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Author's title

Author

*Unverified author*

R Software Module

rwasp_agglomerativehierarchicalclustering.wasp

Title produced by software

Agglomerative Nesting (Hierarchical Clustering)

Date of computation

Thu, 12 May 2011 19:30:06 +0000

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2011/May/12/t130523187730pox81vleg9u2f.htm/, Retrieved Fri, 10 May 2024 14:45:43 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=121526, Retrieved Fri, 10 May 2024 14:45:43 +0000

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Original text written by user:

Not sure why points 21 and 25 did not cluster first and strongest--though still learning.

IsPrivate?

No (this computation is public)

User-defined keywords

Establish regions of Ohio, USA counties for purposes of workforce administration

Estimated Impact

189

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

-       [Agglomerative Nesting (Hierarchical Clustering)] [Robert Schmidley,...] [2011-05-12 19:30:06] [d41d8cd98f00b204e9800998ecf8427e] [Current]

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Dataseries X:

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Histogram

Boxplots

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0.997491448	0.998175599	0.999087799	0.998859749	0.899201824	0.99977195	0.994070696	0.998175599	0.994754846	0.99977195	1	0.996807298	0.997491448	0.998631699	0.997947548	0.998403649	0.998859749	0.984264538	0.999087799	0.99977195	0.991334094	0.99977195	0.958038769	0.998175599	0.901254276	0.9995439	0.965792474	0.9995439	0.997719498	0.993842645	0.982668187	0.998631699	0.99977195	0.999315849	1	0.997263398	0.953021665	0.998631699	0.999315849	0.783808438	0.996807298	0.999315849	0.996807298	0.991562144	0.992930445	0.999315849	0.995438997	0.995210946	0.997719498	0.996579247	0.998175599	0.997035348	0.983580388	0.999315849	0.998631699	0.999307958	0.992702395	0.996123147	0.9995439	0.988825542	0.9995439	0.99977195	1	0.997491448	0.975142531	0.983124287	0.998403649	0.9995439	0.99977195	0.994298746	0.884150513	0.999315849	0.987913341	0.998631699	0.99977195	0.994754846	0.991562144	0.998175599	0.998175599	0.997263398	0.99977195	0	0.998859749	0.98722919	0.997491448	1	0.998859749	0.99977195
0.987255036	0.994121063	0.997022923	0.999334498	0.995232615	0.994759825	0.997601407	0.961422692	0.739735737	0.998370218	0.993965235	0.985338891	0.916312705	0.835403547	0.99876115	0.996943383	0.996883563	0.98326485	0.98801212	0.996576984	0.992876961	0.996275374	0.995015475	0.974643279	0.952264395	0.998422284	0.995747395	0.998839488	0.944606317	0.997845911	0.564697804	0.996433575	0.994738227	0.998562444	0.996057128	0.967078447	0.996618766	0.99823722	0.997837115	0.996093378	0.99838005	0.997895131	0.999036427	0.994073732	0.994625467	0.995854233	0.996677336	0.993498654	0.992775602	0.998774889	0.996185801	0.997250974	0.99853114	0.996703772	0.98334432	0.998154556	0.786540996	0.997172684	0.997610218	0.996447328	0.998234385	0.997725593	0.997020431	0.997925146	0.995031814	0.991656986	0.997905114	0.961414584	0.995573589	0.996860911	0.990896963	0.997296226	0.99065204	0.996754027	0.987568878	0.995890972	0.99209206	0.99899859	0.998348884	0.993313867	0.994849996	0.998859749	0	0.99612639	0.997513591	0.997324633	0.996606836	0.998395025
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0.99739619	0.996588765	0.868810162	0.995896071	0.997040934	0.998544396	0.995309418	0.999201425	0.994900754	0.989706639	0.997113808	0.996902524	0.997892874	0.996706915	0.994631648	0.975477596	0.993444737	0.93526908	0.997628771	0.996645444	0.994015761	0.994288907	0.995258966	0.996273744	0.957709975	0.998150264	0.996810546	0.996492676	0.997977159	0.988080707	0.99049686	0.994841421	0.995527493	0.98885894	0.997020941	0.998745846	0.997675402	0.782989171	0.990011404	0.998856598	0.995581955	0.981991674	0.993952548	0.997229277	0.994647899	0.998310984	0.982995491	0.989064014	0.998180114	0.993193982	0.99358006	0.871507438	0.998971798	0.998432941	0.998242145	0.998615917	0.992835771	0.997172684	0.994561187	0.992956441	0.996689473	0.99670211	0.998864926	0.994972468	0.997995293	0.998204668	0.990623288	0.998582839	0.997479405	0.982742636	0.997096445	0.99556581	0.998570766	0.996152921	0.998192638	0.888954444	0.869526739	0.994205403	0.970253695	0.996149126	0.998038094	0.997491448	0.997513591	0.996403077	0	0.997650897	0.996755025	0.997592537
0.999588872	0.986557912	0.999673736	0.999282219	0.999412724	0.997520392	0.999151713	0.999673736	0.993996737	0.99957111	0.999147261	0.997585644	0.997846656	0.999347471	0.998890701	0.999444251	0.997455139	0.984535073	0.998694943	0.83898131	0.997194127	0.995171289	0.997585644	0.998545851	0.973637847	0.854355628	0.999468424	0.99954323	0.995562806	0.999138364	0.985709625	0.987145188	0.998246076	0.999640611	0.931306405	0.999216966	0.999471682	0.99908646	0.997585644	0.999428299	0.998890701	0.99908646	0.99771615	0.999692142	0.997650897	0.998564437	0.996411093	0.913931485	0.999282219	0.997650897	0.996019576	0.99725938	0.999706228	0.994910277	0.996411093	1	0.991451876	0.999782514	0.998846312	0.998368679	0.999558596	0.996998369	0.956583428	0.999840396	0.999347471	0.998732707	0.998303426	0.998694943	0.991908646	0.995040783	0.999216966	0.994779772	0.999347471	0.996998369	0.998303426	0.99406199	0.994192496	0.998042414	0.998760196	0.999151713	0.991661898	1	0.997324633	0.99954323	0.997650897	0	0.975464927	0.998395025
0.998766616	0.984637345	0.99518706	0.997698472	0.997314921	0.991751577	0.997867917	0.99895571	0.993252232	0.998027106	0.995539521	0.996606836	0.997416569	0.998671212	0.998045369	0.997012852	0.993605932	0.962848715	0.995037984	0.966796741	0.994813858	0.972437768	0.996934585	0.998636735	0.960477356	0.931722975	0.997767383	0.998065814	0.996086294	0.997989517	0.983149858	0.876289645	0.99026572	0.998203055	0.910978708	0.998933969	0.998626374	0.997229917	0.994494475	0.997999047	0.996416474	0.99625801	0.99475602	0.998152852	0.99564287	0.995291227	0.991054387	0.341552758	0.997297744	0.995199445	0.992296073	0.993984846	0.998678026	0.992867178	0.997363217	0.999077278	0.987950414	0.998260113	0.994643593	0.998115539	0.998455087	0.884687553	0.990209989	0.999201979	0.997341585	0.998415883	0.996953865	0.997874259	0.982847658	0.993721822	0.998195087	0.917336602	0.998261743	0.923459165	0.99757549	0.993136556	0.985289865	0.99591278	0.998322675	0.996741568	0.989863484	0.998859749	0.996606836	0.997786509	0.996755025	0.975464927	0	0.972715418
0.999588872	0.980639984	0.99578694	0.998796268	0.999097201	0.995586318	0.99899689	0.999799378	0.996288494	0.999598756	0.996990671	0.995084763	0.997492226	0.999097201	0.998695957	0.998896579	0.927274551	0.980840606	0.997592537	0.996990671	0.989367038	0.986859264	0.995586318	0.999598756	0.955963487	0.998395025	0.99978737	0.997993781	0.996790049	0.999297823	0.988163306	0.817333735	0.950145451	0.999460916	0.997090982	0.999498445	0.999577346	0.998695957	0.989868593	0.999598756	0.998595647	0.998194403	0.99588725	0.999498445	0.993881031	0.994683519	0.994884141	0.962283078	0.998194403	0.996388805	0.900892768	0.995987561	1	0.996087872	0.998194403	1	0.991573879	0.999782514	0.993078543	0.998395025	0.999779298	0.99578694	0.997729852	0.999598756	0.997793159	0.999788784	0.998194403	0.999197512	0.993479787	0.990069215	0.998294714	0.974320393	0.999398134	0.857558431	0.997793159	0.990470458	0.990470458	0.997391915	0.998395025	0.995285385	0.997291604	0.99977195	0.998395025	0.998796268	0.997592537	0.998395025	0.972715418	0

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	56 seconds
R Server	'George Udny Yule' @ 216.218.223.82

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 56 seconds \tabularnewline
R Server & 'George Udny Yule' @ 216.218.223.82 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=121526&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]56 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 216.218.223.82[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=121526&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=121526&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 Output	view raw output of R engine
Computing time	56 seconds
R Server	'George Udny Yule' @ 216.218.223.82

Agglomerative Nesting (Hierarchical Clustering)
Agglomerative Coefficient	0.182552071384495

\begin{tabular}{lllllllll}
\hline
Agglomerative Nesting (Hierarchical Clustering) \tabularnewline
Agglomerative Coefficient & 0.182552071384495 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=121526&T=1

[TABLE]
[ROW][C]Agglomerative Nesting (Hierarchical Clustering)[/C][/ROW]
[ROW][C]Agglomerative Coefficient[/C][C]0.182552071384495[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=121526&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=121526&T=1

As an alternative you can also use a QR Code:

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

Agglomerative Nesting (Hierarchical Clustering)
Agglomerative Coefficient	0.182552071384495

Figure 1

PNG link

Postscript link

PDF link

Figure 2

PNG link

Postscript link

PDF link

Parameters (Session):

Parameters (R input):

par1 = euclidean ; par2 = single ; par3 = 1 ; par4 = 0.5 ; par5 = 0.5 ; par6 = 0.5 ; par7 = 0 ;

R code (references can be found in the software module):

par3 <- as.numeric(par3)
par4 <- as.numeric(par4)
par5 <- as.numeric(par5)
par6 <- as.numeric(par6)
par7 <- as.numeric(par7)
library(cluster)
if (par2 == 'flexible')
{
if (par3 == 1) pm <- c(par4)
if (par3 == 3) pm <- c(par4,par5,par6)
if (par3 == 4) pm <- c(par4,par5,par6,par7)
ag <- agnes(t(y),metric=par1,method=par2,par.method=pm)
} else {
ag <- agnes(t(y),metric=par1,method=par2)
}
mysub <- paste('Method: ',par2)
summary(ag)
bitmap(file='test1.png')
plot(ag,which.plots=2,main=main,sub=mysub,xlab=xlab,ylab=ylab)
dev.off()
bitmap(file='test2.png')
plot(ag,which.plots=1,main='Banner',sub=mysub,xlab=ylab,ylab=xlab)
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Agglomerative Nesting (Hierarchical Clustering)',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Agglomerative Coefficient',header=TRUE)
a<-table.element(a,ag$ac)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code