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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 07:42:21 +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/t1286178077wdn64c9x2pttgq7.htm/, Retrieved Sat, 27 Apr 2024 17:06:37 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=80429, Retrieved Sat, 27 Apr 2024 17:06:37 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact837

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Bivariate Kernel Density Estimation] [Connected vs Sepa...] [2010-10-04 07:42:21] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-         [Bivariate Kernel Density Estimation] [Connected & Separ...] [2010-10-08 14:02:53] [aeb27d5c05332f2e597ad139ee63fbe4]
-         [Bivariate Kernel Density Estimation] [] [2010-10-11 10:41:25] [033eb2749a430605d9b2be7c4aac4a0c]
-         [Bivariate Kernel Density Estimation] [Task 8 bivariate ...] [2010-10-11 15:27:23] [87d60b8864dc39f7ed759c345edfb471]
-         [Bivariate Kernel Density Estimation] [task 9] [2010-10-11 21:29:46] [f4dc4aa51d65be851b8508203d9f6001]
F         [Bivariate Kernel Density Estimation] [Task 8b] [2010-10-11 23:39:56] [48146708a479232c43a8f6e52fbf83b4]
-    D    [Bivariate Kernel Density Estimation] [Popularity vs Kno...] [2010-11-28 16:32:46] [26379b86c25fbf0febe6a7a428e65173]
-   PD      [Bivariate Kernel Density Estimation] [Bivariate Kernel ...] [2010-11-28 16:40:22] [26379b86c25fbf0febe6a7a428e65173]
- R       [Bivariate Kernel Density Estimation] [Task 8] [2011-10-07 14:21:31] [088a244c534fec2347300624359db3c1]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-08 21:38:39] [a9a952c1cbc7081c25fad93a34aab827]
- R P     [Bivariate Kernel Density Estimation] [Week 2 Opdracht 8] [2011-10-09 08:41:01] [d2d464c5b110c95dc0c66eb9ae81f8ec]
- R P     [Bivariate Kernel Density Estimation] [Task 8 b] [2011-10-09 12:51:10] [1321c14511baa35aebbc5dda661708fe]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-10 16:58:32] [aefb5c2d4042694c5b6b82f93ac1885a]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-10 17:18:05] [06c08141d7d783218a8164fd2ea166f2]
- R P     [Bivariate Kernel Density Estimation] [Workshop 3 - task...] [2011-10-10 19:29:08] [6a3e51c0c7ab195427042dfaef1df5a0]
- R P     [Bivariate Kernel Density Estimation] [Workshop 2 - Task 8] [2011-10-11 08:41:26] [ec29c78521a0445a37e4526edb78f709]
- R P     [Bivariate Kernel Density Estimation] [Task 8] [2011-10-11 08:49:05] [379dab8110dbf77cfcc4b7951c3a599f]
- RM      [Bivariate Kernel Density Estimation] [bivariate kernell...] [2011-10-11 09:06:05] [f2efe7b37bd12d7944b0ea184fe3529a]
- RM      [Bivariate Kernel Density Estimation] [ws 2 - task 8] [2011-10-11 09:15:01] [7e261c986c934df955dd3ac53e9d45c6]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-11 10:40:55] [72554d79606dc183296fd485368f0ec1]
- RM      [Bivariate Kernel Density Estimation] [] [2011-10-11 13:20:24] [ad2d4c5ace9fa07b356a7b5098237581]
- R P     [Bivariate Kernel Density Estimation] [Task 8,2] [2011-10-11 14:51:42] [80bca13c5f9401fbb753952fd2952f4a]
- R P     [Bivariate Kernel Density Estimation] [Workshop 2 - Fabr...] [2011-10-11 15:20:47] [60c0c94f647e2c90e494ab0f2a2f1926]
- RM      [Bivariate Kernel Density Estimation] [WS2 - Task 8] [2011-10-11 15:48:54] [74b1e5a3104ff0b2404b2865a63336ad]
- R P     [Bivariate Kernel Density Estimation] [] [2011-10-11 15:53:34] [a1957df0bc37aec4aa3c994e6a08412c]
- RM      [Bivariate Kernel Density Estimation] [Workshop 2 Task 8.2] [2011-10-11 17:04:47] [59e9c089bdd600b584669dddc48fbcc3]
- RMPD    [Exercise 1.13] [] [2011-10-11 17:09:10] [bdca8f3e7c3554be8c1291e54f61d441]
- RM      [Bivariate Kernel Density Estimation] [] [2011-10-11 17:22:55] [e21b9c93af4eb9605ecfaf58a559e5ab]
- RM      [Bivariate Kernel Density Estimation] [Task 8.2] [2011-10-11 20:05:40] [e51846b5e808727784baa8d5c183dcd5]
- R P     [Bivariate Kernel Density Estimation] [Connected - Separ...] [2011-12-22 10:57:54] [74b1e5a3104ff0b2404b2865a63336ad]
- RM      [Bivariate Kernel Density Estimation] [Connected vs Sepa...] [2011-12-22 15:15:55] [1321c14511baa35aebbc5dda661708fe]
- RM      [Bivariate Kernel Density Estimation] [Workshop 2 - Task 8] [2012-10-05 10:47:21] [37f59b7a972c225c3d32d27fed432050]
- RM      [Bivariate Kernel Density Estimation] [WS2 - taak 8b] [2012-10-05 11:40:20] [8ce6c7315af51b5eb6923c5fe455d382]
- R P     [Bivariate Kernel Density Estimation] [Kernel Density pl...] [2012-10-05 13:00:29] [87b90d598c60c012567ac118c8f3a654]
- RM      [Bivariate Kernel Density Estimation] [WS2 - Task8] [2012-10-05 13:47:18] [0604709baf8ca89a71bc0fcadc3cdffd]
- RM      [Bivariate Kernel Density Estimation] [Task 8 workshop 2] [2012-10-06 11:28:45] [8c30f4dd45e15fd207e4faf2fdf6253e]
- R P     [Bivariate Kernel Density Estimation] [Density plot - Ta...] [2012-10-06 12:50:12] [677a26acc722ccb2eb1c0d7cc83e1e7a]
- R P     [Bivariate Kernel Density Estimation] [Task 8.2] [2012-10-06 12:56:42] [9d6050326bbbd058eed49c2dec5f39c1]
- RM      [Bivariate Kernel Density Estimation] [Task 8b] [2012-10-07 18:19:16] [783d8509970888a6ec44a5a7a0d2a339]
- RM      [Bivariate Kernel Density Estimation] [Task 8 Biv. Density] [2012-10-08 10:22:56] [ed906dce46ddef36a6228cf206971146]
- RM      [Bivariate Kernel Density Estimation] [Task 9 Biv 2] [2012-10-08 10:29:10] [ed906dce46ddef36a6228cf206971146]
- R P     [Bivariate Kernel Density Estimation] [Task 8(2)] [2012-10-08 14:55:40] [f8da7216ca6ab56f40bda6dd57b36742]
- RM      [Bivariate Kernel Density Estimation] [bivariate kerel d...] [2012-10-08 15:56:54] [87b90d598c60c012567ac118c8f3a654]
- R P     [Bivariate Kernel Density Estimation] [Workshop 2 - Task 8] [2012-10-08 18:32:53] [7dcdf88f8d4909016d1a598b3c226ce5]
- R P     [Bivariate Kernel Density Estimation] [Taak 8] [2012-10-08 20:39:59] [c5e00e3d2459b4cd6380f7873395bbc5]
- R P     [Bivariate Kernel Density Estimation] [WS2 task8b] [2012-10-09 07:53:50] [d31c851fa7fbee45412c0a7bcdad10e5]
- R       [Bivariate Kernel Density Estimation] [WS2 task 9b] [2012-10-09 08:05:58] [d31c851fa7fbee45412c0a7bcdad10e5]
- RM      [Bivariate Kernel Density Estimation] [] [2012-10-09 08:52:34] [af1abf97761ac0cf7dc5c55ff51240a9]
- RM      [Bivariate Kernel Density Estimation] [Taak 8: ws2] [2012-10-09 09:30:45] [73586a5ad7cb70bd9d8f219d68ef24b6]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Dataseries Y:
Download CSV
Histogram 
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35
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31
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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 time1 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80429&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80429&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80429&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'George Udny Yule' @ 72.249.76.132






Bandwidth
x axis1.23601999273031
y axis1.23979312917482
Correlation
correlation used in KDE0.344824466097388
correlation(x,y)0.344824466097388

\begin{tabular}{lllllllll}
\hline
Bandwidth \tabularnewline
x axis & 1.23601999273031 \tabularnewline
y axis & 1.23979312917482 \tabularnewline
Correlation \tabularnewline
correlation used in KDE & 0.344824466097388 \tabularnewline
correlation(x,y) & 0.344824466097388 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=80429&T=1

[TABLE]
[ROW][C]Bandwidth[/C][/ROW]
[ROW][C]x axis[/C][C]1.23601999273031[/C][/ROW]
[ROW][C]y axis[/C][C]1.23979312917482[/C][/ROW]
[ROW][C]Correlation[/C][/ROW]
[ROW][C]correlation used in KDE[/C][C]0.344824466097388[/C][/ROW]
[ROW][C]correlation(x,y)[/C][C]0.344824466097388[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=80429&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=80429&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 axis1.23601999273031
y axis1.23979312917482
Correlation
correlation used in KDE0.344824466097388
correlation(x,y)0.344824466097388


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')