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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 computationWed, 10 Dec 2014 10:42:27 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2014/Dec/10/t1418208212jcj2b28ntg0brv5.htm/, Retrieved Fri, 17 May 2024 08:34:14 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=264902, Retrieved Fri, 17 May 2024 08:34:14 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact108
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2014-12-10 10:42:27] [10320d42b3a1ca1321e6e126fa928a8a] [Current]
- RMPD    [Classical Decomposition] [Decomposition of ...] [2014-12-10 12:47:30] [9ecaa0fefb0ae88b4782d69916cabb9e]
- RMPD    [Decomposition by Loess] [Decomposition by ...] [2014-12-10 13:18:31] [9ecaa0fefb0ae88b4782d69916cabb9e]
- RMPD    [Structural Time Series Models] [Structural time s...] [2014-12-10 13:36:16] [9ecaa0fefb0ae88b4782d69916cabb9e]
- RMPD    [Skewness and Kurtosis Test] [Sweness/kurtosis] [2014-12-10 13:42:05] [9ecaa0fefb0ae88b4782d69916cabb9e]
- RMPD    [Exponential Smoothing] [Exponential smoot...] [2014-12-10 13:46:46] [9ecaa0fefb0ae88b4782d69916cabb9e]
- RMPD    [Multiple Regression] [Multiple regressi...] [2014-12-10 13:57:37] [9ecaa0fefb0ae88b4782d69916cabb9e]
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Dataseries X:
26
51
57
37
67
43
52
52
43
84
67
49
70
52
58
68
62
43
56
56
74
65
63
58
57
63
53
57
51
64
53
29
54
51
58
43
51
53
54
56
61
47
39
48
50
35
30
68
49
61
67
47
56
50
43
67
62
57
41
54
45
48
61
56
41
43
53
44
66
58
46
37
51
51
56
66
45
37
59
42
38
66
34
53
49
55
49
59
40
58
60
63
56
54
52
34
69
32
48
67
58
57
42
64
58
66
26
61
52
51
55
50
60
56
63
61
52
16
46
56
52
55
50
59
60
52
44
67
52
55
37
54
72
51
48
60
50
63
33
67
46
54
59
61
33
47
69
52
55
55
41
73
51
52
50
51
60
56
56
29
66
66
73
55
64
40
46
58
43
61
51
50
52
54
66
61
80
51
56
56
56
53
47
25
47
46
50
39
51
58
35
58
60
62
63
53
46
67
59
64
38
50
48
48
47
66
47
63
58
44
51
43
55
38
56
45
50
54
57
60
55
56
49
37
43
59
46
51
58
64
53
48
51
47
59
62
62
51
64
52
67
50
54
58
56
63
31
65
71
50
57
47
54
47
57
43
41
63
63
56
51
50
22
41
59
56
66
53
42
52
54
44
62
53
50
36
76
66
62
59
47
55
58
60
44
57
45
58
51
57
30
46
51
56
58
44
14
53
42
49
44
62
30
46
56
50
54
48
55
35
55
41
59
54
66
55
45
51
47
42
53
53
41
55
55
46
63
43
65
59
39
44
60
57
67
52
52
69
46
46
53
40
70
54
77
45
60
47
50
66
60
41
53
34
51
69
60
45
58
39
51
52
49
63
44
51
52
60
53
53
52
31
51
65
51
49
61
58
62
54
52
72
50
65
53
56
63
62
66
50
45
58
52
53
68
59
58
52
45
58
70
69
71
46
58
39
46
64
67
44
54
41
68
63
57
61
39
69
64
38
59
51
59
51
65
47
50
57
21
47
51
37
67
43
58
51
40
41
58
64
64
58
50
59
55
59
58
41
56
63
77
60
58
64
47
46
62
60
50
46
44
58
56
43
54
54
56
65
66
62
58
67
25
56
53
56
59
46
49
56
76
33
49
53
58
72
51
42
69
51
54
52
59
51
67
64
58
Dataseries Y:
50
68
62
54
71
54
65
73
52
84
42
66
65
78
73
75
72
66
70
61
81
71
69
71
72
68
70
68
61
67
76
70
60
77
72
69
71
62
70
64
58
76
52
59
68
76
65
67
59
69
76
63
75
63
60
73
63
70
75
66
63
63
64
70
75
61
60
62
73
61
66
64
59
64
60
56
66
78
53
67
59
66
68
71
66
73
72
71
59
64
66
78
68
73
62
65
68
65
60
71
65
68
64
74
69
76
68
72
67
63
59
73
66
62
69
66
51
56
67
69
57
56
55
63
67
65
47
76
64
68
64
65
71
63
60
68
72
70
61
61
62
71
71
51
56
70
73
76
59
68
48
52
59
60
59
57
79
60
60
59
62
59
61
71
57
66
63
69
58
59
48
66
73
67
61
68
75
62
69
58
60
74
55
62
63
69
58
58
68
72
62
62
65
69
66
72
62
75
58
66
55
47
72
62
64
64
19
50
68
70
79
69
71
48
66
73
74
66
71
74
78
75
53
60
50
70
69
65
78
78
59
72
70
63
63
71
74
67
66
62
80
73
67
61
73
74
32
69
69
84
64
58
60
59
78
57
60
68
68
73
69
67
60
65
66
74
81
72
55
49
74
53
64
65
57
51
80
67
70
74
75
70
69
65
55
71
65
69
48
69
68
74
67
65
63
74
39
68
69
68
63
67
70
68
66
70
78
59
62
75
74
73
62
69
65
67
73
52
61
53
63
78
65
77
69
68
76
63
41
76
67
69
59
73
72
52
65
63
78
56
68
56
64
68
75
67
55
73
66
75
77
65
75
57
61
71
72
62
66
66
63
60
64
74
59
71
69
63
73
55
77
70
64
78
60
66
77
68
78
68
60
65
64
69
72
50
72
71
80
74
64
69
76
75
79
73
60
76
55
53
62
69
78
68
67
75
59
73
70
59
64
63
67
58
71
79
53
76
66
64
57
67
72
58
74
57
62
74
54
62
66
64
74
71
66
66
63
65
70
66
66
78
77
72
65
67
72
58
84
67
84
58
63
75
55
72
58
69
54
58
67
77
80
67
75
71
72
75
79
76
72
81
52
76
60
72
77
64
67
72
79
40
71
73
75
70
66
66
73
74
58
51
75
70
50
64
77




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264902&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' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264902&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264902&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







Bandwidth
x axis2.77509871856541
y axis2.34177593328443
Correlation
correlation used in KDE0.36093004615882
correlation(x,y)0.36093004615882

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 2.77509871856541 \tabularnewline
y axis & 2.34177593328443 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.36093004615882 \tabularnewline
correlation(x,y) & 0.36093004615882 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=264902&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]2.77509871856541[/C][/ROW]
[ROW][C]y axis[/C][C]2.34177593328443[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.36093004615882[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.36093004615882[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=264902&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=264902&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 axis2.77509871856541
y axis2.34177593328443
Correlation
correlation used in KDE0.36093004615882
correlation(x,y)0.36093004615882



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')