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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 computationThu, 13 Nov 2008 11:57:05 -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/13/t1226602731mflblz1ox57v9l6.htm/, Retrieved Mon, 20 May 2024 12:26:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24775, Retrieved Mon, 20 May 2024 12:26:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact143

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Hierarchical Clustering] [Hierarchical clus...] [2008-11-13 17:54:36] [5262baed313b307078ce11eb68e9efe6]
F RMPD    [Bivariate Kernel Density Estimation] [Bivariate density] [2008-11-13 18:57:05] [5bd06487453d0eec7a1bf04bf9f25085] [Current]
Feedback Forum
2008-11-20 15:43:28 [Gert-Jan Geudens] [reply] 
Ook hier heeft de studente geen conclusie gegeven. We zien net zoals bij de vorige plot een positief verband. Bij deze plot kunnen we extra opmerken dat de meeste gegevens zich rond de coördinaten (450,350) bevinden.
2008-11-20 20:37:49 [Gilliam Schoorel] [reply] 
Hier zijn de hoogtelijnen ook ellipsvormig en liggen schuin naar boven wat wijst op een positief verband. De meeste variabelen zijn duidelijk rond een punt georiënteerd. Er zijn geen twee duidelijke clusters te onderscheiden dus het gaat weldegelijk om één waarneming
2008-11-24 19:04:29 [Sören Van Donink] [reply] 
Ook hier werd geen conclusie gegeven. Echter zien we ook hier een positief verband. En de dicht op elkaar liggende hoogtelijnen duiden op een sterk geconcentreerd resultaat in de linker benedenhoek.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
493
481
462
457
442
439
488
521
501
485
464
460
467
460
448
443
436
431
484
510
513
503
471
471
476
475
470
461
455
456
517
525
523
519
509
512
519
517
510
509
501
507
569
580
578
565
547
555
562
561
555
544
537
543
594
611
613
611
594
595
591
589
584
573
Dataseries Y:
Download CSV
Histogram 
377
370
358
357
349
348
369
381
368
361
351
351
358
354
347
345
343
340
362
370
373
371
354
357
363
364
363
358
357
357
380
378
376
380
379
384
392
394
392
396
392
396
419
421
420
418
410
418
426
428
430
424
423
427
441
449
452
462
455
461
461
463
462
456

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'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24775&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24775&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24775&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'George Udny Yule' @ 72.249.76.132






Bandwidth
x axis21.8832264871429
y axis11.4166019197183
Correlation
correlation used in KDE0.958779844345424
correlation(x,y)0.958779844345424

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 21.8832264871429 \tabularnewline
y axis & 11.4166019197183 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.958779844345424 \tabularnewline
correlation(x,y) & 0.958779844345424 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24775&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]21.8832264871429[/C][/ROW]
[ROW][C]y axis[/C][C]11.4166019197183[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.958779844345424[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.958779844345424[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24775&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24775&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 axis21.8832264871429
y axis11.4166019197183
Correlation
correlation used in KDE0.958779844345424
correlation(x,y)0.958779844345424


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')