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Author's title

Author*Unverified author*
R Software Modulerwasp_hierarchicalclustering.wasp
Title produced by softwareHierarchical Clustering
Date of computationTue, 19 Feb 2019 06:32:21 +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/2019/Feb/19/t1550554556w13d2617tw58ozl.htm/, Retrieved Sat, 04 May 2024 15:44:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=318730, Retrieved Sat, 04 May 2024 15:44:41 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsclustering
Estimated Impact108
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Hierarchical Clustering] [data] [2019-02-19 05:32:21] [03c7c0ace395d80182db07ae2c30f034] [Current]
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Dataseries X:
	kk1_1_R1_R2	kk1_2_R1_R2	7kk_1_R1_R2	7kk_2_R1_R2
GG2335|c0_g1	0.000	0.000	1.047	0.313
GG1949|c10_g1	0.560	0.941	0.000	0.865
GG19775|c2_g1	0.000	0.861	0.000	0.000
GG10182|c7_g1	0.000	0.367	0.834	0.928
GG20880|c5_g1	0.704	0.000	0.232	0.000
GG18785|c0_g1	170.253	277.297	292.272	306.461
GG1852|c0_g1	115.506	101.047	108.864	82.721
GG18116|c0_g2	6.156	8.653	5.547	7.213
GG15867|c1_g2	0.415	0.597	0.167	0.365
GG18183|c0_g2	2.248	0.734	1.667	2.126
GG15403|c6_g1	3.629	0.000	6.186	4.826
GG18453|c1_g1	1.065	0.000	0.000	0.000
GG19167|c11_g1	0.000	0.000	1.130	0.000
GG15276|c1_g1	6.969	13.014	2.741	9.808
GG15205|c0_g2	7.889	9.181	8.409	9.642
GG5606|c6_g1	1.390	0.000	0.194	0.292
GG4136|c2_g1	27.324	37.125	29.339	34.887
GG1315|c0_g1	2.103	0.712	1.250	0.000
GG18466|c0_g1	1.760	0.505	0.954	0.771
GG1210|c4_g1	0.298	1.056	0.593	1.303
GG13717|c0_g1	0.370	0.000	0.000	0.552
GG12208|c1_g2	1.128	0.000	1.676	3.513
GG15480|c0_g1	470.318	544.232	513.622	599.548
GG4007|c1_g1	145.556	87.218	99.474	136.923
GG17158|c1_g1	1.507	1.687	1.343	1.584
GG14355|c1_g2	1.110	0.000	0.157	0.000
GG7780|c0_g1	0.343	0.585	0.000	0.490
GG10134|c0_g1	2.564	7.184	5.446	3.669
GG9796|c1_g1	2.049	0.000	0.000	0.208
GG16111|c0_g4	0.803	0.264	0.519	0.521
GG19236|c0_g1	239.912	378.482	258.709	370.346
GG15049|c0_g1	30.826	29.207	37.989	33.147
GG6824|c0_g1	1.128	1.400	1.482	1.564
GG16396|c1_g1	0.505	0.608	0.407	0.719
GG11748|c9_g1	1.480	0.000	1.102	0.000
GG8281|c3_g1	0.912	0.000	0.380	0.188




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=318730&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=318730&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=318730&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 = complete ; par2 = ALL ; par3 = TRUE ; par4 = TRUE ;
Parameters (R input):
par1 = complete ; par2 = ALL ; par3 = TRUE ; par4 = TRUE ;
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')
}