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akromeson.html
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<h3>akromeson.R</h3>
<p>phug7649 — <em>Apr 10, 2013, 4:35 PM</em></p>
<pre><code class="r">#setwd("C:/Users/phug7649/Desktop/kmeans/Paper_2/0-5")
#classes<-read.csv("f1.25 11_class.txt",sep="") ##this is the output from the fuzzy k analysis. Using 0-5 currently
##as a testbed
Clusters<-11
setwd("C:/Users/phug7649/Desktop/txtbin")
##for the function to work optimally, the specific "subset" file needs to be changed to a generic "input" file.
##I have created several input files based on the same data set.
#subset0_5<-read.csv("USII_0_5.csv")
input<-read.csv("USII_0_5.csv")
# input<-read.csv("USII_5_10.csv")
# input<-read.csv("USII_10_20.csv")
# input<-read.csv("USII_20_40.csv")
# input<-read.csv("USII_40_60.csv")
# input<-read.csv("USII_60_100.csv")
# input<-read.csv("USII_100plus.csv")
##this data needs the silt fraction removed. This may cause problems down the line
one<-input[,1:9]
two<-input[,11:12]
input<-cbind(one,two)
##reading in the results from FKM in its appropriate directory
setwd("C:/Users/phug7649/Desktop/kmeans/Paper_2/0-5")
clusfind<-read.csv("summary.txt",sep="", header=TRUE)
head(clusfind)
</code></pre>
<pre><code> Class Phi X..u.u... OFV X.dJ.dphi FPI MPE S Wilks_L
1 2 1.1 9.296e-05 94715 54507 0.2823 0.3230 2.3770 NA
2 3 1.1 8.923e-05 87009 80205 0.2255 0.2390 1.6501 NA
3 4 1.1 8.452e-05 80234 88848 0.1849 0.1792 1.4356 NA
4 5 1.1 9.445e-05 75557 106387 0.1914 0.1737 1.1782 NA
5 6 1.1 9.324e-05 71859 118066 0.1744 0.1529 1.1619 NA
6 7 1.1 9.240e-05 68711 126758 0.1670 0.1406 0.9873 NA
</code></pre>
<pre><code class="r">
# Spinning 3d Scatterplot
##how do you rename a header? heres how!
#names(classes)[1]<-"natural_key"
#class_input<-merge(input,classes, by= "natural_key",all=TRUE)
y<-ncol(input)
##should have used "row.names=FALSE" when making this csv. I will fix the problem later.
input<-input[,2:y]# remove this when the issue is fixed.
y<-ncol(input)
a<-princomp(input[,2:y], cor=TRUE)
prin<-a$scores
#csprin<-cbind(class_input,prin)
####################################################################################################################
#################################### Time to use the script found in EMII.r ########################################
####################################################################################################################
#I need to make a function out of this...
############################################# THE CONVEX BICYCLE ###################################################
##A script made to identify a small number of points around the periphery of a data cloud. This should coincide with
##the location of end members. It works by creating an n-dimensional convex hull (thanks seb),finding a point with a
##maximum distance from zero then finding the maximum distance of this point from all other points in the hull.
####################################################################################################################
##constructing the dataset. Required: 1 column (column.30 with the components arranged after that.)
Column.30<-1:nrow(prin)
z<-cbind(Column.30,prin)
z<-z[,2:ncol(z)]
##scripts required for this algorithm to work...
source("C:/Users/phug7649/Desktop/TXTBIN/R-scripts/functions/point_euclid.R")
source("C:/Users/phug7649/Desktop/TXTBIN/R-scripts/functions/qhull_algorithm.R")
################################################# control panel ####################################################
## there are two control methods atm; the first is to define the length of the yardstick. Provides an undefined number
## of end-members. the second is to use an equation which most likely is data specific.
ys<-10 ##starting parameter for yardstick
factor<-.55 ##creating the factor by which the yardstick length is modified (previous run was 0.8)
YScrit<-3.2 ##Stopping criteria; when the overall size of the hull is less than this, the algorithm stops.
####################################################################################################################
rm(bin)
</code></pre>
<pre><code>Warning: object 'bin' not found
</code></pre>
<pre><code class="r">file.create("bin.csv")##creating a file to dump values
</code></pre>
<pre><code>[1] TRUE
</code></pre>
<pre><code class="r">bin<-c()
cz<-quick_hull(z)##Using sebs script to create hulls
while (ys>YScrit)##I want the loop to start here
{
czr<-z[cz,]##sum of rows
czr<-czr^2
czrsum<-rowSums(czr)
fin<-sqrt(czrsum)
finm<-as.matrix(fin)
refmax<-which.max(finm)##rows with max and min euclidean distance from zero
BLARG<-as.data.frame(z)[cz[refmax],]##getting maximum value and anchoring it to the row number in the master data set (z)
rowx<-BLARG##retrieving all the principal component data from rows that contain maximum and minimum euclidean distances
object<-z[cz,] ## retrieving all pc data from cz
pcdist<-as.matrix(point_euclid(object,rowx))##getting distances
b<-as.numeric(pcdist[which.max(pcdist),])##max distance
ys<-b*factor##yardstick
new <- ys < as.vector(pcdist)##compare yardstick to the convex hull
or <- cz[which(pcdist == 0)]##Placing maximum (maxi) and minimum (origin) points in the final file
bin <- rbind(or,bin)
cz <- cz[which(new)]##Exclude any values inferior to yardstick (this file should be renamed cz when its time to reiterate)
print(ys) #print the yardstick value to see if the script is running
}
</code></pre>
<pre><code>[1] 11.43
[1] 11.67
[1] 8.963
[1] 7.801
[1] 8.109
[1] 7.718
[1] 6.708
[1] 7.636
[1] 6.22
[1] 5.423
[1] 5.424
[1] 4.87
[1] 4.02
[1] 3.742
[1] 3.236
[1] 3.47
[1] 3.307
[1] 3.113
</code></pre>
<pre><code class="r">paste0("your algorithm has returned ",nrow(bin), " end points")
</code></pre>
<pre><code>[1] "your algorithm has returned 18 end points"
</code></pre>
<pre><code class="r">paste0("Yardstick factor is ",factor,","," stopping criterion is ",YScrit)
</code></pre>
<pre><code>[1] "Yardstick factor is 0.55, stopping criterion is 3.2"
</code></pre>
<pre><code class="r">ys<-10
####################################################################################################################
##creating an identity matrix
matrix<-diag(nrow(bin))
##creating end point matrices
points<-input[bin,]
#verify<-cbind(bin,points)
# #creating control file
# crow1<-c("weights","phi","nend","nclass")
# crow2<-c(w,p,ncol(bin),total)
# #writing files
setwd("C:\\Users\\phug7649\\Documents\\MATLAB")
write.table(matrix,"matrix.csv",row.names=FALSE,col.names=FALSE,sep=",")
write.csv(points,"EP.csv",row.names=FALSE)
write.csv(input,"DATA.csv",row.names=FALSE)
checkdata<-read.csv("edg_2072_ep_k_2072_II.csv")
head(checkdata)
</code></pre>
<pre><code> Soil_ID Silt Clay pH_Water Conductivity_.CS.M. Chloride Ca_Carbonate
1 ed001 10.9 52.8 6.85 24.0 72 0.05
2 ed001 11.2 58.2 8.08 10.0 12 0.05
3 ed001 11.6 51.9 9.20 23.9 30 0.30
4 ed001 10.9 49.7 9.29 41.6 160 0.40
5 ed001 10.8 55.6 9.24 73.6 336 0.70
6 ed001 12.6 51.8 9.46 62.5 289 3.80
Carbon Phosphate Calcium Magnesium Potassium Sodium
1 2.29 60.3 183.1 145.8 24.6 19.0
2 0.90 8.2 234.2 171.6 12.5 34.1
3 0.74 7.3 230.8 166.4 10.6 70.2
4 0.35 26.0 167.8 151.6 5.6 81.7
5 0.25 20.0 203.3 166.7 7.9 100.0
6 0.03 13.0 146.4 143.7 2.5 94.9
</code></pre>
<pre><code class="r">data<-read.csv("DATA.csv")
head(data)
</code></pre>
<pre><code> natural_key ph_h2o caco3 cec_nh4 L A B clay_tot_psa
1 00P00090 6.222 0 20.4 30.92 1.552 5.917 8.2
2 00P00265 6.100 0 10.0 20.37 1.910 5.438 3.1
3 00P00398 8.900 0 33.6 30.90 3.704 12.138 47.7
4 00P00422 9.408 12 25.5 30.90 3.704 12.138 45.9
5 00P00432 6.800 15 25.2 41.30 1.459 13.280 45.4
6 00P00440 7.036 0 10.2 30.90 3.704 12.138 13.1
sand_tot_psa oc
1 42.2 6.30
2 84.9 1.69
3 14.3 0.96
4 8.6 1.20
5 7.1 2.07
6 54.3 0.88
</code></pre>
<pre><code class="r">checkep<-read.csv("edg_2072_ep_k_5_II.csv")
head(checkep)
</code></pre>
<pre><code> Soil.ID Silt Clay pH.Water Conductivity..CS.M. Chloride Ca.Carbonate
1 E1 16.0 45.0 8.66 116.5 1059 8.50
2 E2 6.5 19.9 6.67 26.3 29 0.05
3 E3 7.8 17.1 9.18 14.3 12 77.60
4 E4 18.1 27.5 4.34 64.3 603 0.05
5 E5 18.3 58.0 8.10 171.2 764 0.50
Carbon Phosphate Calcium Magnesium Potassium Sodium
1 0.90 6.10 76.70 471.4 2.00 151.8
2 8.27 91.50 101.40 66.9 13.80 0.2
3 0.04 0.05 32.20 102.8 0.05 20.6
4 0.05 0.60 0.05 145.0 1.90 27.6
5 0.55 26.60 196.00 111.9 133.90 31.2
</code></pre>
<pre><code class="r">ep<-read.csv("EP.csv")
head(ep)
</code></pre>
<pre><code> natural_key ph_h2o caco3 cec_nh4 L A B clay_tot_psa
1 97P01625 8.4 0 10.1 20.36 4.0872 11.6491 5.8
2 82P04392 7.4 2 0.6 41.32 -4.4656 6.0573 5.6
3 02N01222 4.5 0 17.7 20.39 -0.2811 -0.7465 0.9
4 PSU07151 4.7 0 22.6 20.39 -0.2811 -0.7465 0.0
5 03N05180 5.1 0 74.4 41.30 3.3216 12.5259 14.3
6 40A32030 3.9 0 11.7 71.58 1.8513 5.5929 11.5
sand_tot_psa oc
1 86.4 2.29
2 33.7 0.23
3 92.6 6.72
4 0.0 10.58
5 65.8 0.96
6 29.1 0.88
</code></pre>
<pre><code class="r">checkmatrix<-read.csv("edg_2073_ep_k_id.csv")
head(checkmatrix)
</code></pre>
<pre><code> X1 X0 X0.1 X0.2 X0.3
1 0 1 0 0 0
2 0 0 1 0 0
3 0 0 0 1 0
4 0 0 0 0 1
</code></pre>
<pre><code class="r">matII<-read.csv("matrix.csv")
head(matII)
</code></pre>
<pre><code> X1 X0 X0.1 X0.2 X0.3 X0.4 X0.5 X0.6 X0.7 X0.8 X0.9 X0.10 X0.11 X0.12
1 0 1 0 0 0 0 0 0 0 0 0 0 0 0
2 0 0 1 0 0 0 0 0 0 0 0 0 0 0
3 0 0 0 1 0 0 0 0 0 0 0 0 0 0
4 0 0 0 0 1 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 1 0 0 0 0 0 0 0 0
6 0 0 0 0 0 0 1 0 0 0 0 0 0 0
X0.13 X0.14 X0.15 X0.16
1 0 0 0 0
2 0 0 0 0
3 0 0 0 0
4 0 0 0 0
5 0 0 0 0
6 0 0 0 0
</code></pre>
<pre><code class="r">
message(paste0("nclass needs to be ", Clusters+nrow(bin)))
</code></pre>
<pre><code>nclass needs to be 29
</code></pre>
<pre><code class="r">
shell("matlab -nodesktop -nosplash -wait -r rep")
##this will turn matlab output into a pretty graph without having to think about it.
# install.packages(c("Cairo"), repos="http://cran.r-project.org" )
library(Cairo)
library(plyr)
library(ggplot2)
library(grid)
source("C:/Users/phug7649/Desktop/TXTBIN/R-scripts/functions/make_letter_ids.R")
##input files for matlab:
setwd("C:\\Users\\phug7649\\Documents\\MATLAB")
matrix<-read.table("matrix.csv",sep=",")
end_points<-read.csv("EP.csv",sep=",")
data<-read.csv("DATA.csv",sep=",")
##matlab output files:
centroid_table<-read.csv("mcent.csv",header=FALSE,sep=",")
a<-nrow(centroid_table)
y<-make_letter_ids(nrow(centroid_table))
centroid_table<-cbind(y,centroid_table)
##joining original data to the centroid table, to be used later.
names(centroid_table) <- names(data)
data_cent<-rbind(data,centroid_table)
##creating principal components from the original data.
princomp_main<-princomp(data_cent[,2:ncol(data)],cor=TRUE)
princomp_comp<-princomp_main$scores
##attaching principal components to main data, plotting to ensure we know what it looks like.
#plot(princomp_comp[,1],princomp_comp[,2])
##creating max distance column
data_distances<-read.csv("mdist.csv",sep=",",header=F)
id.matrix<-diag(nrow(centroid_table))
max<-y
id.matrix<-cbind(id.matrix,max)
natural_key<-max
data_cent.prin<-cbind(data_cent,princomp_comp)
names(data_distances)<-make_letter_ids(nrow(centroid_table))
weighting_factor<-read.csv("weighting.csv",sep=",")
number_of_rows<-read.csv("rows.csv",header=FALSE,sep=",")
number_of_end_members<-read.csv("end.csv",header=FALSE,sep=",")
number_of_centroids<-read.csv("cent.csv",header=FALSE,sep=",")
w<-weighting_factor[1,1]
##create the id matrix from the matlab data
##creating max column for data distances
aa<-as.matrix(data_distances)
data_distances$max<-apply(aa,1,which.max)
data_ratio<-data_distances
data_ratio$max<-apply(aa,1,which.max)
max<-make_letter_ids(nrow(centroid_table))
data_distances$max<-max[data_distances$max]
max<-data_distances$max
max<-rbind(c(max,y))
max<-t(max)
data.complete<-cbind(data_cent.prin,max)
datarows<-nrow(data)
position1<-datarows+1
cdatarows<-nrow(data.complete)
centroids.complete<-data.complete[position1:cdatarows,]
emno<-number_of_end_members[1,1]
ceno<-nrow(centroid_table)-emno
soil.id<-rep(c("E", "C"), c(emno, ceno))
centroids.complete<-cbind(centroids.complete,soil.id)
totals<-as.data.frame(table(data_ratio$max))
end.tot<-totals[1:nrow(matrix),]
cent.tot<-totals[nrow(matrix):nrow(totals),]
sum.end<-sum(end.tot[,2])
sum.cent<-sum(cent.tot[,2])
ratio<-(sum.end/(sum.end+sum.cent))*100
ratio<-round(ratio,digits=2)
(paste0("Weighting ", weighting_factor[1,1],", creating ",ratio, "% end point memberships"))
</code></pre>
<pre><code>[1] "Weighting 2000, creating 5.02% end point memberships"
</code></pre>
<pre><code class="r">
setwd("C:/Users/phug7649/Desktop/txtbin")
###SEB GOING NUTS (more often referred to as a panel plot)
NUTS<-ggplot(data.complete, aes(x=Comp.1, y=Comp.2), group=max)+
theme_bw() +
geom_point(colour="grey40")+
#stat_bin2d(binwidth=c(1, 1),colour=gray) +
facet_wrap(~ max, nrow=5)+
geom_point(data=centroids.complete, aes(shape=soil.id),size=4, colour="black")+
scale_shape_manual(values=c(16, 17))+
theme(plot.background = element_rect(fill = w))+
# xlim(-12,8)+
# ylim(-7.5,5)+
ggtitle(paste0("Weighting ", weighting_factor[1,1],", creating ",ratio, " percent end point memberships"))+
# theme(panel.margin = unit(5, "lines"))+
coord_equal()
NUTS
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r">
ggsave(paste0("nuts", w, number_of_end_members[1,1],".png"),type="cairo")
</code></pre>
<pre><code>Saving 7 x 7 in image
</code></pre>
<pre><code class="r">
#Assign colours in one giant plot for Alex.
#hclust(centroids.complete[,2:19])
#qplot(Comp.1,Comp.2,data=data.complete,colour=centroids.complete$soil.id)
combined<-ggplot(data.complete,aes(x=Comp.1,y=Comp.2))+
geom_point(aes(colour=max))+
geom_point(data=centroids.complete,aes(shape=soil.id),size=4)+
scale_shape_manual(values=c(16,17))+
# xlim(-12,8)+
# ylim(-7.5,5)+
ggtitle(paste0("Weighting ", weighting_factor[1,1],", creating ",ratio, " percent end point memberships"))+
coord_equal()
combined
</code></pre>
<p><img 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" alt="plot of chunk unnamed-chunk-1"/> </p>
<pre><code class="r">ggsave(paste0("combined", w, number_of_end_members[1,1],".png"),type="cairo")
</code></pre>
<pre><code>Saving 7 x 7 in image
</code></pre>
<pre><code class="r">
##creating a plot of end point ratios-hard to put into function
# w<-c(5,10,20,40,100,200,400,1000,2000,4000)
# ratio<-c(55.87,48.89,35.17,35.25,19.89,11.3,6.81,5.36,5.02,4.85)
# percent<-cbind(w,ratio)
# plot(percent)
# E.C<-ggplot(data.complete,aes(x=Comp.1,y=Comp.2))+
# geom_point(aes(colour=soil.id))+
# geom_point(data=centroids.complete,aes(shape=soil.id),size=4)+
# scale_shape_manual(values=c(16,17))+
# # xlim(-12,8)+
# # ylim(-7.5,5)+
# ggtitle(paste0("Weighting ", weighting_factor[1,1],", creating ",ratio, " percent end point memberships"))+
# coord_equal()
# E.C
# ggsave(paste0("combined", w, number_of_end_members[1,1],".png"),type="cairo")
##################################################################################################################
############################################### THE ALEX BIT #####################################################
##################################################################################################################
# attach(centroids.muns)
# newdata <- centroids.muns[order(caco3),]
# newdata <- centroids.muns[order(cec_nh4),]
# newdata <- centroids.muns[order(clay_tot_psa),]
# newdata <- centroids.muns[order(oc),]
# newdata <- centroids.muns[order(ph_h2o),]
# newdata <- centroids.muns[order(soil.id),]
# detach(centroids.muns)
# EP<-newdata[1:11,]
# C<-newdata[12:nrow(newdata),]
#
# EP<-EP[order(EP$ph_h2o),]
# EP<-EP[order(EP$sand_tot_psa),]
# EP<-EP[order(EP$clay_tot_psa),]
#
# C<-C[order(C$ph_h2o),]
# C<-C[order(C$sand_tot_psa),]
# C<-C[order(C$clay_tot_psa),]
#
# # prepare hierarchical cluster
# hc = hclust(dist(EP[,2:10]))
# # very simple dendrogram
# plot(hc)
# # labels at the same level
# plot(hc,hang=-1)
# hc <- hclust(dist(EP[,c("ph_h2o","caco3","cec_nh4","L","A","B","clay_tot_psa","sand_tot_psa","oc")]), "ward")
# plot(hc, hang=-1,labels=EP$natural_key)
</code></pre>
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