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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 computationMon, 19 Sep 2016 09:36:39 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2016/Sep/19/t1474270610jrzaz2crhsn5vz2.htm/, Retrieved Tue, 30 Apr 2024 01:17:50 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=296527, Retrieved Tue, 30 Apr 2024 01:17:50 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact179
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Kernel Density Estimation] [] [2016-09-19 07:36:39] [93189e2c4c7b1a2c7b16a24d5daa98a9] [Current]
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Dataseries X:
21
22
22
18
23
12
20
22
21
19
22
15
20
19
18
15
20
21
21
15
16
23
21
18
25
9
30
20
23
16
16
19
25
18
23
21
10
14
22
26
23
23
24
24
18
23
15
19
16
25
23
17
19
21
18
27
21
13
8
29
28
23
21
19
19
20
18
19
17
19
25
19
22
23
14
16
24
20
12
24
22
12
22
20
10
23
17
22
24
18
21
20
20
22
19
20
26
23
24
21
21
19
8
17
20
11
8
15
18
18
19
19
23
22
21
25
30
17
27
23
23
18
18
23
19
15
20
16
24
25
25
19
19
16
19
19
23
21
22
19
20
20
3
23
23
20
15
16
7
24
17
24
24
19
25
20
28
23
27
18
28
21
19
23
27
22
28
25
21
22
28
20
29
25
25
20
20
16
20
20
23
18
25
18
19
25
25
25
24
19
26
10
17
13
17
30
25
4
16
21
23
22
17
20
20
22
16
23
0
18
25
23
12
18
24
11
18
23
24
29
18
15
29
16
19
22
16
23
23
19
4
20
24
20
4
24
22
16
3
15
24
17
20
27
26
23
17
20
22
19
24
19
23
15
27
26
22
22
18
15
22
27
10
20
17
23
19
13
27
23
16
25
2
26
20
23
22
24
Dataseries Y:
7.5
6
6.5
1
1
5.5
8.5
6.5
4.5
2
5
0.5
5
5
2.5
5
5.5
3.5
3
4
0.5
6.5
4.5
7.5
5.5
4
7.5
7
4
5.5
2.5
5.5
3.5
2.5
4.5
4.5
4.5
6
2.5
5
0
5
6.5
5
6
4.5
5.5
1
7.5
6
5
1
5
6.5
7
4.5
0
8.5
3.5
7.5
3.5
6
1.5
9
3.5
3.5
4
6.5
7.5
6
5
5.5
3.5
7.5
6.5
6.5
6.5
7
3.5
1.5
4
7.5
4.5
0
3.5
5.5
5
4.5
2.5
7.5
7
0
4.5
3
1.5
3.5
2.5
5.5
8
1
5
4.5
3
3
8
2.5
7
0
1
3.5
5.5
5.5
0.5
7.5
9
9.5
8.5
7
8
10
7
8.5
9
9.5
4
6
8
5.5
9.5
7.5
7
7.5
8
7
7
6
10
2.5
9
8
6
8.5
6
9
8
9
5.5
7
5.5
9
2
8.5
9
8.5
9
7.5
10
9
7.5
6
10.5
8.5
8
10
10.5
6.5
9.5
8.5
7.5
5
8
10
7
7.5
7.5
9.5
6
10
7
3
6
7
10
7
3.5
8
10
5.5
6
6.5
6.5
8.5
4
9.5
8
8.5
5.5
7
9
8
10
8
6
8
5
9
4.5
8.5
9.5
8.5
7.5
7.5
5
7
8
5.5
8.5
9.5
7
8
8.5
3.5
6.5
6.5
10.5
8.5
8
10
10
9.5
9
10
7.5
4.5
4.5
0.5
6.5
4.5
5.5
5
6
4
8
10.5
6.5
8
8.5
5.5
7
5
3.5
5
9
8.5
5
9.5
3
1.5
6
0.5
6.5
7.5
4.5
8
9
7.5
8.5
7
9.5
6.5
9.5
6
8
9.5
8
8
9
5




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=296527&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=296527&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296527&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 axis1.21919798699388
y axis0.775611808959543
Correlation
correlation used in KDE0.167635854924032
correlation(x,y)0.167635854924032

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 1.21919798699388 \tabularnewline
y axis & 0.775611808959543 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.167635854924032 \tabularnewline
correlation(x,y) & 0.167635854924032 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=296527&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]1.21919798699388[/C][/ROW]
[ROW][C]y axis[/C][C]0.775611808959543[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.167635854924032[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.167635854924032[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=296527&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=296527&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 axis1.21919798699388
y axis0.775611808959543
Correlation
correlation used in KDE0.167635854924032
correlation(x,y)0.167635854924032



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')