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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 computationSun, 24 Dec 2017 11:01:26 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2017/Dec/24/t1514109753nwdrp7oj9gs99g6.htm/, Retrieved Tue, 14 May 2024 20:40:43 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310886, Retrieved Tue, 14 May 2024 20:40:43 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsPaper Sandrine Ponet en Jeremy Moras
Estimated Impact90
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2017-12-24 10:01:26] [26c84e1eb0baa7a22199b5c546f8cdcb] [Current]
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Dataseries X:
5.90
7.20
4.80
4.50
9.40
6.00
2.80
5.70
6.30
8.30
7.80
7.20
9.30
11.10
7.40
9.20
11.30
6.20
4.70
2.80
6.90
5.40
7.40
3.00
6.40
3.00
4.20
13.70
6.30
10.30
4.70
5.90
8.90
9.00
2.60
9.80
5.50
4.70
5.60
6.70
4.70
4.70
10.40
4.90
8.80
3.50
3.30
6.90
5.60
6.50
5.90
7.00
5.40
7.20
7.10
17.10
8.60
5.30
7.10
5.00
6.40
Dataseries Y:
3.30
4.30
6.30
3.30
5.60
2.40
2.20
5.10
6.90
5.80
4.50
4.40
6.30
12.80
4.20
4.40
5.10
3.00
3.20
2.80
3.70
6.10
5.00
3.80
2.20
2.70
5.90
6.80
5.10
8.40
5.50
5.40
8.40
4.60
2.10
4.80
2.80
2.70
4.20
4.10
5.10
5.60
4.80
3.00
6.00
1.70
5.10
3.90
6.20
5.80
3.20
6.20
2.90
3.30
6.20
5.40
4.50
6.90
6.30
1.30
2.50




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310886&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310886&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310886&T=0

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center







Bandwidth
x axis0.953828473389642
y axis0.781212423323513
Correlation
correlation used in KDE0.484372770866061
correlation(x,y)0.484372770866061

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 0.953828473389642 \tabularnewline
y axis & 0.781212423323513 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.484372770866061 \tabularnewline
correlation(x,y) & 0.484372770866061 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310886&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]0.953828473389642[/C][/ROW]
[ROW][C]y axis[/C][C]0.781212423323513[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.484372770866061[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.484372770866061[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310886&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310886&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Bandwidth
x axis0.953828473389642
y axis0.781212423323513
Correlation
correlation used in KDE0.484372770866061
correlation(x,y)0.484372770866061



Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ; par8 = terrain.colors ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = 0 ; par4 = 0 ; par5 = 0 ; par6 = Y ; par7 = Y ; par8 = terrain.colors ;
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')
x <- x[!is.na(y)]
y <- y[!is.na(y)]
y <- y[!is.na(x)]
x <- x[!is.na(x)]
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
if (par8 == 'terrain.colors') mycol <- terrain.colors(100)
if (par8 == 'rainbow') mycol <- rainbow(100)
if (par8 == 'heat.colors') mycol <- heat.colors(100)
if (par8 == 'topo.colors') mycol <- topo.colors(100)
if (par8 == 'cm.colors') mycol <- cm.colors(100)
bitmap(file='bidensity.png')
op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=par5, xbandwidth=par3, ybandwidth=par4)
image(op$xords, op$yords, op$zden, col=mycol, axes=TRUE,main=main,xlab=xlab,ylab=ylab)
if (par6=='Y') contour(op$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')