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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 computationWed, 12 Nov 2008 11:20:22 -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/12/t1226514234ztlphf8roxx8dfz.htm/, Retrieved Sun, 19 May 2024 12:19:18 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24347, Retrieved Sun, 19 May 2024 12:19:18 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsbivariate kernel
Estimated Impact147

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] [] [2008-11-12 15:02:05] [b87cf4f6ac665e4d6a88fc8d9f2625f6]
F    D    [Bivariate Kernel Density Estimation] [bivariate kernel ...] [2008-11-12 18:20:22] [35c75b0726318bf2908e4a56ed2df1a9] [Current]
Feedback Forum
2008-11-21 21:52:59 [Gilliam Schoorel] [reply] 
De bivariate density geeft de dichtheid tussen variabelen weer om zo de afhankelijkheid tussen deze 2 variabelen te meten. Je moet eerder naar de vormen van de hoogtelijnen kijken en in welke maten ze tegen over elkaar liggen/gepositioneerd zijn. Je kan hier spreken over een normaalverdeling. De hoogtelijnen zijn centraal gepositioneerd en hebben een 'neutrale' semenhang.
2008-11-24 15:17:26 [Ellen Van den Broeck] [reply] 
De hoogtelijnen geven een derde dimensie weer. Inderdaad is er een hoge concentratie waar de kleur rozer/witter wordt. Maar de cluster die wordt weergegeven is cirkelvormig. Enkel bij een ellipsvormige cluster met een postieve of negatieve helling is er correlatie/verband. Die lijn die in het midden wordt weergegeven is een gemiddelde van alle gegevens.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
62,2
88,5
93,3
89,2
101,3
97
102,2
100,3
78,2
105,9
119,9
108
77
93,1
109,5
100,4
99
113,9
102,1
101,6
84
110,7
111,6
110,7
73,1
87,5
109,6
99,3
92,1
109,3
94,5
91,4
82,9
103,3
96
104,8
65,8
78,7
100,3
85
94,5
97,9
91,9
87,2
84,4
99,2
105,4
110,9
69,8
86,8
106,7
88,8
96,9
108,1
93,7
94,8
79,8
95,6
107,9
104,9
61,9
85,7
92,4
86,4
99,3
95,5
97
102,1
77,8
105,5
113,2
108,8
66,9
89,3
93,6
92
99,5
98,6
94,6
96,7
75,3
102,5
115,1
104,7
71,4
Dataseries Y:
Download CSV
Histogram 
43,5
37,7
36,8
24,4
31,3
43,9
53,6
48,9
30,9
31,8
41,3
43,7
54,1
47,8
36,7
30,8
31,9
61,7
73
64,7
24,2
33,9
32,4
63,2
71,8
60,4
48
44,5
44,9
70,9
72,7
59,5
35,9
40
43,6
57,2
75,8
57,7
47,7
42,3
43
68
70,6
54,2
38,6
40,3
49,2
68,5
75,9
63,2
49,8
37
48,8
74,9
75,3
66,9
44,1
39,8
56,6
77,1
78,5
70,6
54,2
47,2
55,1
74,5
88
80,8
54,4
55,2
73,8
85,3
98,7
86,1
62,5
58,6
67
88,4
96,5
87,1
61,2
62,5
85,2
101,7
113,7

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'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 & 2 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=24347&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24347&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24347&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'Herman Ole Andreas Wold' @ 193.190.124.10:1001






Bandwidth
x axis5.32292985796581
y axis8.34906272493126
Correlation
correlation used in KDE-0.09912956797355
correlation(x,y)-0.09912956797355

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 5.32292985796581 \tabularnewline
y axis & 8.34906272493126 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & -0.09912956797355 \tabularnewline
correlation(x,y) & -0.09912956797355 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24347&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]5.32292985796581[/C][/ROW]
[ROW][C]y axis[/C][C]8.34906272493126[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]-0.09912956797355[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]-0.09912956797355[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24347&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24347&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 axis5.32292985796581
y axis8.34906272493126
Correlation
correlation used in KDE-0.09912956797355
correlation(x,y)-0.09912956797355


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')