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Author's title

Author

*Unverified author*

R Software Module

rwasp_bidensity.wasp

Title produced by software

Bivariate Kernel Density Estimation

Date of computation

Wed, 12 Nov 2008 07:36:20 -0700

Cite this page as follows

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2008/Nov/12/t122650062075tzs6nzgy2wl3q.htm/, Retrieved Sun, 19 May 2024 07:55:29 +0000

Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24216, Retrieved Sun, 19 May 2024 07:55:29 +0000

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Original text written by user:

IsPrivate?

No (this computation is public)

User-defined keywords

Estimated Impact

147

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)

F       [Bivariate Kernel Density Estimation] [opdracht3_q1] [2008-11-12 14:36:20] [e8ace8b3d80d7fc51f1760fb13a6fe6b] [Current]

Feedback Forum

2008-11-19 17:47:14 [Steven Vercammen] [reply] 
De grafiek wordt verkeerd geïnterpreteerd.De grafiek kan gebruikt worden om na te gaan of er sprake is van correlatie tussen de 2 variabelen. Het voordeel dat deze grafiek heeft ten opzichte van de gewone scatterplot is dat door de hoogtelijnen een soort van derde dimensie wordt weergegeven. Deze geeft de dichtheid van de observaties weer. Men kan dus zien waar de meeste observaties zich bevinden tov van de regressielijn. In dit geval is er sprake van een zeer sterke correlatie.
2008-11-21 20:43:29 [Gilliam Schoorel] [reply] 
Bivariate dichtheid schat de afhankelijkheid van de variabelen dmv de dichtheid te berekenen. Hier ligt de dichtheid zeer hoog, dit kan je ook zien in de tabel. De hoogtelijnen liggen zo dicht bij elkaar dat de ellipsvorm zo goed als verdwijnt en overlapt met de rechte. Men kan hier dus spreken van een zeer grote afhankelijkheid van de 2 variabelen.

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Dataseries X:

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Histogram

Boxplots

Dataseries Y:

Download CSV

Histogram

Summary of computational transaction
Raw Input	view raw input (R code)
Raw Output	view raw output of R engine
Computing time	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24216&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24216&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24216&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 Output	view raw output of R engine
Computing time	2 seconds
R Server	'Sir Ronald Aylmer Fisher' @ 193.190.124.24

Bandwidth
x axis	115.816012412044
y axis	56.9782686507355
Correlation
correlation used in KDE	0.999919280929741
correlation(x,y)	0.999919280929741

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 115.816012412044 \tabularnewline
y axis & 56.9782686507355 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.999919280929741 \tabularnewline
correlation(x,y) & 0.999919280929741 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24216&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]115.816012412044[/C][/ROW]
[ROW][C]y axis[/C][C]56.9782686507355[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.999919280929741[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.999919280929741[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=24216&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24216&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 axis	115.816012412044
y axis	56.9782686507355
Correlation
correlation used in KDE	0.999919280929741
correlation(x,y)	0.999919280929741

Figure 1

PNG link

Postscript link

PDF link

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')

Free Statistics

Description of Statistical Computation

Tree of Dependent Computations

Dataset

Tables (Output of Computation)

Figures (Output of Computation)

Input Parameters & R Code