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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 computationMon, 10 Nov 2008 06:30:59 -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/10/t1226323913n0qqg8lqxucfj81.htm/, Retrieved Sun, 19 May 2024 08:46:25 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23042, Retrieved Sun, 19 May 2024 08:46:25 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact160

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 - Biv...] [2008-11-10 13:30:59] [6912578025c824de531bc660dd61b996] [Current]
-    D    [Bivariate Kernel Density Estimation] [Q1] [2008-11-11 16:54:29] [2b46c8b774ad566be9a33a8da3812a44]
F    D    [Bivariate Kernel Density Estimation] [Q1] [2008-11-11 16:58:31] [2b46c8b774ad566be9a33a8da3812a44]
F         [Bivariate Kernel Density Estimation] [] [2008-11-11 19:55:09] [4ddbf81f78ea7c738951638c7e93f6ee]
Feedback Forum
2008-11-24 13:35:00 [Ellen Smolders] [reply] 
De student heeft geen informatie gegeven. Met de Triviate scatterplot kunnen we een driedimensionaal inzicht krijgen in de structuur van 3 datareeksen. Wanneer we een dimensie zouden weglaten zouden twee punten op de kubus een vertekend beeld geven, daarom is de triviate scatterplot zeer nuttig. Wanneer we de Kernel Density Plot gebruiken krijgen we nog meer informatie dan bij de triviate scatterplot of correlatie. De Kernel Density Plot verbindt punten met een gelijke dichtheid, dus waar de intensiteit hoog of laag is. De dichtheid is het gebied rond de punten waar de waarschijnlijkheid groot is dat je een observatie gaat vinden.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
356,2
359,5
368,4
371
397,5
416,7
413,2
424,3
415
421,7
422,1
429,2
452,1
471,5
488,3
506,2
517,3
538,6
545,3
546,7
540,3
549,2
563,9
581,7
590,7
594,1
604
628,1
662,4
688,6
705,9
701,5
686,2
645,7
668,7
696,7
715,5
741,4
754,3
771,3
797,7
809,9
790,1
830,3
847,7
834,8
824,5
764,6
780
803,2
751,1
755,2
708,2
685,4
680
710,6
702,8
656,3
575,6
567,2
545,2
Dataseries Y:
Download CSV
Histogram 
152823,6
123780,5
159987,1
139603,7
177831,2
173656,9
252392
228029
197300
214088
160275
186851
227777
246899
295338
243847
324602
347066
407916
312914
326127
394369
310078
422770
417974
402347
360809
298289
375873
407210
413968
457532
695731
544623
292833
534403
517030
455714
471401
451493
480615
568272
650780
553643
780711
650724
586345
725173
701257
859063
789842
512707
780845
637804
640694
553416
554622
616736
536994
407237
618796

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=23042&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=23042&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23042&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 axis62.7153974197359
y axis97780.9673643169
Correlation
correlation used in KDE0.848324077478414
correlation(x,y)0.848324077478414

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 62.7153974197359 \tabularnewline
y axis & 97780.9673643169 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.848324077478414 \tabularnewline
correlation(x,y) & 0.848324077478414 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23042&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]62.7153974197359[/C][/ROW]
[ROW][C]y axis[/C][C]97780.9673643169[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.848324077478414[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.848324077478414[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23042&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23042&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 axis62.7153974197359
y axis97780.9673643169
Correlation
correlation used in KDE0.848324077478414
correlation(x,y)0.848324077478414


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')