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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_bidensity.wasp
Title produced by softwareBivariate Kernel Density Estimation
Date of computationSun, 09 Nov 2008 10:45:07 -0700
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/09/t12262527352a4f914yz0zgyba.htm/, Retrieved Sun, 19 May 2024 09:40:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22795, Retrieved Sun, 19 May 2024 09:40:04 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact148

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Bivariate Kernel Density Estimation] [Werkloosheid - Ni...] [2008-11-09 17:45:07] [54ae75b68e6a45c6d55fa4235827d5b3] [Current]
Feedback Forum
2008-11-22 10:00:58 [Astrid Sniekers] [reply] 
Werkloosheid – niet-duurzame consumptiegoederen

Er zijn duidelijk twee clusters te onderscheiden. De correlatie is 0.692158324661321. Er is een klein positief verband tussen de twee datasets.
2008-11-24 19:08:26 [Dorien Peeters] [reply] 
De correlatie is 0.692158324661321. Als je naar de grafiek kijkt zie je eigenlijk twee blokken, clusters. Je kan een positief verband zien tussen de twee datasets.
2008-11-24 19:09:28 [Liese Tormans] [reply] 
De Bivariate Density gaat met behulp van hoogtelijnen de punten van gelijkaardige observaties verbinden. De grafiek bestaat uit verschillende kleuren die intensiteit weergeven.
Een rode kleur geeft een sterke intensiteit weer terwijl de gele en groene kleur wijzen op een eerder zwakke correlatie.
Ook is het mogelijk om aan de hand van deze grafiek de clusters af te lezen.

Het voordeel hier is dat we meer info krijgen dan bij de scatter plot of gewone correlatie.
Want bij de andere grafieken is niet echt rekening gehouden met de invloed van de derde variabele.

Op deze grafiek is er duidelijk een sterk verband zichtbaar tussen de dataset werkloosheid en niet duurzame consumptie goederen. De correlatie is 0.69 we kunnen dus spreken van een matig tot sterk positief verband. Dit is ook zichtbaar op de grafiek de punten liggen redelijk dicht bij de rechte. Daarnaast zijn er 2 grote clusters zichtbaar.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
467
460
448
443
436
431
484
510
513
503
471
471
476
475
470
461
455
456
517
525
523
519
509
512
519
517
510
509
501
507
569
580
578
565
547
555
562
561
555
544
537
543
594
611
613
611
594
595
591
589
584
573
567
569
621
629
628
612
595
597
593
590
580
574
573
573
620
626
620
588
566
557
Dataseries Y:
Download CSV
Histogram 
98.6
98
106.8
96.7
100.2
107.7
92
98.4
107.4
117.7
105.7
97.5
99.9
98.2
104.5
100.8
101.5
103.9
99.6
98.4
112.7
118.4
108.1
105.4
114.6
106.9
115.9
109.8
101.8
114.2
110.8
108.4
127.5
128.6
116.6
127.4
105
108.3
125
111.6
106.5
130.3
115
116.1
134
126.5
125.8
136.4
114.9
110.9
125.5
116.8
116.8
125.5
104.2
115.1
132.8
123.3
124.8
122
117.4
117.9
137.4
114.6
124.7
129.6
109.4
120.9
134.9
136.3
133.2
127.2

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 2 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22795&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]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22795&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22795&T=0

As an alternative you can also use a QR Code:  
QR Code


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

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Bandwidth
x axis21.3901917936107
y axis5.57686862052402
Correlation
correlation used in KDE0.692158324661321
correlation(x,y)0.692158324661321

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 21.3901917936107 \tabularnewline
y axis & 5.57686862052402 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.692158324661321 \tabularnewline
correlation(x,y) & 0.692158324661321 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22795&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]21.3901917936107[/C][/ROW]
[ROW][C]y axis[/C][C]5.57686862052402[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.692158324661321[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.692158324661321[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22795&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22795&T=1

As an alternative you can also use a QR Code:  
QR Code


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

Bandwidth
x axis21.3901917936107
y axis5.57686862052402
Correlation
correlation used in KDE0.692158324661321
correlation(x,y)0.692158324661321


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ;
R code (references can be found in the software module):
par1 <- as(par1,'numeric')
par2 <- as(par2,'numeric')
par3 <- as(par3,'numeric')
par4 <- as(par4,'numeric')
par5 <- as(par5,'numeric')
library('GenKern')
if (par3==0) par3 <- dpik(x)
if (par4==0) par4 <- dpik(y)
if (par5==0) par5 <- cor(x,y)
if (par1 > 500) par1 <- 500
if (par2 > 500) par2 <- 500
bitmap(file='bidensity.png')
op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=par5, xbandwidth=par3, ybandwidth=par4)
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main=main,xlab=xlab,ylab=ylab)
if (par6=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par7=='Y') points(x,y)
(r<-lm(y ~ x))
abline(r)
box()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Bandwidth',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'x axis',header=TRUE)
a<-table.element(a,par3)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'y axis',header=TRUE)
a<-table.element(a,par4)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Correlation',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'correlation used in KDE',header=TRUE)
a<-table.element(a,par5)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'correlation(x,y)',header=TRUE)
a<-table.element(a,cor(x,y))
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')