Free Statistics

of Irreproducible Research!

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 computationTue, 11 Nov 2008 05:14:21 -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/2008/Nov/11/t122640571478vvjdaurzwitxk.htm/, Retrieved Sun, 19 May 2024 09:37:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23363, Retrieved Sun, 19 May 2024 09:37:26 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Bivariate Kernel Density Estimation] [Various EDA Topic...] [2008-11-11 12:14:21] [620b6ad5c4696049e39cb73ce029682c] [Current]
Feedback Forum
2008-11-14 10:09:48 [Ciska Tanghe] [reply
Bij deze grafiek worden alle punten met een gelijke dichtheid verbonden met elkaar. We zien in dit geval twee clusters, twee scatterplot die sterk ineen lopen. Hieruit volgt dat tussen deze twee tijdreeksen een sterk verband is.
2008-11-24 17:57:15 [Jan Cavents] [reply
hier wordt op een grafische manier het verband tussen de variabelen weergegeven. in het midden waar de punten het dichtst bij elkaar liggen is het verband het grootst.

Post a new message
Dataseries X:
1045,9
1401,9
1027,6
1703,8
1481,3
1422,7
1304,7
1246,1
1417,8
1459,1
1156,4
1304,5
1336,9
1372,3
975,5
1180,8
1361,3
1428,1
1355,9
1781,2
1697
1852
1844,1
1967,2
1747,1
1863,9
1559,3
1675
2237,5
1965,2
1871,5
1752,2
1360,7
1444,3
1621,6
1368
1553,9
1695,3
1397,1
1848,4
1809,2
1551,1
1546,6
1467,9
1662,4
1972,3
1673,5
1762
2019,8
1754,3
1400,4
1453,6
1740,9
1694,6
1541,2
1482,3
1632,1
1837,3
1797
2066,2
1983,8
1601,7
1660,3
1954
1991,9
1881,4
2345,5
1773,1
1719,2
2240,9
1816,4
2171,3
1823,3
2022,5
1991
1920
2168,4
2013,5
1790,8
1855,7
2074
2535,8
1837,2
1805,1
1785,7
2250
1959,7
1890,8
2405,7
2090,3
1666,5
1803,5
1793,8
1488,8
1545
1369,9
1451,6
Dataseries Y:
1593
1477,9
1733,7
1569,7
1843,7
1950,3
1657,5
1772,1
1568,3
1809,8
1646,7
1808,5
1763,9
1625,5
1538,8
1342,4
1645,1
1619,9
1338,1
1505,5
1529,1
1511,9
1656,7
1694,4
1662,3
1588,7
1483,3
1585,6
1658,9
1584,4
1470,6
1618,7
1407,6
1473,9
1515,3
1485,4
1496,1
1493,5
1298,4
1375,3
1507,9
1455,3
1363,3
1392,8
1348,8
1880,3
1669,2
1543,6
1701,2
1516,5
1466,8
1484,1
1577,2
1684,5
1414,7
1674,5
1598,7
1739,1
1674,6
1671,8
1802
1526,8
1580,9
1634,8
1610,3
1712
1678,8
1708,1
1680,6
2056
1624
2021,4
1861,1
1750,8
1767,5
1710,3
2151,5
2047,9
1915,4
1984,7
1896,5
2170,8
2139,9
2330,5
2121,8
2226,8
1857,9
2155,9
2341,7
2290,2
2006,5
2111,9
1731,3
1762,2
1863,2
1943,5
1975,2




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=23363&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=23363&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23363&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 axis111.537179366116
y axis84.4219637251178
Correlation
correlation used in KDE0.473776522098695
correlation(x,y)0.473776522098695

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 111.537179366116 \tabularnewline
y axis & 84.4219637251178 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.473776522098695 \tabularnewline
correlation(x,y) & 0.473776522098695 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23363&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]111.537179366116[/C][/ROW]
[ROW][C]y axis[/C][C]84.4219637251178[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.473776522098695[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.473776522098695[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23363&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23363&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 axis111.537179366116
y axis84.4219637251178
Correlation
correlation used in KDE0.473776522098695
correlation(x,y)0.473776522098695



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')