Dear List,
I have been trying to extract associations from a matrix whereby individual
locations are within a certain distance threshold from one another.
I have been able to extract those individuals where there is 'no interaction'
(i.e. where these individuals are not within a specified distance threshold
from another individual) and give these individuals a unique Group ID
containing that one individual.
i.e.
ID Group
1 ID1 1
2 ID3 2
3 ID4 3
4 ID5 4
5 ID7 5
6 ID8 6
7 ID9 7
What I need assistance with is allocating associations with a unique group id.
i.e. If we have interactions between "ID2_ID6", "ID6_ID2", "ID6_ID10",
"ID10_ID6" as in the example code...
ID Group
1 ID1 1
2 ID3 2
3 ID4 3
4 ID5 4
5 ID7 5
6 ID8 6
7 ID9 7
##
8 ID2 8
9 ID6 8
10 ID10 8
##
The code also needs to robust enough to recognize instances where we have an
interaction in a separate group...
i.e. "ID11_ID12" should be in a separate group (Group 9) as they don't interact
with IDs 2, 6, or 10 (not in below code!)
11 ID11 9
12 ID12 9
I've been trying to figure this out but have drawn a blank. My example code can
be found below.
Very best wishes,
Ross
Dr Ross Dwyer
Postdoctoral Research Fellow
University of Queensland
> ###
> require(stats)
> x <- sample(1:20,10)
> y <- sample(1:20,10)
> IDs <- sapply(1:10,function(i) paste("ID",i,sep=""))
> (DFid <- data.frame(x,y))
x y
1 7 20
2 5 3
3 12 5
4 3 12
5 18 19
6 2 1
7 19 15
8 20 11
9 13 14
10 1 2
>
>
> (DMdist <- dist(DFid, method = "euclidean",
+ diag = FALSE, upper = TRUE))
1 2 3 4 5 6 7
8 9 10
1 17.117243 15.811388 8.944272 11.045361 19.646883 13.000000
15.811388 8.485281 18.973666
2 17.117243 7.280110 9.219544 20.615528 3.605551 18.439089
17.000000 13.601471 4.123106
3 15.811388 7.280110 11.401754 15.231546 10.770330 12.206556
10.000000 9.055385 11.401754
4 8.944272 9.219544 11.401754 16.552945 11.045361 16.278821
17.029386 10.198039 10.198039
5 11.045361 20.615528 15.231546 16.552945 24.083189 4.123106
8.246211 7.071068 24.041631
6 19.646883 3.605551 10.770330 11.045361 24.083189 22.022716
20.591260 17.029386 1.414214
7 13.000000 18.439089 12.206556 16.278821 4.123106 22.022716
4.123106 6.082763 22.203603
8 15.811388 17.000000 10.000000 17.029386 8.246211 20.591260 4.123106
7.615773 21.023796
9 8.485281 13.601471 9.055385 10.198039 7.071068 17.029386 6.082763
7.615773 16.970563
10 18.973666 4.123106 11.401754 10.198039 24.041631 1.414214 22.203603
21.023796 16.970563
>
> #Generate True/False matrix on those individuals < 4 units apart
> DMTF <- apply(as.matrix(DMdist), c(1,2), function(x) ifelse(x<=4,T,F))
> diag(DMTF)<- NA #replace diagonal with NA
> dimnames(DMTF) <- list(IDs, IDs) #add individual's name to matrix
>
> DMTF
ID1 ID2 ID3 ID4 ID5 ID6 ID7 ID8 ID9 ID10
ID1 NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE
ID2 FALSE NA FALSE FALSE FALSE TRUE FALSE FALSE FALSE FALSE
ID3 FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE FALSE FALSE
ID4 FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE FALSE
ID5 FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE FALSE FALSE
ID6 FALSE TRUE FALSE FALSE FALSE NA FALSE FALSE FALSE TRUE
ID7 FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE FALSE
ID8 FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE FALSE
ID9 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE NA FALSE
ID10 FALSE FALSE FALSE FALSE FALSE TRUE FALSE FALSE FALSE NA
>
> irow <- as.character(gl(length(IDs),length(IDs),labels=IDs))
> icol <-rep(IDs, length(IDs))
>
> AssocMatrix <- matrix(data=paste(irow,"_",icol,"_",sep=""),
+ nrow = length(IDs), ncol = length(IDs),
+ dimnames= list(IDs, IDs))
>
> AssocMatrix
ID1 ID2 ID3 ID4 ID5 ID6
ID7 ID8 ID9 ID10
ID1 "ID1_ID1_" "ID2_ID1_" "ID3_ID1_" "ID4_ID1_" "ID5_ID1_" "ID6_ID1_"
"ID7_ID1_" "ID8_ID1_" "ID9_ID1_" "ID10_ID1_"
ID2 "ID1_ID2_" "ID2_ID2_" "ID3_ID2_" "ID4_ID2_" "ID5_ID2_" "ID6_ID2_"
"ID7_ID2_" "ID8_ID2_" "ID9_ID2_" "ID10_ID2_"
ID3 "ID1_ID3_" "ID2_ID3_" "ID3_ID3_" "ID4_ID3_" "ID5_ID3_" "ID6_ID3_"
"ID7_ID3_" "ID8_ID3_" "ID9_ID3_" "ID10_ID3_"
ID4 "ID1_ID4_" "ID2_ID4_" "ID3_ID4_" "ID4_ID4_" "ID5_ID4_" "ID6_ID4_"
"ID7_ID4_" "ID8_ID4_" "ID9_ID4_" "ID10_ID4_"
ID5 "ID1_ID5_" "ID2_ID5_" "ID3_ID5_" "ID4_ID5_" "ID5_ID5_" "ID6_ID5_"
"ID7_ID5_" "ID8_ID5_" "ID9_ID5_" "ID10_ID5_"
ID6 "ID1_ID6_" "ID2_ID6_" "ID3_ID6_" "ID4_ID6_" "ID5_ID6_" "ID6_ID6_"
"ID7_ID6_" "ID8_ID6_" "ID9_ID6_" "ID10_ID6_"
ID7 "ID1_ID7_" "ID2_ID7_" "ID3_ID7_" "ID4_ID7_" "ID5_ID7_" "ID6_ID7_"
"ID7_ID7_" "ID8_ID7_" "ID9_ID7_" "ID10_ID7_"
ID8 "ID1_ID8_" "ID2_ID8_" "ID3_ID8_" "ID4_ID8_" "ID5_ID8_" "ID6_ID8_"
"ID7_ID8_" "ID8_ID8_" "ID9_ID8_" "ID10_ID8_"
ID9 "ID1_ID9_" "ID2_ID9_" "ID3_ID9_" "ID4_ID9_" "ID5_ID9_" "ID6_ID9_"
"ID7_ID9_" "ID8_ID9_" "ID9_ID9_" "ID10_ID9_"
ID10 "ID1_ID10_" "ID2_ID10_" "ID3_ID10_" "ID4_ID10_" "ID5_ID10_" "ID6_ID10_"
"ID7_ID10_" "ID8_ID10_" "ID9_ID10_" "ID10_ID10_"
>
> (AMatrix <- as.character(unique(AssocMatrix[which(DMTF==TRUE)])))
[1] "ID2_ID6" "ID6_ID2" "ID6_ID10" "ID10_ID6"
>
> ##Extract those individuals not in any interactions
> nonassoc <- IDs[is.na(charmatch(inassoc,AMatrix))]
>
> (NGroups <- data.frame(ID=nonassoc,Group=1:length(nonassoc)))
ID Group
1 ID1 1
2 ID3 2
3 ID4 3
4 ID5 4
5 ID7 5
6 ID8 6
7 ID9 7
>
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