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Author*Unverified author*
R Software Modulerwasp_hierarchicalclustering.wasp
Title produced by softwareHierarchical Clustering
Date of computationMon, 02 Dec 2024 15:45:39 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2024/Dec/02/t1733150876q9ip4meak3mmb3w.htm/, Retrieved Fri, 07 Aug 2026 21:30:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=320155, Retrieved Fri, 07 Aug 2026 21:30:55 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact287
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Hierarchical Clustering] [] [2024-12-02 14:45:39] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
Genotypes	Dobie susceptibility indexes
JM-PI230970/PI635999-020-T14-S43 (Figure 4)	0.82
JM-PI635999/F6LG04-5196-LG06-5920-020-T3-S5	1.05
TGX 2014-49FZ	1.86
JM-PI230970/PI635999-020-T14-S23	2.07
JM-PI230970/PI635999-020-T14-S21	2.17
JM-PI230970/PI635999-020-T14-S40	2.39
SCS-1	2.43
PI423958	2.61
PI471904	2.67
JM-CLK/CRFD-15-SD	2.95
TGX 2001-13DM	3.06
PI423961A	3.07
Belesa-95	3.08
JM-PI230970/PI635999-020-T14-S10	3.16
JM-PI230970/PI635999-020-T14-S26	3.23
JM-PI230970/PI635999-020-T14-S36	3.28
5002T	3.33
JM-PI230970/PI635999-020-T14-S30	3.38
TGX 2014-19FM	3.50
JM-PI230970/PI635999-020-T14-S15	3.55
JM-PI230970/PI635999-020-T14-S18	3.60
Dundee	3.70
Lukanga	3.73
JM-PI230970/PI635999-020-T14-S37	3.73
SNKGM001	3.86
JM-SCS-1/PI605891B-020-T5-S4	4.02
Gazela	4.19
JM-PI230970/PI635999-020-T14-S33	4.23
TGX 2021-03FZ	4.25
JM-PI230970/PI635999-020-T14-S20	4.39
SC Squire	4.47
JM-PI230970/PI635999-020-T14-S16	4.52
JM-PI230970/PI635999-020-T14-S27	4.53
JM-PI230970/PI635999-020-T14-S45	4.62
JM-PI230970/PI635999-020-T14-S42	4.77
Gishama	4.84
PAWE3	4.88
S1150/5/22	4.94
SC STATUS	5.05
MAKSOY 4N	5.18
SNKGM004 (Figure 3)	5.37
Guda	5.40
S1146/5/25	5.42
JM-PI230970/PI635999-020-T14-S11	5.47
MAKSOY 6N	5.48
PI594796	5.51
JM-PI230970/Clark-63K-020-T15-S1	5.62
Afgat	5.66
JM-PI230970/PI635999-020-T14-S22	6.34
S1180/5/54	6.50




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time0 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=320155&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]0 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=320155&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=320155&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time0 seconds
R ServerBig Analytics Cloud Computing Center



Parameters (Session):
par1 = complete ; par2 = ALL ; par3 = FALSE ; par4 = FALSE ;
Parameters (R input):
par1 = complete ; par2 = ALL ; par3 = FALSE ; par4 = FALSE ;
R code (references can be found in the software module):
par3 <- as.logical(par3)
par4 <- as.logical(par4)
if (par3 == 'TRUE'){
dum = xlab
xlab = ylab
ylab = dum
}
x <- t(y)
hc <- hclust(dist(x),method=par1)
d <- as.dendrogram(hc)
str(d)
mysub <- paste('Method: ',par1)
bitmap(file='test1.png')
if (par4 == 'TRUE'){
plot(d,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8),type='t',center=T, sub=mysub)
} else {
plot(d,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8), sub=mysub)
}
dev.off()
if (par2 != 'ALL'){
if (par3 == 'TRUE'){
ylab = 'cluster'
} else {
xlab = 'cluster'
}
par2 <- as.numeric(par2)
memb <- cutree(hc, k = par2)
cent <- NULL
for(k in 1:par2){
cent <- rbind(cent, colMeans(x[memb == k, , drop = FALSE]))
}
hc1 <- hclust(dist(cent),method=par1, members = table(memb))
de <- as.dendrogram(hc1)
bitmap(file='test2.png')
if (par4 == 'TRUE'){
plot(de,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8),type='t',center=T, sub=mysub)
} else {
plot(de,main=main,ylab=ylab,xlab=xlab,horiz=par3, nodePar=list(pch = c(1,NA), cex=0.8, lab.cex = 0.8), sub=mysub)
}
dev.off()
str(de)
}
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Dendrogram',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Label',header=TRUE)
a<-table.element(a,'Height',header=TRUE)
a<-table.row.end(a)
num <- length(x[,1])-1
for (i in 1:num)
{
a<-table.row.start(a)
a<-table.element(a,hc$labels[i])
a<-table.element(a,hc$height[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable1.tab')
if (par2 != 'ALL'){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Summary of Cut Dendrogram',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Label',header=TRUE)
a<-table.element(a,'Height',header=TRUE)
a<-table.row.end(a)
num <- par2-1
for (i in 1:num)
{
a<-table.row.start(a)
a<-table.element(a,i)
a<-table.element(a,hc1$height[i])
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}