witwatersrand
Johannesburg
South Africa


On Tue, 28 Jun 2005, Steven K Friedman wrote:
>
> Hello,
>
> I have a data set with 9700 records, and 7 parameters.
>
> The data were collected for a survey of forest communities.  Sample plots
> (1009) and species (139) are included in this data set. I need to determine
> how species are accumulated as new plots are considered. Basically, I want
> to develop a species area curve.
>
> I've included the first 20 records from the data set.  Point represents
the
> plot id. The other parameters are parts of the information statistic
H'.
>
> Using "Table", I can construct a data set that lists the
occurrence of a
> species at any Point (it produces a binary 0/1 data table). From there it
> get confusing, regarding the most efficient approach to determining the
> addition of new and or repeated species occurrences.
>
> ptcount <-  table(sppoint.freq$species, sppoint.freq$Point)
>
>  From here I've played around with colSums to calculate the number of
species
> at each Point.  The difficulty is determining if a species is new or
> repeated.  Also since there are 1009 points a function is needed to screen
> every Point.
>
> Two goals are of interest: 1) the species accumulation curve, and 2) an
> accumulation curve when random Points are considered.
>
> Any help would be greatly appreciated.
>
> Thank you
> Steve Friedman
>
>
>  Point        species frequency point.list point.prop   log.prop
> point.hprime
> 1      7   American elm         7         27 0.25925926 -1.3499267
> 0.3499810
> 2      7          apple         2         27 0.07407407 -2.6026897
> 0.1927918
> 3      7   black cherry         8         27 0.29629630 -1.2163953
> 0.3604134
> 4      7      black oak         1         27 0.03703704 -3.2958369
> 0.1220680
> 5      7    chokecherry         1         27 0.03703704 -3.2958369
> 0.1220680
> 6      7         oak sp         1         27 0.03703704 -3.2958369
> 0.1220680
> 7      7 pignut hickory         1         27 0.03703704 -3.2958369
> 0.1220680
> 8      7      red maple         1         27 0.03703704 -3.2958369
> 0.1220680
> 9      7      white oak         5         27 0.18518519 -1.6863990
> 0.3122961
> 10     9   black spruce         2         27 0.07407407 -2.6026897
> 0.1927918
> 11     9    blue spruce         2         27 0.07407407 -2.6026897
> 0.1927918
> 12     9        missing        12         27 0.44444444 -0.8109302
> 0.3604134
> 13     9  Norway spruce         8         27 0.29629630 -1.2163953
> 0.3604134
> 14     9   white spruce         3         27 0.11111111 -2.1972246
> 0.2441361
> 15    12          apple         2         27 0.07407407 -2.6026897
> 0.1927918
> 16    12   black cherry         1         27 0.03703704 -3.2958369
> 0.1220680
> 17    12   black locust         1         27 0.03703704 -3.2958369
> 0.1220680
> 18    12   black walnut         1         27 0.03703704 -3.2958369
> 0.1220680
> 19    12          lilac         3         27 0.11111111 -2.1972246
> 0.2441361
> 20    12        missing         2         27 0.07407407 -2.6026897
> 0.1927918
>
> ______________________________________________
> R-help at stat.math.ethz.ch mailing list
> https://stat.ethz.ch/mailman/listinfo/r-help
> PLEASE do read the posting guide!
http://www.R-project.org/posting-guide.html
>