FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

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 06:04:27 -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/t12264087813wredtwxu2tt0rf.htm/, Retrieved Sun, 19 May 2024 12:39:11 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23435, Retrieved Sun, 19 May 2024 12:39:11 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact158

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Notched Boxplots] [workshop 3] [2007-10-26 13:31:48] [e9ffc5de6f8a7be62f22b142b5b6b1a8]
F    D  [Notched Boxplots] [Q1 - Notched Boxplot] [2008-11-03 09:57:32] [a7f04e0e73ce3683561193958d653479]
F RMPD      [Bivariate Kernel Density Estimation] [Various EDA topic...] [2008-11-11 13:04:27] [f1a30f1149cef3ef3ef69d586c6c3c1c] [Current]
F    D        [Bivariate Kernel Density Estimation] [Various EDA topic...] [2008-11-11 13:51:35] [a7f04e0e73ce3683561193958d653479]
F RMPD        [Hierarchical Clustering] [Various EDA topic...] [2008-11-11 14:17:41] [a7f04e0e73ce3683561193958d653479]
Feedback Forum
2008-11-20 09:10:49 [An De Koninck] [reply] 
Er is inderdaad sprake van een vrij grote samenhang tussen het spaarvermogen van de gezinnen en de economische situatie, wat me los van cijfers en statistieken logisch lijkt.
Naast het getal van de correlatie van 0.59143406672748 kan je dit ook duidelijk zien aan de grafiek.
Er is één grote cluster, waaruit het positief verband tussen deze twee variabelen reeds blijkt.
In de cluster zie je veel hoogtelijnen, en je ziet dan duidelijk dat er een hoge dichtheid is. Vele punten zijn immers gecentraliseerd in het midden van deze cluster. De kleuren gaan van rood (heel hoge dichtheid)over oranje en geel naar groen (kleinere dichtheid).
Er is eigenlijk maar één hoogtelijn (de buitenste) die er was uitspringt en die een staart vertoont.
2008-11-24 11:28:14 [Steven Hulsmans] [reply] 
Er is inderdaad een grote correlatie tussen de 2 variabelen. We zien dt ook aan de hoogtelijnen, want deze verbinden punten met een gelijke dichtheid met mekaar.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
10
12
12
13
17
12
15
12
14
19
16
17
16
19
17
17
20
18
16
19
18
23
20
20
15
17
16
15
10
13
10
19
21
17
16
17
14
18
17
14
15
16
11
15
13
17
16
9
17
15
12
12
12
12
4
7
4
3
3
0
5
Dataseries Y:
Download CSV
Histogram 
19
14
11
13
21
10
23
23
27
30
28
25
21
29
20
24
23
16
17
19
19
21
25
18
8
7
5
10
0
13
13
17
20
19
16
16
16
20
23
18
20
26
25
16
22
23
20
27
25
26
23
13
13
14
1
7
5
9
11
6
0

Tables (Output of Computation)





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=23435&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=23435&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23435&T=0

As an alternative you can also use a QR Code:  
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 axis1.55385919601336
y axis3.54409029282468
Correlation
correlation used in KDE0.59143406672748
correlation(x,y)0.59143406672748

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 1.55385919601336 \tabularnewline
y axis & 3.54409029282468 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.59143406672748 \tabularnewline
correlation(x,y) & 0.59143406672748 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23435&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]1.55385919601336[/C][/ROW]
[ROW][C]y axis[/C][C]3.54409029282468[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.59143406672748[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.59143406672748[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23435&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23435&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Bandwidth
x axis1.55385919601336
y axis3.54409029282468
Correlation
correlation used in KDE0.59143406672748
correlation(x,y)0.59143406672748


Figures (Output of Computation)

Input Parameters & R Code

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')