I have two problems where I've come up with some code that will do the analysis that I want, but it looks pretty clumsy. In the first case, I calculate the variance on five different columns for each of 14 clusters and get them into one matrix. I get the job done, but I would have thought that it could be done in one or two lines, not six, and be generalized so that it didn't matter how many columns I had. Any suggestions? xtap1<-tapply(xcmd[,1],xclu$clustering,var) xtap2<-tapply(xcmd[,2],xclu$clustering,var) xtap3<-tapply(xcmd[,3],xclu$clustering,var) xtap4<-tapply(xcmd[,4],xclu$clustering,var) xtap5<-tapply(xcmd[,5],xclu$clustering,var) xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5) The next example is somewhat similar, but I'm trying to calculate a Shannon-weaver information index for each cluster based on the proportion of objects with each "local.name". I start by tabulating the frequency of presence of each local.name in each cluster. The code does what I want, but I have other examples where I have hundreds of clusters and I would prefer not to have to type in a line for each cluster. Again, there must be a way and I would appreciate any suggestions: xtab<-table(x$LOCAL.NAME,xclu$clustering) xshw1<--sum((xtab[,1]+0.000001)/sum(xtab[,1])*(log((xtab[,1]+0.000001)/sum(x tab[,1])))) xshw2<--sum((xtab[,2]+0.000001)/sum(xtab[,2])*(log((xtab[,2]+0.000001)/sum(x tab[,2])))) xshw3<--sum((xtab[,3]+0.000001)/sum(xtab[,3])*(log((xtab[,3]+0.000001)/sum(x tab[,3])))) xshw4<--sum((xtab[,4]+0.000001)/sum(xtab[,4])*(log((xtab[,4]+0.000001)/sum(x tab[,4])))) xshw5<--sum((xtab[,5]+0.000001)/sum(xtab[,5])*(log((xtab[,5]+0.000001)/sum(x tab[,5])))) xshw6<--sum((xtab[,6]+0.000001)/sum(xtab[,6])*(log((xtab[,6]+0.000001)/sum(x tab[,6])))) xshw7<--sum((xtab[,7]+0.000001)/sum(xtab[,7])*(log((xtab[,7]+0.000001)/sum(x tab[,7])))) xshw8<--sum((xtab[,8]+0.000001)/sum(xtab[,8])*(log((xtab[,8]+0.000001)/sum(x tab[,8])))) xshw9<--sum((xtab[,9]+0.000001)/sum(xtab[,9])*(log((xtab[,9]+0.000001)/sum(x tab[,9])))) xshw10<--sum((xtab[,10]+0.000001)/sum(xtab[,10])*(log((xtab[,10]+0.000001)/s um(xtab[,10])))) xshw11<--sum((xtab[,11]+0.000001)/sum(xtab[,11])*(log((xtab[,11]+0.000001)/s um(xtab[,11])))) xshw12<--sum((xtab[,12]+0.000001)/sum(xtab[,12])*(log((xtab[,12]+0.000001)/s um(xtab[,12])))) xshw13<--sum((xtab[,13]+0.000001)/sum(xtab[,13])*(log((xtab[,13]+0.000001)/s um(xtab[,13])))) xshw14<--sum((xtab[,14]+0.000001)/sum(xtab[,14])*(log((xtab[,14]+0.000001)/s um(xtab[,14])))) xshw<-rbind(xshw1,xshw2,xshw3,xshw4,xshw5,xshw6,xshw7,xshw8,xshw9,xshw10,xsh w11,xshw12,xshw13,xshw14) xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5,(xtapx<-(xtap1+xtap2+xtap3+xtap4+x tap5)),xshw) Thanks a lot in advance!! Mikkel Mikkel Grum, PhD Genetic Diversity Scientist International Plant Genetic Resources Institute (IPGRI) Sub-Saharan Africa Group *** c/o ICRAF PO Box 30677 Nairobi, Kenya m.grum at cgiar.org ipgri-kenya at cgiar.org www.ipgri.cgiar.org -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
I think you can use a for loop.
Something like:
for( i in 1:n ) {
...
}
where n is whatever the number of iterations.
Cheers,
Ko-Kang
--------------------------------------------------------------------------------------------
Ko-Kang Kevin Wang
Head of Statistical Analysis Division
Software Developers' Klub (SDK)
University of Auckland
New Zealand
-----Original Message-----
From: owner-r-help at stat.math.ethz.ch [mailto:owner-r-help at
stat.math.ethz.ch]On Behalf Of Grum, Mikkel
Sent: Tuesday, August 21, 2001 7:15 PM
To: R-help at lists.R-project.org
Subject: [R] looking for a smarter way
I have two problems where I've come up with some code that will do the
analysis that I want, but it looks pretty clumsy. In the first case, I
calculate the variance on five different columns for each of 14 clusters and
get them into one matrix. I get the job done, but I would have thought that
it could be done in one or two lines, not six, and be generalized so that it
didn't matter how many columns I had. Any suggestions?
xtap1<-tapply(xcmd[,1],xclu$clustering,var)
xtap2<-tapply(xcmd[,2],xclu$clustering,var)
xtap3<-tapply(xcmd[,3],xclu$clustering,var)
xtap4<-tapply(xcmd[,4],xclu$clustering,var)
xtap5<-tapply(xcmd[,5],xclu$clustering,var)
xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5)
The next example is somewhat similar, but I'm trying to calculate a
Shannon-weaver information index for each cluster based on the proportion of
objects with each "local.name". I start by tabulating the frequency
of
presence of each local.name in each cluster. The code does what I want, but
I have other examples where I have hundreds of clusters and I would prefer
not to have to type in a line for each cluster. Again, there must be a way
and I would appreciate any suggestions:
xtab<-table(x$LOCAL.NAME,xclu$clustering)
xshw1<--sum((xtab[,1]+0.000001)/sum(xtab[,1])*(log((xtab[,1]+0.000001)/sum(x
tab[,1]))))
xshw2<--sum((xtab[,2]+0.000001)/sum(xtab[,2])*(log((xtab[,2]+0.000001)/sum(x
tab[,2]))))
xshw3<--sum((xtab[,3]+0.000001)/sum(xtab[,3])*(log((xtab[,3]+0.000001)/sum(x
tab[,3]))))
xshw4<--sum((xtab[,4]+0.000001)/sum(xtab[,4])*(log((xtab[,4]+0.000001)/sum(x
tab[,4]))))
xshw5<--sum((xtab[,5]+0.000001)/sum(xtab[,5])*(log((xtab[,5]+0.000001)/sum(x
tab[,5]))))
xshw6<--sum((xtab[,6]+0.000001)/sum(xtab[,6])*(log((xtab[,6]+0.000001)/sum(x
tab[,6]))))
xshw7<--sum((xtab[,7]+0.000001)/sum(xtab[,7])*(log((xtab[,7]+0.000001)/sum(x
tab[,7]))))
xshw8<--sum((xtab[,8]+0.000001)/sum(xtab[,8])*(log((xtab[,8]+0.000001)/sum(x
tab[,8]))))
xshw9<--sum((xtab[,9]+0.000001)/sum(xtab[,9])*(log((xtab[,9]+0.000001)/sum(x
tab[,9]))))
xshw10<--sum((xtab[,10]+0.000001)/sum(xtab[,10])*(log((xtab[,10]+0.000001)/s
um(xtab[,10]))))
xshw11<--sum((xtab[,11]+0.000001)/sum(xtab[,11])*(log((xtab[,11]+0.000001)/s
um(xtab[,11]))))
xshw12<--sum((xtab[,12]+0.000001)/sum(xtab[,12])*(log((xtab[,12]+0.000001)/s
um(xtab[,12]))))
xshw13<--sum((xtab[,13]+0.000001)/sum(xtab[,13])*(log((xtab[,13]+0.000001)/s
um(xtab[,13]))))
xshw14<--sum((xtab[,14]+0.000001)/sum(xtab[,14])*(log((xtab[,14]+0.000001)/s
um(xtab[,14]))))
xshw<-rbind(xshw1,xshw2,xshw3,xshw4,xshw5,xshw6,xshw7,xshw8,xshw9,xshw10,xsh
w11,xshw12,xshw13,xshw14)
xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5,(xtapx<-(xtap1+xtap2+xtap3+xtap4+x
tap5)),xshw)
Thanks a lot in advance!!
Mikkel
Mikkel Grum, PhD
Genetic Diversity Scientist
International Plant Genetic Resources Institute (IPGRI)
Sub-Saharan Africa Group
***
c/o ICRAF
PO Box 30677 Nairobi, Kenya
m.grum at cgiar.org
ipgri-kenya at cgiar.org
www.ipgri.cgiar.org
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-help-request at
stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-help-request at
stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
"Grum, Mikkel" wrote:
I have two problems where I've come up with some code that will do the
analysis that I want, but it looks pretty clumsy. In the first case, I
calculate the variance on five different columns for each of 14 clusters
and get them into one matrix. I get the job done, but I would have
thought that it could be done in one or two lines, not six, and be
generalized so that it didn't matter how many columns I had. Any
suggestions?
xtap1<-tapply(xcmd[,1],xclu$clustering,var)
xtap2<-tapply(xcmd[,2],xclu$clustering,var)
xtap3<-tapply(xcmd[,3],xclu$clustering,var)
xtap4<-tapply(xcmd[,4],xclu$clustering,var)
xtap5<-tapply(xcmd[,5],xclu$clustering,var)
xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5)
Mikkel:
I think you might be looking for something like this:
xtap <- apply(xcmd[,1:5], 2, function(x){tapply(x, list(xclu$clustering),
var)})
Hope this helps,
Chuck
-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-
Chuck Cleland
Institute for the Study of Child Development
UMDNJ--Robert Wood Johnson Medical School
97 Paterson St.
New Brunswick, NJ 08903
phone: (732) 235-7699
fax: (732) 235-6189
http://www2.umdnj.edu/iscdweb/
http://members.nbci.com/cmcleland
-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-help-request at
stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
This works great and I was able to extend it to the other problem too!
Thanks a lot!!
Cheers,
Mikkel
-----Original Message-----
From: Cleland [mailto:clelancm at UMDNJ.EDU]
Sent: 21 August 2001 13:31
To: Grum, Mikkel
Cc: R-help at hypatia.math.ethz.ch
Subject: Re: [R] looking for a smarter way
"Grum, Mikkel" wrote:
I have two problems where I've come up with some code that will do the
analysis that I want, but it looks pretty clumsy. In the first case, I
calculate the variance on five different columns for each of 14 clusters and
get them into one matrix. I get the job done, but I would have thought that
it could be done in one or two lines, not six, and be generalized so that it
didn't matter how many columns I had. Any suggestions?
xtap1<-tapply(xcmd[,1],xclu$clustering,var)
xtap2<-tapply(xcmd[,2],xclu$clustering,var)
xtap3<-tapply(xcmd[,3],xclu$clustering,var)
xtap4<-tapply(xcmd[,4],xclu$clustering,var)
xtap5<-tapply(xcmd[,5],xclu$clustering,var)
xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5)
Mikkel:
I think you might be looking for something like this:
xtap <- apply(xcmd[,1:5], 2, function(x){tapply(x, list(xclu$clustering),
var)})
Hope this helps,
Chuck
-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-
Chuck Cleland
Institute for the Study of Child Development UMDNJ-Robert Wood Johnson
Medical School 97 Paterson St.
New Brunswick, NJ 08903
phone: (732) 235-7699
fax: (732) 235-6189
http://www2.umdnj.edu/iscdweb/
http://members.nbci.com/cmcleland
-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-<>-
-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-
r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html
Send "info", "help", or "[un]subscribe"
(in the "body", not the subject !) To: r-help-request at
stat.math.ethz.ch
_._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._
It looks like you've solved this problem, but another nice solution is to use the aggregate command as in: xtap<-aggregate(xcmd[,1:5],list(xclu$clustering),var) I'm not sure why R requires one to explicitly provide a list as the second argument if the second arguement is a vector (this is not necessary in SPLUS), but this is a minor issue.... -----Original Message----- From: Grum, Mikkel [mailto:M.GRUM at CGIAR.ORG] Sent: Tuesday, August 21, 2001 3:15 AM To: R-help at lists.R-project.org Subject: [R] looking for a smarter way I have two problems where I've come up with some code that will do the analysis that I want, but it looks pretty clumsy. In the first case, I calculate the variance on five different columns for each of 14 clusters and get them into one matrix. I get the job done, but I would have thought that it could be done in one or two lines, not six, and be generalized so that it didn't matter how many columns I had. Any suggestions? xtap1<-tapply(xcmd[,1],xclu$clustering,var) xtap2<-tapply(xcmd[,2],xclu$clustering,var) xtap3<-tapply(xcmd[,3],xclu$clustering,var) xtap4<-tapply(xcmd[,4],xclu$clustering,var) xtap5<-tapply(xcmd[,5],xclu$clustering,var) xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5) The next example is somewhat similar, but I'm trying to calculate a Shannon-weaver information index for each cluster based on the proportion of objects with each "local.name". I start by tabulating the frequency of presence of each local.name in each cluster. The code does what I want, but I have other examples where I have hundreds of clusters and I would prefer not to have to type in a line for each cluster. Again, there must be a way and I would appreciate any suggestions: xtab<-table(x$LOCAL.NAME,xclu$clustering) xshw1<--sum((xtab[,1]+0.000001)/sum(xtab[,1])*(log((xtab[,1]+0.000001)/sum(x tab[,1])))) xshw2<--sum((xtab[,2]+0.000001)/sum(xtab[,2])*(log((xtab[,2]+0.000001)/sum(x tab[,2])))) xshw3<--sum((xtab[,3]+0.000001)/sum(xtab[,3])*(log((xtab[,3]+0.000001)/sum(x tab[,3])))) xshw4<--sum((xtab[,4]+0.000001)/sum(xtab[,4])*(log((xtab[,4]+0.000001)/sum(x tab[,4])))) xshw5<--sum((xtab[,5]+0.000001)/sum(xtab[,5])*(log((xtab[,5]+0.000001)/sum(x tab[,5])))) xshw6<--sum((xtab[,6]+0.000001)/sum(xtab[,6])*(log((xtab[,6]+0.000001)/sum(x tab[,6])))) xshw7<--sum((xtab[,7]+0.000001)/sum(xtab[,7])*(log((xtab[,7]+0.000001)/sum(x tab[,7])))) xshw8<--sum((xtab[,8]+0.000001)/sum(xtab[,8])*(log((xtab[,8]+0.000001)/sum(x tab[,8])))) xshw9<--sum((xtab[,9]+0.000001)/sum(xtab[,9])*(log((xtab[,9]+0.000001)/sum(x tab[,9])))) xshw10<--sum((xtab[,10]+0.000001)/sum(xtab[,10])*(log((xtab[,10]+0.000001)/s um(xtab[,10])))) xshw11<--sum((xtab[,11]+0.000001)/sum(xtab[,11])*(log((xtab[,11]+0.000001)/s um(xtab[,11])))) xshw12<--sum((xtab[,12]+0.000001)/sum(xtab[,12])*(log((xtab[,12]+0.000001)/s um(xtab[,12])))) xshw13<--sum((xtab[,13]+0.000001)/sum(xtab[,13])*(log((xtab[,13]+0.000001)/s um(xtab[,13])))) xshw14<--sum((xtab[,14]+0.000001)/sum(xtab[,14])*(log((xtab[,14]+0.000001)/s um(xtab[,14])))) xshw<-rbind(xshw1,xshw2,xshw3,xshw4,xshw5,xshw6,xshw7,xshw8,xshw9,xshw10,xsh w11,xshw12,xshw13,xshw14) xtap<-cbind(xtap1,xtap2,xtap3,xtap4,xtap5,(xtapx<-(xtap1+xtap2+xtap3+xtap4+x tap5)),xshw) Thanks a lot in advance!! Mikkel Mikkel Grum, PhD Genetic Diversity Scientist International Plant Genetic Resources Institute (IPGRI) Sub-Saharan Africa Group *** c/o ICRAF PO Box 30677 Nairobi, Kenya m.grum at cgiar.org ipgri-kenya at cgiar.org www.ipgri.cgiar.org -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-. -.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._. _._ -.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.-.- r-help mailing list -- Read http://www.ci.tuwien.ac.at/~hornik/R/R-FAQ.html Send "info", "help", or "[un]subscribe" (in the "body", not the subject !) To: r-help-request at stat.math.ethz.ch _._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._._