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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 computationTue, 11 Nov 2008 07:13:42 -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/11/t1226412866i91q49i0ipbpqse.htm/, Retrieved Mon, 20 May 2024 11:10:47 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23498, Retrieved Mon, 20 May 2024 11:10:47 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact168

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Notched Boxplots] [workshop 3] [2007-10-26 13:31:48] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F RMPD  [Mean Plot] [workshop 4 deel 1...] [2008-10-31 09:40:26] [077ffec662d24c06be4c491541a44245]
F         [Mean Plot] [] [2008-11-01 13:19:15] [4c8dfb519edec2da3492d7e6be9a5685]
F    D      [Mean Plot] [] [2008-11-01 14:24:03] [4c8dfb519edec2da3492d7e6be9a5685]
F RMPD        [Star Plot] [Star Plot - Bob L...] [2008-11-02 16:44:14] [57850c80fd59ccfb28f882be994e814e]
F RMPD            [Bivariate Kernel Density Estimation] [Q1-1] [2008-11-11 14:13:42] [541f63fa3157af9df10fc4d202b2a90b] [Current]
Feedback Forum
2008-11-22 11:10:30 [Kenny Simons] [reply] 
Met de techniek van de Bivariate Kernel Density kan je net zoals bij een scatter plot, de correlatie meten tussen 2 variabelen. De grafische voorstelling echter is anders.
Bij deze techniek zijn de hoogtelijnen de derde dimensie, hier kan je de clusters dan ook veel beter zien. Als alle clusters een zelfde oriëntatie hebben, dan is er een verband tussen de variabelen. De rode zone in de grafiek duidt een sterke correlatie aan en de groene zone duidt een zwakke correlatie aan.

In de bekomen grafiek zie je inderdaad dat de clusters grotendeelseen zelfde oriëntatie hebben en dat er wel degelijk een verband is tussen de variabelen. Als je nu gaat zien in de wiskundig berekende tabel, zal je zien dat er een correlatie is van 0.74.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
81,6
84,1
88,1
85,3
82,9
84,8
71,2
68,9
94,3
97,6
85,6
91,9
75,8
79,8
99
88,5
86,7
97,9
94,3
72,9
91,8
93,2
86,5
98,9
77,2
79,4
90,4
81,4
85,8
103,6
73,6
75,7
99,2
88,7
94,6
98,7
84,2
87,7
103,3
88,2
93,4
106,3
73,1
78,6
101,6
101,4
98,5
99
89,5
83,5
97,4
87,8
90,4
101,6
80
81,7
96,4
110,2
101,1
89,3
Dataseries Y:
Download CSV
Histogram 
91,2
99,2
108,2
101,5
106,9
104,4
77,9
60
99,5
95
105,6
102,5
93,3
97,3
127
111,7
96,4
133
72,2
95,8
124,1
127,6
110,7
104,6
112,7
115,3
139,4
119
97,4
154
81,5
88,8
127,7
105,1
114,9
106,4
104,5
121,6
141,4
99
126,7
134,1
81,3
88,6
132,7
132,9
134,4
103,7
119,7
115
132,9
108,5
113,9
142
97,7
92,2
128,8
134,9
128,2
114,8

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23498&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23498&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23498&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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132






Bandwidth
x axis4.7858020196429
y axis7.85113635870872
Correlation
correlation used in KDE0.742165988275247
correlation(x,y)0.742165988275247

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 4.7858020196429 \tabularnewline
y axis & 7.85113635870872 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.742165988275247 \tabularnewline
correlation(x,y) & 0.742165988275247 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23498&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]4.7858020196429[/C][/ROW]
[ROW][C]y axis[/C][C]7.85113635870872[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.742165988275247[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.742165988275247[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23498&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23498&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 axis4.7858020196429
y axis7.85113635870872
Correlation
correlation used in KDE0.742165988275247
correlation(x,y)0.742165988275247


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')