FreeStatistics.org

Free Statistics

of Irreproducible Research!

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 12:53:23 -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/t1226346840r45qra06rfqvb42.htm/, Retrieved Sun, 19 May 2024 06:23:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23196, Retrieved Sun, 19 May 2024 06:23:19 +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] [Bivariate Density] [2008-11-10 19:53:23] [bda7fba231d49184c6a1b627868bbb81] [Current]
Feedback Forum
2008-11-22 10:57:56 [Jeroen Michel] [reply] 
Er is inderdaad een goede uitwerking gebeurd, maar er is geen conclusie getrokken.

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!
2008-11-23 19:12:56 [Bonifer Spillemaeckers] [reply] 
Met de techniek van de Bivariate Density kan je de correlatie meten tussen 2 variabelen. Je hebt hier wel te maken met hoogtelijnen. Punten van gelijke dichtheid worden door deze hoogtelijnen met elkaar verbonden. Op de grafiek kan je ook clusters bemerken. Als de clusters eenzelfde orientatie vertonen, kunnen we afleiden dat er een verband is tussen de 2 variabelen. Ook kunnen we een rode zone en een groene zone bemerken op de grafiek. De rode zone duidt een sterke correlatie aan en de groene zone een zwakke correlatie.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
189917
184128
175335
179566
181140
177876
175041
169292
166070
166972
206348
215706
202108
195411
193111
195198
198770
194163
190420
189733
186029
191531
232571
243477
227247
217859
208679
213188
216234
213587
209465
204045
200237
203666
241476
260307
243324
244460
233575
237217
235243
230354
227184
221678
217142
219452
256446
265845
248624
241114
229245
231805
219277
219313
212610
214771
211142
211457
240048
240636
230580
Dataseries Y:
Download CSV
Histogram 
250853
245775
220309
216689
220316
220726
220286
216507
213020
213001
231854
236386
241168
240326
234294
235304
238127
238726
235694
236660
232986
233705
253525
254303
261224
258778
252791
256389
258961
258647
256304
250498
247883
249552
262626
264416
273049
272441
267564
265952
263937
264765
263386
258985
257334
257477
271486
274488
281274
272674
269704
268227
276444
272247
268516
263406
263619
265905
281681
287413
289423

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'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23196&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23196&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23196&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Bandwidth
x axis13621.9096915226
y axis7592.46070554827
Correlation
correlation used in KDE0.870940329678518
correlation(x,y)0.870940329678518

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 13621.9096915226 \tabularnewline
y axis & 7592.46070554827 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.870940329678518 \tabularnewline
correlation(x,y) & 0.870940329678518 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23196&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]13621.9096915226[/C][/ROW]
[ROW][C]y axis[/C][C]7592.46070554827[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.870940329678518[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.870940329678518[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23196&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23196&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 axis13621.9096915226
y axis7592.46070554827
Correlation
correlation used in KDE0.870940329678518
correlation(x,y)0.870940329678518


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')