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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, 11 Nov 2009 03:26:25 -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/2009/Nov/11/t12579352136945h1kmfxfwy0o.htm/, Retrieved Thu, 28 Mar 2024 20:24:38 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=55465, Retrieved Thu, 28 Mar 2024 20:24:38 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact211
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Kernel Density Estimation] [3/11/2009] [2009-11-02 21:54:51] [b98453cac15ba1066b407e146608df68]
-   PD  [Bivariate Kernel Density Estimation] [ws6 bivariate ker...] [2009-11-06 12:52:23] [8b1aef4e7013bd33fbc2a5833375c5f5]
-           [Bivariate Kernel Density Estimation] [] [2009-11-11 10:26:25] [2a6f24d4847085573f343c759dfbabef] [Current]
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Dataseries X:
100.00
100.00
97.56
95.12
92.68
92.68
97.56
107.32
112.20
112.20
112.20
114.63
117.07
117.07
114.63
114.63
114.63
112.20
121.95
131.71
134.15
136.59
136.59
141.46
146.34
148.78
148.78
146.34
146.34
148.78
158.54
173.17
180.49
180.49
182.93
185.37
190.24
190.24
187.80
185.37
182.93
178.05
185.37
195.12
195.12
192.68
190.24
187.80
190.24
187.80
182.93
178.05
173.17
170.73
178.05
190.24
192.68
192.68
190.24
190.24
192.68
190.24
185.37
180.49
175.61
168.29
173.17
182.93
185.37
180.49
178.05
175.61
178.05
175.61
173.17
170.73
168.29
165.85
175.61
185.37
187.80
185.37
182.93
182.93
185.37
185.37
185.37
182.93
178.05
175.61
180.49
195.12
200.00
195.12
187.80
187.80
190.24
190.24
187.80
182.93
178.05
173.17
173.17
175.61
165.85
160.98
156.10
156.10
158.54
153.66
143.90
134.15
126.83
119.51
131.71
141.46
139.02
136.59
134.15
131.71
131.71
131.71
134.15
141.46
139.02
131.71
136.59
141.46
151.22
165.85
163.41
163.41
156.10
153.66
153.66
156.10
153.66
146.34
153.66
153.66
160.98
182.93
190.24
192.68
190.24
185.37
182.93
185.37
182.93
178.05
185.37
182.93
185.37
192.68
192.68
197.56
200.00
195.12
182.93
165.85
158.54
160.98
185.37
195.12
197.56
187.80
182.93
185.37
190.24
190.24
190.24
182.93
182.93
173.17
182.93
182.93
185.37
187.80
187.80
192.68
197.56
200.00
200.00
200.00
192.68
178.05
168.29
160.98
163.41
168.29
170.73
173.17
175.61
173.17
168.29
170.73
165.85
156.10
163.41
160.98
156.10
153.66
151.22
158.54
165.85
165.85
156.10
148.78
141.46
148.78
175.61
178.05
168.29
148.78
141.46
151.22
173.17
187.80
192.68
187.80
180.49
182.93
195.12
197.56
Dataseries Y:
100.00
100.78
101.24
102.35
102.41
102.67
102.54
102.74
103.07
103.39
103.72
103.91
104.37
104.89
105.61
106.00
106.13
106.20
106.26
106.33
106.98
107.44
107.50
107.57
107.63
108.15
108.35
109.07
109.39
109.78
109.98
110.18
110.31
110.57
110.63
110.70
110.76
111.15
111.48
112.13
112.13
112.20
112.20
112.46
112.85
113.31
113.37
113.37
113.44
113.89
114.16
114.74
114.81
114.22
114.55
114.74
115.26
114.87
114.87
114.94
115.00
115.98
116.44
117.03
117.09
117.16
117.22
117.22
117.29
117.35
117.55
117.68
117.68
118.26
118.66
119.05
119.18
119.44
119.77
119.83
119.90
119.96
120.09
120.29
120.35
121.27
121.72
122.37
122.50
122.57
122.64
122.83
122.90
122.96
122.96
123.03
123.03
123.55
123.94
125.18
125.57
125.90
126.22
126.48
126.68
127.14
127.14
127.14
127.14
127.53
127.85
127.98
127.98
127.98
127.98
128.51
128.96
129.16
129.29
129.48
129.55
130.14
130.46
131.31
131.90
132.35
132.75
132.75
133.07
133.14
133.27
133.40
133.59
133.79
134.18
134.70
134.96
135.09
135.55
135.68
136.01
136.07
136.33
136.40
136.79
136.86
136.92
136.99
137.51
137.90
138.10
138.29
138.42
138.55
138.88
139.20
139.40
139.60
139.79
140.12
140.25
140.83
141.16
141.49
141.68
141.88
142.14
142.27
142.60
142.79
143.05
143.77
144.10
144.16
144.68
144.81
144.94
145.14
145.40
145.53
145.73
146.12
146.84
147.10
147.36
147.62
147.75
148.08
148.27
148.60
148.79
148.99
149.05
149.25
149.38
149.45
149.58
149.77
149.97
150.03
150.16
150.55
150.68
151.14
151.27
151.99
153.36
153.95
154.53
154.66
154.92
155.38
155.84
155.97
156.56
156.69
156.88
157.01
157.27
157.47
157.99
158.45
158.64
159.10




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=55465&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=55465&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55465&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' @ 72.249.127.135







Bandwidth
x axis4.92847883142004
y axis5.0330054655662
Correlation
correlation used in KDE0.338742096524238
correlation(x,y)0.338742096524238

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 4.92847883142004 \tabularnewline
y axis & 5.0330054655662 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.338742096524238 \tabularnewline
correlation(x,y) & 0.338742096524238 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=55465&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]4.92847883142004[/C][/ROW]
[ROW][C]y axis[/C][C]5.0330054655662[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.338742096524238[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.338742096524238[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=55465&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=55465&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 axis4.92847883142004
y axis5.0330054655662
Correlation
correlation used in KDE0.338742096524238
correlation(x,y)0.338742096524238



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 ; par8 = ; par9 = ; par10 = ; par11 = ; par12 = ; par13 = ; par14 = ; par15 = ; par16 = ; par17 = ; par18 = ; par19 = ; par20 = ;
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')