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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 computationSun, 09 Nov 2008 10:47:04 -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/09/t1226252858c8bfxhalij0qy5o.htm/, Retrieved Sun, 19 May 2024 10:49:55 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22796, Retrieved Sun, 19 May 2024 10:49:55 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact128

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] [] [2008-11-09 17:47:04] [6d40a467de0f28bd2350f82ac9522c51] [Current]
F RMPD    [Partial Correlation] [] [2008-11-09 17:56:39] [4c8dfb519edec2da3492d7e6be9a5685]
F RMPD      [Trivariate Scatterplots] [] [2008-11-09 18:03:24] [4c8dfb519edec2da3492d7e6be9a5685]
Feedback Forum
2008-11-19 14:18:07 [2df1bcd103d52957f4a39bd4617794c8] [reply] 
Student gebruikt allereerst correcte techniek om Q1 op te lossen.

De Bivariate Kernel Density methode geeft het verband, de correlatie weer tussen 2 verschillende datareeksen. Een groene zone duidt op een lage correlatie, een rode zone daarentegen op een hogere correlatiewaarde.

In dit specifieke geval de uitvoer van de BLEU Levende dieren en producten van het dierenrijk ten opzichte van uitvoer Producten van het plantenrijk.

Visueel nemen we waar dat de hoogtelijnen niet dicht bij elkaar liggen. Deze verbinden de punten met eenzelfde dichtheid. Ze liggen ook niet sterk rond de rechte, wat duidt op een niet al te grote correlatie.

Dit klopt ook wanneer we de cijfergegevens bekijken. Er is een positieve samenhang tussen de twee datareeksen, deze is echter niet bijzonder groot 0.659021971841534.
2008-11-22 12:31:52 [Jeroen Michel] [reply] 
Er is inderdaad een goede, ruime bespreking gegeven van de verschillende onderdelen. De uitwerking en anlyse zijn correct.

Het is zo dat er clusters worden weergegeven. In het midden van de clusters zal op te merken zijn dat de intensiteit zeer dicht is, terwijl, hoe verder van het midden van de cluster, de intensiteit sterk zal afnemen.

Ook hier is het belangrijk om weten dat, hoe dichter de clusters bij mekaar liggen, hoe groter de correlatie zal zijn. Let wel! Deze correlatie hoeft niet altijd positief te zijn.

Bij deze techniek kunnen slechts 2 variabelen worden vergeleken!

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
299,63
305,945
382,252
348,846
335,367
373,617
312,612
312,232
337,161
331,476
350,103
345,127
297,256
295,979
361,007
321,803
354,937
349,432
290,979
349,576
327,625
349,377
336,777
339,134
323,321
318,86
373,583
333,03
408,556
414,646
291,514
348,857
349,368
375,765
364,136
349,53
348,167
332,856
360,551
346,969
392,815
372,02
371,027
342,672
367,343
390,786
343,785
362,6
349,468
340,624
369,536
407,782
392,239
404,824
373,669
344,902
396,7
398,911
366,009
392,484
Dataseries Y:
Download CSV
Histogram 
154,783
187,646
237,863
215,54
231,745
199,548
164,147
159,388
203,514
224,901
211,539
211,16
181,712
203,908
240,774
232,819
255,221
246,7
206,263
211,679
236,601
237,43
233,767
219,52
222,625
216,238
248,587
221,376
242,453
246,539
189,351
185,956
213,175
228,732
212,93
218,254
227,103
219,026
264,529
262,057
258,779
231,928
211,167
205,439
224,883
228,624
209,435
215,607
287,356
306,015
338,546
344,16
328,412
342,006
277,668
290,477
314,967
324,627
290,646
315,033

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=22796&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=22796&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22796&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 axis11.6560362157082
y axis12.3238034373634
Correlation
correlation used in KDE0.659021971841534
correlation(x,y)0.659021971841534

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 11.6560362157082 \tabularnewline
y axis & 12.3238034373634 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.659021971841534 \tabularnewline
correlation(x,y) & 0.659021971841534 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22796&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]11.6560362157082[/C][/ROW]
[ROW][C]y axis[/C][C]12.3238034373634[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.659021971841534[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.659021971841534[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22796&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22796&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 axis11.6560362157082
y axis12.3238034373634
Correlation
correlation used in KDE0.659021971841534
correlation(x,y)0.659021971841534


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')