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Author's title

Author*Unverified author*
R Software Modulerwasp_hierarchicalclustering.wasp
Title produced by softwareHierarchical Clustering
Date of computationSat, 07 Nov 2020 18:01:29 +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/2020/Nov/07/t1604768578g48onsuv4zxsfh4.htm/, Retrieved Wed, 21 Apr 2021 08:53:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=319288, Retrieved Wed, 21 Apr 2021 08:53:22 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordscytotoxicity
Estimated Impact21
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Hierarchical Clustering] [] [2020-11-07 17:01:29] [d41d8cd98f00b204e9800998ecf8427e] [Current]
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Dataseries X:
Acanthus ebracteatus Vahl	>50
Cordia globifera W.W. Sm.	2.85 ± 0.86
Eupatorium capillifolium (Lam.) Small ex Porter & Britton	22.56 ±1.22
Hydnophytum formicarum Jack	>50
Levisticum officinale W.D.J. Koch	25.54 ± 2.60
Orthosiphon aristatus (Blume) Mig.	31.95± 1.31
Polyalthia cerasoides (Roxb.)	>50
Rhinacanthus nasutus (L.) Kurz	>50
Salacia chinensis L.	>50
Smilax corbularia Kunth	>50
Smilax glabra Roxb.	>50
Dioscorea membranacea Pierre ex Prain & Burkill	7.65 ± 0.39
Andrographis paniculata (Burm.f.) Wall. Ex Nees 	38.65± 0.55
Bauhinia strychnifolia Craib.	>50
Betula alnoides Buch. Ham.	>50
Caesalpinia sappan Linn.	13.68± 2.20
Coptosapelta flavescens Korth.	>50
Coscinium fenestratum (Gaertn.) Colebr.	10.32± 1.53
Ficus foveolata Wall.	>50
Litsea cubeba (Lour).	>50
Ochna integerrima (Lour.) Merr.	>50
Suregada multiflorum (A.Juss) Baill	>50
Baliospermum montanum Muell-Arg.	4.49 ± 2.59
Cassia garrettiana (Craib) Irwin & Barneby	8.76 ± 0.31
Croton oblongifolius Roxb. 	3.65 ± 0.20
Harrisonia perforata (Blanco) Merr.	30.81± 1.45
Maytenus marcanii	>50
Phyllanthus amarus Schum & Thonn.	22.89± 1.10
Phyllanthus reticulatus Poir.	>50
Tiliacora triandra (Colebr.) Diels	33.18± 2.02
Tinospora baenzigeri Forman	>50
Tinospora crispa (L.) Hook. f., & Thomson	>50
Piper interruptum Opiz.	26.06± 1.71
Piper retrofractum Vahl.	10.24± 2.23
Piper sarmentosum Roxb.	9.51 ± 0.57
Plumbago indica L.	21.50± 0.79
Zingiber officinale Roscoe.	15.14 ± 1.26




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=319288&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]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=319288&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=319288&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 time1 seconds
R ServerBig Analytics Cloud Computing Center



Parameters (Session):
par1 = ward ; par2 = ALL ; par3 = FALSE ; par4 = FALSE ;
Parameters (R input):
par1 = ward ; 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')
}