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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 computationMon, 04 Oct 2010 07:40:49 +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/Oct/04/t1286177972yp6631v9melb6a6.htm/, Retrieved Sun, 28 Apr 2024 11:09:41 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=80428, Retrieved Sun, 28 Apr 2024 11:09:41 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact1077

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Bivariate Kernel Density Estimation] [Connected vs Sepa...] [2010-10-04 07:40:49] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
F         [Bivariate Kernel Density Estimation] [Connected vs Sepa...] [2010-10-08 13:51:26] [aeb27d5c05332f2e597ad139ee63fbe4]
-         [Bivariate Kernel Density Estimation] [WS 2 Task 7] [2010-10-09 12:23:06] [afe9379cca749d06b3d6872e02cc47ed]
-         [Bivariate Kernel Density Estimation] [Task 7 probability] [2010-10-10 09:42:24] [87d60b8864dc39f7ed759c345edfb471]
-         [Bivariate Kernel Density Estimation] [Workshop 2 – Pr...] [2010-10-11 18:41:05] [603e2f5305d3a2a4e062624458fa1155]
-         [Bivariate Kernel Density Estimation] [] [2010-10-11 21:07:20] [f4dc4aa51d65be851b8508203d9f6001]
F         [Bivariate Kernel Density Estimation] [Task 7b] [2010-10-11 23:15:40] [48146708a479232c43a8f6e52fbf83b4]
-   PD    [Bivariate Kernel Density Estimation] [Paper - Bivariate...] [2010-11-12 21:24:52] [6ff9fb24bdca608d2f4f1f9db3f6445e]
-   PD      [Bivariate Kernel Density Estimation] [computation17] [2010-11-16 11:51:47] [dc30d19c3bc2be07fe595ad36c2cf923]
-   PD    [Bivariate Kernel Density Estimation] [Paper - Bivariate...] [2010-11-12 21:24:52] [6ff9fb24bdca608d2f4f1f9db3f6445e]
- R  D    [Bivariate Kernel Density Estimation] [Kernel Density Pl...] [2010-12-17 20:27:42] [87116ee6ef949037dfa02b8eb1a3bf97]
- R P     [Bivariate Kernel Density Estimation] [Workshop 2 - Task 7] [2011-10-07 08:41:41] [fbaf17a8836493f6de0f4e0e997711e1]
- R P     [Bivariate Kernel Density Estimation] [Task 7] [2011-10-07 14:10:38] [088a244c534fec2347300624359db3c1]
- RM      [Bivariate Kernel Density Estimation] [] [2011-10-07 15:33:21] [ee8c3a74bf3b349877806e9a50913c60]
- RM      [Bivariate Kernel Density Estimation] [] [2011-10-08 13:32:16] [06c08141d7d783218a8164fd2ea166f2]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-08 21:37:00] [a9a952c1cbc7081c25fad93a34aab827]
-    D      [Bivariate Kernel Density Estimation] [PAPER: werklooshe...] [2011-12-20 21:18:56] [f0cb027b41af06223bae4ee77475f3bc]
- R P     [Bivariate Kernel Density Estimation] [Task 7 b] [2011-10-09 12:43:10] [1321c14511baa35aebbc5dda661708fe]
- R P     [Bivariate Kernel Density Estimation] [Task 7.2] [2011-10-09 17:49:33] [80bca13c5f9401fbb753952fd2952f4a]
-           [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2012-12-05 15:47:08] [f8da7216ca6ab56f40bda6dd57b36742]
- R P     [Bivariate Kernel Density Estimation] [taak 7a] [2011-10-10 08:36:34] [c4580079d5d2b3f0ba412f27cdc441be]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-10 16:28:58] [aefb5c2d4042694c5b6b82f93ac1885a]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-10 16:30:55] [aefb5c2d4042694c5b6b82f93ac1885a]
- R P     [Bivariate Kernel Density Estimation] [Workshop 3 - Task 9] [2011-10-10 19:28:17] [6a3e51c0c7ab195427042dfaef1df5a0]
- RM      [Bivariate Kernel Density Estimation] [task 7] [2011-10-11 08:21:30] [379dab8110dbf77cfcc4b7951c3a599f]
- R P     [Bivariate Kernel Density Estimation] [bivairate kernel ...] [2011-10-11 08:39:38] [f2efe7b37bd12d7944b0ea184fe3529a]
- RM      [Bivariate Kernel Density Estimation] [ws2 -task 7] [2011-10-11 08:52:35] [7e261c986c934df955dd3ac53e9d45c6]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-11 10:38:28] [72554d79606dc183296fd485368f0ec1]
- R P     [Bivariate Kernel Density Estimation] [Vraag 7] [2011-10-11 11:00:03] [c505444e07acba7694d29053ca5d114e]
- RM      [Bivariate Kernel Density Estimation] [] [2011-10-11 12:55:35] [ad2d4c5ace9fa07b356a7b5098237581]
- RM      [Bivariate Kernel Density Estimation] [WS2 - Task 7] [2011-10-11 15:19:35] [74b1e5a3104ff0b2404b2865a63336ad]
- RM      [Bivariate Kernel Density Estimation] [WS2 - Task 7] [2011-10-11 15:19:35] [74b1e5a3104ff0b2404b2865a63336ad]
- R P     [Bivariate Kernel Density Estimation] [Workshop 2 - Fabr...] [2011-10-11 15:20:05] [60c0c94f647e2c90e494ab0f2a2f1926]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-11 15:40:08] [a1957df0bc37aec4aa3c994e6a08412c]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-11 15:52:26] [a1957df0bc37aec4aa3c994e6a08412c]
- RM      [Bivariate Kernel Density Estimation] [Workshop 2 Task 7.2] [2011-10-11 16:32:27] [59e9c089bdd600b584669dddc48fbcc3]
- RM      [Bivariate Kernel Density Estimation] [Workshop 2 Task 7.2] [2011-10-11 16:34:39] [59e9c089bdd600b584669dddc48fbcc3]
- RM      [Bivariate Kernel Density Estimation] [] [2011-10-11 17:11:06] [e21b9c93af4eb9605ecfaf58a559e5ab]
- RM      [Bivariate Kernel Density Estimation] [Task 7.2] [2011-10-11 19:45:49] [e51846b5e808727784baa8d5c183dcd5]
- RM      [Bivariate Kernel Density Estimation] [] [2011-10-11 21:04:35] [50e3859e0b739a5118d466e989dfc0cb]
- RM      [Bivariate Kernel Density Estimation] [WS 2 - 7.2 ] [2011-10-17 10:59:04] [2c786c21adba4dd4c8af44dce5258f06]
- RM      [Bivariate Kernel Density Estimation] [] [2011-12-02 16:50:36] [ee8c3a74bf3b349877806e9a50913c60]
- RM      [Bivariate Kernel Density Estimation] [Paper 1.1.2. Conn...] [2011-12-06 18:22:06] [9d4f280afcb4ecc352d7c6f913a0a151]
- R PD    [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2011-12-16 14:05:29] [379dab8110dbf77cfcc4b7951c3a599f]
- R P     [Bivariate Kernel Density Estimation] [Connected vs sepe...] [2011-12-16 18:31:05] [c035d973aa8488be257660c2dc4ec375]
- R P     [Bivariate Kernel Density Estimation] [] [2011-12-18 13:23:13] [bdca8f3e7c3554be8c1291e54f61d441]
- R PD    [Bivariate Kernel Density Estimation] [bvfbvbvv] [2011-12-21 17:59:44] [a9671b130b33f9fcb98554992ce4582f]
- RM      [Bivariate Kernel Density Estimation] [Connected vs Sepa...] [2011-12-22 15:15:01] [1321c14511baa35aebbc5dda661708fe]
- R PD    [Bivariate Kernel Density Estimation] [] [2011-12-22 15:53:54] [18e0b15711387f6270134133fa101957]

[Truncated]
Feedback Forum
2010-10-13 16:32:31 [Pascal Wijnen] [reply] 
De student komt hier tot een correcte interpretatie van de Bivariate Kernel Density plot. Er kon wel nog iets meer geschreven worden over het belang van de ellipsvormigheid van de hoogtelijnen. Ook gaat de voorkeur van de student naar de scatter plot, iets wat ik niet zou doen.
2010-10-15 13:21:02 [] [reply] 
Inderdaad, ,ik zou ook opteren voor de Bivariate Kernel Densisty Plot, enerzijds omdat je daar beter de hoge concentraties ziet (ook in combinatie met de kleuren) en je ziet er ook duidelijk het lineaire verband in, wat het veel makkelijker maakt dan bij de Scatter Plot.
2010-10-15 13:22:10 [] [reply] 
Inderdaad, ,ik zou ook opteren voor de Bivariate Kernel Densisty Plot, enerzijds omdat je daar beter de hoge concentraties ziet (ook in combinatie met de kleuren) en je ziet er ook duidelijk het lineaire verband in, wat het veel makkelijker maakt dan bij de Scatter Plot.
2010-10-17 09:06:23 [201022de16daa1dc0c172603d7d3cd57] [reply] 
De student maakt de juiste beschrijving. Enkel hoe dichter bij het centrum hoe lichter de kleur, namelijk wit. Ook is er niets vermeld over de hoogtelijnen. De keuze tussen de twee vind ik eerder persoonlijke voorkeur.

Post a new message

Dataset

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Tables (Output of Computation)





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=80428&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=80428&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80428&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 time2 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






Bandwidth
x axis1.49671962080113
y axis1.30492764651663
Correlation
correlation used in KDE0.52595026181494
correlation(x,y)0.52595026181494

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 1.49671962080113 \tabularnewline
y axis & 1.30492764651663 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.52595026181494 \tabularnewline
correlation(x,y) & 0.52595026181494 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80428&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]1.49671962080113[/C][/ROW]
[ROW][C]y axis[/C][C]1.30492764651663[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.52595026181494[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.52595026181494[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80428&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80428&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 axis1.49671962080113
y axis1.30492764651663
Correlation
correlation used in KDE0.52595026181494
correlation(x,y)0.52595026181494


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')