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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 11:34:39 -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/t1226342133h06xev72lrxtti2.htm/, Retrieved Sun, 19 May 2024 10:20:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23178, Retrieved Sun, 19 May 2024 10:20:49 +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] [Brandstof - Wagens] [2008-11-10 18:34:39] [63302faa1e3976bf98d1de42298c0b24] [Current]
Feedback Forum
2008-11-19 13:44:20 [Mehmet Yilmaz] [reply] 
Je hebt de berekeningen gedaan maar geen uitleg gegeven.

Interpretatie van de Bivarlate kernel density:
Deze methode geeft clusters weer. In het midden van elke cluster ligt de intensiteit van de punten (observaties) heel dicht. Hoe verder je gaat van het midden, hoe minder de intensiteit.
Hoe dichter de clusters bij elkaar liggen, hoe groter de correlatie. Dit kan zowel een negatieve als positieve correlatie zijn.
Je kan hierbij slechts 2 variabelen vergelijken.
2008-11-23 14:43:25 [Chi-Kwong Man] [reply] 
Bivariate Density: deze grafiek gebruiken we omdat deze meer informatie geeft dan een scatterplot en een correlatieplot (beetje zoals een weerkaart). Dichtheid ~ waarschijnlijkheid.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
105,15
105,24
105,57
105,62
106,17
106,27
106,41
106,94
107,16
107,32
107,32
107,35
107,55
107,87
108,37
108,38
107,92
108,03
108,14
108,3
108,64
108,66
109,04
109,03
109,03
109,54
109,75
109,83
109,65
109,82
109,95
110,12
110,15
110,21
109,99
110,14
110,14
110,81
110,97
110,99
109,73
109,81
110,02
110,18
110,21
110,25
110,36
110,51
110,6
110,95
111,18
111,19
111,69
111,7
111,83
111,77
111,73
112,01
111,86
112,04
Dataseries Y:
Download CSV
Histogram 
118,63
121,83
119,97
124,98
129,99
126,6
121,71
119,28
122,63
116,74
114,23
113,23
112,75
113,54
115,3
121,05
119,51
116,78
117,17
117,5
119,65
120,97
117,18
116,87
119,46
122,52
124,1
118,39
113,1
113,94
114,58
118,79
120,44
118,37
118,44
117,93
117,76
118,29
121,11
124,86
131,17
130,16
131,76
134,7
135,32
140,23
136,31
131,62
128,9
133,89
138,21
146,12
144,69
149,18
156,6
158,87
164,85
162,89
153,31
150,91

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'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 & 2 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23178&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23178&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23178&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'George Udny Yule' @ 72.249.76.132






Bandwidth
x axis0.770900370116557
y axis2.94496139837346
Correlation
correlation used in KDE0.611843847055095
correlation(x,y)0.611843847055095

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 0.770900370116557 \tabularnewline
y axis & 2.94496139837346 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.611843847055095 \tabularnewline
correlation(x,y) & 0.611843847055095 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23178&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]0.770900370116557[/C][/ROW]
[ROW][C]y axis[/C][C]2.94496139837346[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.611843847055095[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.611843847055095[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23178&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23178&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 axis0.770900370116557
y axis2.94496139837346
Correlation
correlation used in KDE0.611843847055095
correlation(x,y)0.611843847055095


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')