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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, 20 Dec 2010 15:27:48 +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/2010/Dec/20/t1292858741g9exi2uqcrzunhz.htm/, Retrieved Sat, 04 May 2024 03:09:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=112997, Retrieved Sat, 04 May 2024 03:09:07 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Univariate Data Series] [data set] [2008-12-01 19:54:57] [b98453cac15ba1066b407e146608df68]
- RMP   [Standard Deviation-Mean Plot] [Unemployment] [2010-11-29 10:34:47] [b98453cac15ba1066b407e146608df68]
- RMP     [ARIMA Forecasting] [Unemployment] [2010-11-29 20:46:45] [b98453cac15ba1066b407e146608df68]
-   PD      [ARIMA Forecasting] [test 7] [2010-12-05 11:13:06] [74be16979710d4c4e7c6647856088456]
-   P         [ARIMA Forecasting] [W9 - Blog 9] [2010-12-06 16:23:47] [1aa8d85d6b335d32b1f6be940e33a166]
-   PD          [ARIMA Forecasting] [ARIMA forecast Li...] [2010-12-20 15:06:20] [1aa8d85d6b335d32b1f6be940e33a166]
- RMPD              [Bivariate Kernel Density Estimation] [Bivariate Kernell...] [2010-12-20 15:27:48] [47bfda5353cd53c1cf7ea7aa9038654a] [Current]
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Dataseries X:
11.04
11.02
11.03
11.17
11.19
11.15
11.13
11.06
11.01
11.03
10.99
10.94
11.00
11.06
11.06
11.05
11.04
11.15
11.20
11.16
11.30
11.23
11.25
11.25
11.12
11.14
11.17
11.25
11.27
11.34
11.39
11.44
11.46
11.49
11.51
11.48
11.49
11.52
11.56
11.58
11.58
11.58
11.60
11.62
11.62
11.64
11.67
11.66
11.72
11.82
11.90
12.04
12.08
12.15
12.19
12.22
12.23
12.25
12.26
12.27
12.34
12.38
12.42
12.43
12.48
12.50
12.50
12.49
12.46
12.45
12.45
12.38
12.42
Dataseries Y:
25.00
25.09
25.03
25.21
25.33
25.23
25.13
25.03
25.03
25.15
25.18
24.90
25.18
25.25
25.28
25.32
25.27
25.22
25.14
25.41
25.72
25.66
25.65
25.27
23.90
24.06
24.33
24.39
24.39
24.49
24.83
25.08
25.11
25.13
25.17
25.11
25.35
25.36
25.35
25.34
25.39
25.58
25.71
25.66
25.74
25.73
25.72
25.55
25.71
25.92
25.93
26.00
26.02
26.08
26.17
26.18
26.21
26.28
26.34
26.17
26.38
26.36
26.27
26.26
26.49
26.99
27.14
27.10
27.01
26.93
26.97
26.35
26.93




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112997&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112997&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112997&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 time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk







Bandwidth
x axis0.138356379118164
y axis0.213776206907421
Correlation
correlation used in KDE0.874216222633235
correlation(x,y)0.874216222633235

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 0.138356379118164 \tabularnewline
y axis & 0.213776206907421 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.874216222633235 \tabularnewline
correlation(x,y) & 0.874216222633235 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=112997&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]0.138356379118164[/C][/ROW]
[ROW][C]y axis[/C][C]0.213776206907421[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.874216222633235[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.874216222633235[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=112997&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=112997&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 axis0.138356379118164
y axis0.213776206907421
Correlation
correlation used in KDE0.874216222633235
correlation(x,y)0.874216222633235



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')