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pam_clustering.Rout
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R version 2.13.0 (2011-04-13)
Copyright (C) 2011 The R Foundation for Statistical Computing
ISBN 3-900051-07-0
Platform: x86_64-apple-darwin9.8.0/x86_64 (64-bit)
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
Natural language support but running in an English locale
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
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[Previously saved workspace restored]
> library(pamr)
Loading required package: cluster
Loading required package: survival
Loading required package: splines
Warning message:
package 'pamr' was built under R version 2.13.2
> library(StatDA)
Loading required package: akima
Loading required package: car
Loading required package: MASS
Loading required package: nnet
Loading required package: e1071
Loading required package: class
Loading required package: geoR
Loading required package: sp
Spatial Point Pattern Analysis Code in S-Plus
Version 2 - Spatial and Space-Time analysis
--------------------------------------------------------------
Analysis of geostatistical data
For an Introduction to geoR go to http://www.leg.ufpr.br/geoR
geoR version 1.7-2 (built on 2011-11-15) is now loaded
--------------------------------------------------------------
Loading required package: leaps
Loading required package: pixmap
Loading required package: robustbase
Attaching package: 'robustbase'
The following object(s) are masked from 'package:survival':
heart
Loading required package: rgl
Loading required package: rgr
Loading required package: fastICA
Attaching package: 'rgr'
The following object(s) are masked from 'package:car':
logit
Loading required package: sgeostat
Loading required package: mgcv
This is mgcv 1.7-5. For overview type 'help("mgcv-package")'.
Loading required package: xtable
There were 13 warnings (use warnings() to see them)
> data<-read.table("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/dissimilarity.txt")
> energies<-read.table("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/energies.txt")
> clusters <- pam(as.dist(data), 5, diss=1)
> result <- clusters[3]
> postscript("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/pam_clustering.eps")
> clusplot(clusters)
> dev.off()
null device
1
> postscript("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/pam_clustering_energies_1.eps")
> edaplot(energies[stack(clusters[3])[1]==1])
> dev.off()
null device
1
> postscript("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/pam_clustering_energies_2.eps")
> edaplot(energies[stack(clusters[3])[1]==2])
> dev.off()
null device
1
> postscript("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/pam_clustering_energies_3.eps")
> edaplot(energies[stack(clusters[3])[1]==3])
> dev.off()
null device
1
> postscript("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/pam_clustering_energies_4.eps")
> edaplot(energies[stack(clusters[3])[1]==4])
> dev.off()
null device
1
> postscript("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/pam_clustering_energies_5.eps")
> edaplot(energies[stack(clusters[3])[1]==5])
> dev.off()
null device
1
> postscript("/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/energy_distribution.eps")
> edaplot(energies[,1])
> dev.off()
null device
1
>
> #Sys.sleep(10)
> write.table(result, file = "/Volumes/ldpf/md-simulations/LasCMD_Binary/Atieh_et_al/pipeline-test01/bmse000048-L-serine-rmsdTest2/clusterResults.txt")
> q(save = "yes", status = 0, runLast = TRUE)
> proc.time()
user system elapsed
7.726 0.305 38.165