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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 computationTue, 11 Nov 2008 07:12:08 -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/11/t1226412758hdj6a8nlynhpyhb.htm/, Retrieved Sun, 19 May 2024 11:31:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23495, Retrieved Sun, 19 May 2024 11:31:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact151

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] [Q1] [2008-11-11 14:12:08] [2ba2a74112fb2c960057a572bf2825d3] [Current]
F RMPD    [Partial Correlation] [Q2] [2008-11-11 14:20:55] [491a70d26f8c977398d8a0c1c87d3dd4]
F RMP       [Trivariate Scatterplots] [Q1] [2008-11-11 14:27:56] [491a70d26f8c977398d8a0c1c87d3dd4]
- RMPD    [Box-Cox Linearity Plot] [Q3] [2008-11-24 20:46:28] [491a70d26f8c977398d8a0c1c87d3dd4]
F RMP     [Box-Cox Linearity Plot] [Q3] [2008-11-24 20:55:27] [491a70d26f8c977398d8a0c1c87d3dd4]
- RM D      [Box-Cox Normality Plot] [Q4] [2008-11-24 21:10:18] [491a70d26f8c977398d8a0c1c87d3dd4]
- RMP         [Maximum-likelihood Fitting - Normal Distribution] [Q5] [2008-11-24 21:12:21] [491a70d26f8c977398d8a0c1c87d3dd4]
-    D        [Box-Cox Normality Plot] [Q4] [2008-11-24 21:16:12] [491a70d26f8c977398d8a0c1c87d3dd4]
- RMP           [Maximum-likelihood Fitting - Normal Distribution] [Q5] [2008-11-24 21:19:21] [491a70d26f8c977398d8a0c1c87d3dd4]
Feedback Forum
2008-11-20 17:32:01 [Steffi Van Isveldt] [reply] 
Je kan hier op deze output een licht positieve correlatie aflezen van de gebruikte tijdreeksen.
2008-11-23 14:03:56 [Roland Feldman] [reply] 
Een licht positieve correlatie is hier te zien
2008-11-24 13:44:25 [Siem Van Opstal] [reply] 
we merken een lichte correlatie op
2008-11-24 23:07:38 [Liese Tormans] [reply] 
Bivariate Density is een betere methode om te gaan kijken of er een verband is en of er eventuele clusters zijn.
Want de Bivariate Density gaat met behulp van hoogtelijnen de punten van gelijkaardige observaties verbinden. De grafiek bestaat uit verschillende kleuren die intensiteit weergeven.
Een rode kleur geeft een sterke intensiteit weer terwijl de gele en groene kleur wijzen op een eerder zwakke correlatie.
Ook is het mogelijk om aan de hand van deze grafiek de clusters af te lezen.

Het voordeel hier is dat we meer info krijgen dan bij de scatter plot of gewone correlatie.
Want bij de andere grafieken is niet echt rekening gehouden met de invloed van de derde variabele.

Als we dan naar de Bivariate Density gaan kijken zien we dat er een matig positief verband is tussen de totale consumptiegoederen en de investeringsgoederen. We kunnen ook zien dat de correlatie hier 0,65 bedraagt.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
109.6
103
111.6
106.3
97.9
108.8
103.9
101.2
122.9
123.9
111.7
120.9
99.6
103.3
119.4
106.5
101.9
124.6
106.5
107.8
127.4
120.1
118.5
127.7
107.7
104.5
118.8
110.3
109.6
119.1
96.5
106.7
126.3
116.2
118.8
115.2
110
111.4
129.6
108.1
117.8
122.9
100.6
111.8
127
128.6
124.8
118.5
114.7
112.6
128.7
111
115.8
126
111.1
113.2
120.1
130.6
124
119.4
116.7
Dataseries Y:
Download CSV
Histogram 
93.4
101.1
114.2
104.8
113.3
118.2
83.6
73.9
99.5
97.7
103
106.3
92.2
101.8
122.8
111.8
106.3
121.5
81.9
85.4
110.9
117.3
106.3
105.6
101.2
105.9
126.3
111.9
108.9
127.2
94.2
85.7
116.2
107.2
110.5
112
104.4
112
132.8
110.8
128.7
136.8
94.8
88.8
123.2
125.3
122.7
125.8
116.3
118.6
142.1
127.9
132
152.4
110.8
99.1
134.9
133.2
131
133.9
119.9

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=23495&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=23495&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23495&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 axis4.64016355486822
y axis7.80212316703362
Correlation
correlation used in KDE0.649868672897519
correlation(x,y)0.649868672897519

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 4.64016355486822 \tabularnewline
y axis & 7.80212316703362 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.649868672897519 \tabularnewline
correlation(x,y) & 0.649868672897519 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23495&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]4.64016355486822[/C][/ROW]
[ROW][C]y axis[/C][C]7.80212316703362[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.649868672897519[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.649868672897519[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23495&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23495&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 axis4.64016355486822
y axis7.80212316703362
Correlation
correlation used in KDE0.649868672897519
correlation(x,y)0.649868672897519


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')