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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 10:11:38 -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/t12264235841239nqp4oc0bwyu.htm/, Retrieved Sun, 19 May 2024 10:24:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23739, Retrieved Sun, 19 May 2024 10:24:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111

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] [Various EDA topic...] [2008-11-11 17:11:38] [e4cb5a8878d0401c2e8d19a1768b515b] [Current]
Feedback Forum
2008-11-22 17:16:25 [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.

Je ziet inderdaad op de grafiek dat alle punten in de buurt van de rechte lijn liggen en dat er verschillende clusters gevormd worden. De clusters hebben hier dan ook eenzelfde oriëntatie dus kunnen we besluiten dat er correlatie is. Als we nu zien in de wiskundig berekende tabel, kan je zien dat er een correlatie van 0.927 is.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101,5
101,3
99,3
100,6
101,2
99,8
100,6
101,1
101,2
101,5
102,2
102,5
101,4
103,8
105,2
105,3
104,4
104,9
106,9
107,6
106,7
106,1
106,3
105,8
104,4
103,8
102,4
103,3
103,5
104,5
103,5
103,9
103,1
102,2
104,7
105,9
106,6
106,6
107,5
107,2
109
108,4
107
108
110,8
110,9
109,7
111
111,5
111
111,8
111,4
110,8
111,9
112,9
111,8
111
112,3
112,4
111,1
Dataseries Y:
Download CSV
Histogram 
110,4
112,9
109,4
111,9
108,9
113,8
114,5
113,2
111
114,6
113,1
113,2
115,1
117,6
117,8
115,7
115,7
118,3
117,9
117,3
119,4
117,1
119
120
118,9
116
115,6
119,7
119,7
120,8
120
120,2
121,7
116,9
122,4
122,6
123,7
120,9
124,2
122,6
125,7
123,1
122,2
126,2
124,4
127,8
124,2
126,7
126,1
128,2
130,4
130,2
129,2
129,7
131
129,2
131,1
132,9
135,2
132,3

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'Gwilym Jenkins' @ 72.249.127.135

\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 & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23739&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23739&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23739&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'Gwilym Jenkins' @ 72.249.127.135






Bandwidth
x axis1.45267803958993
y axis3.29737113802873
Correlation
correlation used in KDE0.9270237189657
correlation(x,y)0.9270237189657

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 1.45267803958993 \tabularnewline
y axis & 3.29737113802873 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.9270237189657 \tabularnewline
correlation(x,y) & 0.9270237189657 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23739&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]1.45267803958993[/C][/ROW]
[ROW][C]y axis[/C][C]3.29737113802873[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.9270237189657[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.9270237189657[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23739&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23739&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 axis1.45267803958993
y axis3.29737113802873
Correlation
correlation used in KDE0.9270237189657
correlation(x,y)0.9270237189657


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')