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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 02:23:45 -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/t1226309091q4yvbfmqmpv20i6.htm/, Retrieved Sun, 19 May 2024 10:22:51 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22892, Retrieved Sun, 19 May 2024 10:22:51 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact199

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Univariate Data Series] [VMAW] [2008-10-13 16:54:40] [cbd3d88cd5aad6543e769146e7e26b0c]
F RMPD    [Bivariate Kernel Density Estimation] [Opdracht 4 Q1] [2008-11-10 09:23:45] [2ae704d6b0222e84f58032588d68322b] [Current]
- RMPD      [Partial Correlation] [] [2008-11-11 16:13:50] [3f66c6f083b1153972739491b89fa2dd]
- RMPD      [Hierarchical Clustering] [sarah Q2] [2008-11-11 16:29:51] [3f66c6f083b1153972739491b89fa2dd]
- RMPD      [Hierarchical Clustering] [sarah Q2] [2008-11-11 16:37:31] [3f66c6f083b1153972739491b89fa2dd]
F RMPD      [Testing Mean with known Variance - Critical Value] [] [2008-11-11 17:01:20] [3f66c6f083b1153972739491b89fa2dd]
Feedback Forum
2008-11-19 15:29:55 [Mehmet Yilmaz] [reply] 
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.
2008-11-24 17:23:31 [Jan Cavents] [reply] 
Met deze methode kan je clusters achterhalen. in het midden vinden we de punten die het meest met elkaar verbonden zijn. hoe verder weg je van dit punt gaat, hoe minder gemeenschappelijke waarden ze hebben, hoe dichter hoe meer correlatie.

2008-11-24 18:38:38 [Steven Hulsmans] [reply] 
We kunnen aan de hoogtelijnen ook afleiden hoe groot de dichtheid is, aangezien de hoogtelijnen punten met een gelijke dichtheid verbinden.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
97.3
101
113.2
101
105.7
113.9
86.4
96.5
103.3
114.9
105.8
94.2
98.4
99.4
108.8
112.6
104.4
112.2
81.1
97.1
112.6
113.8
107.8
103.2
103.3
101.2
107.7
110.4
101.9
115.9
89.9
88.6
117.2
123.9
100
103.6
94.1
98.7
119.5
112.7
104.4
124.7
89.1
97
121.6
118.8
114
111.5
97.2
102.5
113.4
109.8
104.9
126.1
80
96.8
117.2
112.3
117.3
111.1
102.2
104.3
122.9
107.6
121.3
131.5
89
104.4
128.9
135.9
133.3
121.3
Dataseries Y:
Download CSV
Histogram 
93.5
94.7
112.9
99.2
105.6
113
83.1
81.1
96.9
104.3
97.7
102.6
89.9
96
112.7
107.1
106.2
121
101.2
83.2
105.1
113.3
99.1
100.3
93.5
98.8
106.2
98.3
102.1
117.1
101.5
80.5
105.9
109.5
97.2
114.5
93.5
100.9
121.1
116.5
109.3
118.1
108.3
105.4
116.2
111.2
105.8
122.7
99.5
107.9
124.6
115
110.3
132.7
99.7
96.5
118.7
112.9
130.5
137.9
115
116.8
140.9
120.7
134.2
147.3
112.4
107.1
128.4
137.7
135
151

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 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 & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22892&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]1 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=22892&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22892&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Bandwidth
x axis5.41064131134904
y axis5.45235839632827
Correlation
correlation used in KDE0.747110813264338
correlation(x,y)0.747110813264338

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 5.41064131134904 \tabularnewline
y axis & 5.45235839632827 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.747110813264338 \tabularnewline
correlation(x,y) & 0.747110813264338 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22892&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]5.41064131134904[/C][/ROW]
[ROW][C]y axis[/C][C]5.45235839632827[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.747110813264338[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.747110813264338[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22892&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22892&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 axis5.41064131134904
y axis5.45235839632827
Correlation
correlation used in KDE0.747110813264338
correlation(x,y)0.747110813264338


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')