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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 05:39:03 -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/t1226408127u0s200nhvfd8bxc.htm/, Retrieved Sun, 19 May 2024 09:19:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23417, Retrieved Sun, 19 May 2024 09:19:54 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123

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] [Bivariate Kernell...] [2008-11-11 12:39:03] [02e7fb326979b65614900650d62c19a6] [Current]
Feedback Forum
2008-11-24 17:21:53 [Stefanie Mertens] [reply] 
Bij de bivariatie kernal density ga je een op een andere manier de correlatie bekijken tussen twee variabelen. in deze grafiek zie je enkele hoogtelijnen. dit is een voordeel aangezien je nu ook de punten ziet die in de andere technieken niet te zien zijn, namelijk deze punten die op elkaar liggen. de kleuren geven weer wat de sterkte is van de correlatie. Een rode kleur geeft weer dat er een sterke correlatie is tussen de twee variabelen, een gele kleur geeft een lage correaltie weer. jij zegt dat er geen correlatie is tussen de invoer van nederland en die van frankrijk. maar je ziet toch duidelijk de puntenwolk op een rechte liggen en je zie in de tabel boven de grafiek dat er sprake is van 71%

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2333,3
2282,2
2458,2
2345,5
2065,2
2332,5
2077,5
1691,4
2381,9
2526
2212,1
2459,9
2178,8
2318,2
2661,8
2407,9
2040,6
2601,6
2106,3
1829,9
2546,1
2363
2435,8
2668
2316,9
2324,2
2610,8
2413,2
2345,2
2590,8
2132,1
1990,7
2641,7
2437,1
2649,2
2819,4
2405,6
2451,3
2878,5
2534,1
2670,6
2909,7
2261,8
2135,3
2870,4
2803,2
2775,1
2633,7
2930,6
2779,7
3039,2
2752,7
2743,1
2914
2711,9
2295,8
2840,6
3230,5
2761,1
2769,6
Dataseries Y:
Download CSV
Histogram 
2894,3
2838,1
3137,7
2703,7
2623,6
2691,1
2577,9
2430,5
2871
2922,5
2810,8
3070,3
2790
2821
3383,6
3038,4
2877,3
3283,7
2927,3
2952,5
3328,9
3467,3
3355,6
3707
3275,6
3466,5
4054,3
3708,5
3339
3559,8
3189,2
3620,7
3915,4
3804,3
4391,6
4975,9
4478,7
4455,8
5661,8
4062,8
4257,7
4114,2
3793,8
4170
4004,9
4129,7
4116
4133,8
4081,2
3854,1
4239,8
3718,5
4183,1
4336,1
4299,2
4285,3
4676,7
4980,6
5207,4
5221,7

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=23417&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=23417&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23417&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 axis143.379881804186
y axis276.724585277440
Correlation
correlation used in KDE0.715080971334587
correlation(x,y)0.715080971334587

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 143.379881804186 \tabularnewline
y axis & 276.724585277440 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.715080971334587 \tabularnewline
correlation(x,y) & 0.715080971334587 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23417&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]143.379881804186[/C][/ROW]
[ROW][C]y axis[/C][C]276.724585277440[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.715080971334587[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.715080971334587[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23417&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23417&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 axis143.379881804186
y axis276.724585277440
Correlation
correlation used in KDE0.715080971334587
correlation(x,y)0.715080971334587


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')