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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, 04 Nov 2008 12:00:29 -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/04/t12258253080u0gq17c9wemfhr.htm/, Retrieved Sun, 19 May 2024 04:24:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21627, Retrieved Sun, 19 May 2024 04:24:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171

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...] [2008-11-04 19:00:29] [3817f5e632a8bfeb1be7b5e8c86bd450] [Current]
F         [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2008-11-11 15:54:56] [73d6180dc45497329efd1b6934a84aba]
F           [Bivariate Kernel Density Estimation] [Bivariate Density] [2008-11-11 17:20:05] [6816386b1f3c2f6c0c9f2aa1e5bc9362]
F             [Bivariate Kernel Density Estimation] [Q1 bivariate kernel] [2008-11-11 19:28:24] [a7a7b7de998247cdf0f65ef79d563d66]
-             [Bivariate Kernel Density Estimation] [] [2008-11-20 16:18:02] [888addc516c3b812dd7be4bd54caa358]
Feedback Forum
2008-11-18 19:15:18 [Glenn De Maeyer] [reply] 
De bivariate kernel density verglijkt punten van gelijke dichtheid met elkaar. De punten in de rode zone zijn de punten met de hoogste correlatie. Op basis van de correlatiecoëficiënt en de bivariate kernel density kunnen we stellen dat de vergeleken tijdreeksen (uitvoer Vlaanderen vs de uitvoer van België naar landen buiten de EU) sterk met elkaar gecorreleerd zijn.
2008-11-24 17:36:52 [Jan Cavents] [reply] 
op deze manier kunnen we grafisch de verbanden achterhalen tussen de reeksen. daar waar de punten dicht bij elkaar liggen is er een groot verband, waar ze verder van elkaar liggen daalt de correlatie.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
12300.00
12092.80
12380.80
12196.90
9455.00
13168.00
13427.90
11980.50
11884.80
11691.70
12233.80
14341.40
13130.70
12421.10
14285.80
12864.60
11160.20
14316.20
14388.70
14013.90
13419.00
12769.60
13315.50
15332.90
14243.00
13824.40
14962.90
13202.90
12199.00
15508.90
14199.80
15169.60
14058.00
13786.20
14147.90
16541.70
13587.50
15582.40
15802.80
14130.50
12923.20
15612.20
16033.70
16036.60
14037.80
15330.60
15038.30
17401.80
14992.50
16043.70
16929.60
15921.30
14417.20
15961.00
17851.90
16483.90
14215.50
17429.70
17839.50
17629.20
Dataseries Y:
Download CSV
Histogram 
3423.40
3242.80
3277.20
3833.00
2606.30
3643.80
3686.40
3281.60
3669.30
3191.50
3512.70
3970.70
3601.20
3610.00
4172.10
3956.20
3142.70
3884.30
3892.20
3613.00
3730.50
3481.30
3649.50
4215.20
4066.60
4196.80
4536.60
4441.60
3548.30
4735.90
4130.60
4356.20
4159.60
3988.00
4167.80
4902.20
3909.40
4697.60
4308.90
4420.40
3544.20
4433.00
4479.70
4533.20
4237.50
4207.40
4394.00
5148.40
4202.20
4682.50
4884.30
5288.90
4505.20
4611.50
5081.10
4523.10
4412.80
4647.40
4778.60
4495.30

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21627&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]3 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=21627&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21627&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 time3 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Bandwidth
x axis932.065309826864
y axis255.986090575248
Correlation
correlation used in KDE0.902863979754093
correlation(x,y)0.902863979754093

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 932.065309826864 \tabularnewline
y axis & 255.986090575248 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.902863979754093 \tabularnewline
correlation(x,y) & 0.902863979754093 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21627&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]932.065309826864[/C][/ROW]
[ROW][C]y axis[/C][C]255.986090575248[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.902863979754093[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.902863979754093[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21627&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21627&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 axis932.065309826864
y axis255.986090575248
Correlation
correlation used in KDE0.902863979754093
correlation(x,y)0.902863979754093


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')