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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 computationWed, 12 Nov 2008 03:54:53 -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/12/t1226487388r80zkp09ky5612p.htm/, Retrieved Mon, 20 May 2024 07:45:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24110, Retrieved Mon, 20 May 2024 07:45:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162

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] [W3Q1] [2008-11-12 10:54:53] [434228f9e3c7eaa307f0fb12855e2147] [Current]
Feedback Forum
2008-11-22 12:26:37 [Sandra Hofmans] [reply] 
Juiste grafiek bekomen, maar onvolledige conclusie. De Bivariate Kernel Density plot schat de afhankelijkheid van de data door middel van de dichtheid. Bij een bivariate density plot worden er hoogtelijnen getekend zodat er meer dimensies ontstaan. Het lichtblauwe oppervlakte geeft een hoge concentratie weer; de meeste variabelen bevinden zich waarschijnlijk daar.
Er zijn ellipsen waar te nemen, die duiden op een positief verband.
2008-11-23 15:48:56 [Peter Van Doninck] [reply] 
De berekeningen van de student tonen aan dat de correlatie 0,84 bedraagt. Dit is zeer hoog (ligt dicht tegen 1). Wel geeft hij niet echt een duidelijke conclusie over de verkregen bivariate Kernel density plot. Deze bevat wel 4 verschillende 'concentraties'. Echter de meest linkse concentratie is de hoogste. Hier ontstaat er ook een ellips, waaruit we kunnen afleiden dat er een verband is tussen beide variabelen. Dit had de student min of meer nog moeten toevoegen. Een duidelijke conclusie ontbreekt echter.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
118,4
121,4
128,8
131,7
141,7
142,9
139,4
134,7
125,0
113,6
111,5
108,5
112,3
116,6
115,5
120,1
132,9
128,1
129,3
132,5
131,0
124,9
120,8
122,0
122,1
127,4
135,2
137,3
135,0
136,0
138,4
134,7
138,4
133,9
133,6
141,2
151,8
155,4
156,6
161,6
160,7
156,0
159,5
168,7
169,9
169,9
185,9
190,8
195,8
211,9
227,1
251,3
256,7
251,9
251,2
270,3
267,2
243,0
229,9
187,2
Dataseries Y:
Download CSV
Histogram 
111,4
114,1
121,8
127,6
129,9
128,0
123,5
124,0
127,4
127,6
128,4
131,4
135,1
134,0
144,5
147,3
150,9
148,7
141,4
138,9
139,8
145,6
147,9
148,5
151,1
157,5
167,5
172,3
173,5
187,5
205,5
195,1
204,5
204,5
201,7
207,0
206,6
210,6
211,1
215,0
223,9
238,2
238,9
229,6
232,2
222,1
221,6
227,3
221,0
213,6
243,4
253,8
265,3
268,2
268,5
266,9
268,4
250,8
231,2
192,0

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=24110&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=24110&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24110&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 axis9.5053394905359
y axis13.9743315810296
Correlation
correlation used in KDE0.841656312058416
correlation(x,y)0.841656312058416

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 9.5053394905359 \tabularnewline
y axis & 13.9743315810296 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.841656312058416 \tabularnewline
correlation(x,y) & 0.841656312058416 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24110&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]9.5053394905359[/C][/ROW]
[ROW][C]y axis[/C][C]13.9743315810296[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.841656312058416[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.841656312058416[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24110&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24110&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 axis9.5053394905359
y axis13.9743315810296
Correlation
correlation used in KDE0.841656312058416
correlation(x,y)0.841656312058416


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')