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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 12:59:56 -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/t1226347243vej5n7vk5bg8bx7.htm/, Retrieved Sun, 19 May 2024 03:52:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23197, Retrieved Sun, 19 May 2024 03:52:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact174

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 19:59:56] [bda7fba231d49184c6a1b627868bbb81] [Current]
Feedback Forum
2008-11-22 10:58:25 [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!

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 
72157
72928
75340
74812
74593
76003
75112
75452
75634
75653
78645
73100
79699
82848
81834
81736
82267
84120
83819
82734
81842
81735
83227
81934
89521
88827
85874
85211
87130
88620
89563
89056
88542
89504
89428
86040
96240
94423
93028
92285
91685
94260
93858
92437
92980
92099
92803
88551
98334
98329
96455
97109
97687
98512
98673
96028
98014
95580
97838
97760
99913

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23197&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]2 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=23197&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23197&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Bandwidth
x axis13621.9096915226
y axis3464.54208404512
Correlation
correlation used in KDE0.712304220767885
correlation(x,y)0.712304220767885

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 13621.9096915226 \tabularnewline
y axis & 3464.54208404512 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.712304220767885 \tabularnewline
correlation(x,y) & 0.712304220767885 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23197&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]13621.9096915226[/C][/ROW]
[ROW][C]y axis[/C][C]3464.54208404512[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.712304220767885[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.712304220767885[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23197&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23197&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 axis3464.54208404512
Correlation
correlation used in KDE0.712304220767885
correlation(x,y)0.712304220767885


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')