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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 computationThu, 13 Nov 2008 00:24:50 -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/13/t1226561154vttc8fs49djo9by.htm/, Retrieved Sun, 19 May 2024 09:16:33 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24468, Retrieved Sun, 19 May 2024 09:16:33 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact177

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] [various eda topics 2] [2008-11-13 07:24:50] [e7b1048c2c3a353441b9143db4404b91] [Current]
Feedback Forum
2008-11-20 09:03:43 [Jasmine Hendrikx] [reply] 
Eigen evaluatie:
De bespreking is een beetje onvolledig. Het is inderdaad zo dat het centrum zich rond de 100 situeert. De waarschijnlijkheid dat de punten zich hier zullen bevinden, is dus groter dan ergens erbuiten. Er wordt niet echt gezegd of men van een verband kan spreken of niet. Uit de figuur zou je wel kunnen afleiden dat er een verband is. De hoogtelijnen vertonen ellipsvormen met een over het algemeen positieve helling, waardoor je dus van een positief verband zou kunnen spreken. Zoals ik bij de evaluatie van de vorige 2 grafieken reeds vermeld heb, kunnen we de bivariate kernel density plots gebruiken om scatterplots beter te onderzoeken. Scatterplots zijn namelijk vertekend doordat bepaalde dimensies gereduceerd worden. Een bivariate kernel density plot kan dit probleem een beetje opvangen.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
90,7
94,3
104,6
111,1
110,8
107,2
99,0
99,0
91,0
96,2
96,9
96,2
100,1
99,0
115,4
106,9
107,1
99,3
99,2
108,3
105,6
99,5
107,4
93,1
88,1
110,7
113,1
99,6
93,6
98,6
99,6
114,3
107,8
101,2
112,5
100,5
93,9
116,2
112,0
106,4
95,7
96,0
95,8
103,0
102,2
98,4
111,4
86,6
91,3
107,9
101,8
104,4
93,4
100,1
98,5
112,9
101,4
107,1
110,8
90,3
95,5
111,4
113,0
107,5
95,9
106,3
105,2
117,2
106,9
108,2
113,0
97,2
99,9
108,1
118,1
109,1
93,3
112,1
111,8
112,5
116,3
110,3
117,1
103,4
96,2
Dataseries Y:
Download CSV
Histogram 
78,4
114,6
113,3
117,0
99,6
99,4
101,9
115,2
108,5
113,8
121,0
92,2
90,2
101,5
126,6
93,9
89,8
93,4
101,5
110,4
105,9
108,4
113,9
86,1
69,4
101,2
100,5
98,0
106,6
90,1
96,9
125,9
112,0
100,0
123,9
79,8
83,4
113,6
112,9
104,0
109,9
99,0
106,3
128,9
111,1
102,9
130,0
87,0
87,5
117,6
103,4
110,8
112,6
102,5
112,4
135,6
105,1
127,7
137,0
91,0
90,5
122,4
123,3
124,3
120,0
118,1
119,0
142,7
123,6
129,6
151,6
110,4
99,2
130,5
136,2
129,7
128,0
121,6
135,8
143,8
147,5
136,2
156,6
123,3
100,4

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24468&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24468&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24468&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Bandwidth
x axis2.99006311411283
y axis8.57073381946338
Correlation
correlation used in KDE0.684629200251958
correlation(x,y)0.684629200251958

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 2.99006311411283 \tabularnewline
y axis & 8.57073381946338 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.684629200251958 \tabularnewline
correlation(x,y) & 0.684629200251958 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24468&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]2.99006311411283[/C][/ROW]
[ROW][C]y axis[/C][C]8.57073381946338[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.684629200251958[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.684629200251958[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24468&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24468&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 axis2.99006311411283
y axis8.57073381946338
Correlation
correlation used in KDE0.684629200251958
correlation(x,y)0.684629200251958


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')