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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cloud.wasp
Title produced by softwareTrivariate Scatterplots
Date of computationThu, 13 Nov 2008 14:05:26 -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/13/t1226610397yuku7ypo5albx04.htm/, Retrieved Sat, 18 May 2024 23:28:30 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24830, Retrieved Sat, 18 May 2024 23:28:30 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsJonas Scheltjens
Estimated Impact132

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Trivariate Scatterplots] [Various EDA topic...] [2008-11-13 21:05:26] [f4960a11bac8b7f1cb71c83b5826d5bd] [Current]
Feedback Forum
2008-11-16 15:04:43 [074508d5a5a3592082de3e836d27af7d] [reply] 
Bij de Trivarate scatterplot willen we het verband tussen 3 variabelen gelijktijdig onderzoeken. De 3 kubussen zijn vertekend door de manier van projecteren, daarom kijken we beter naar de matrix. Hier zijn bepaalde dimensies ook vertekend omdat de 3de dimensie er ook niet bijstaat. Dus kunnen we beter naar de Bivariate Kernel density plot kijken. De hoogtelijnen stellen de dichtheid voor, en wanneer ze dicht op de lijn liggen is de correlatie hoog.
2008-11-23 15:29:42 [Alexander Hendrickx] [reply] 
De student heeft er goed aan gedaan de correlatie via 3 verschillende weergaven te verklaren, de bivariate kernell density plot en de trivariate scatterplots geven een visueel beeld van de correlatie. De correlatie en partial correlatie geven de correlatie in cijfers weer. De biavariate scatterplot geeft inderdaad een redelijk lineaire correlatie weer, het voordeel van deze plot is dat zij ook een duidelijk beeld geeft van de concentratie van de gegevens. De partial correlatie is een goede geeft de correlatie van 2 gegevens weer met telkens het constant houden van de derde.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
8
-10
-24
-19
8
24
14
7
9
-26
19
15
-1
-10
-21
-14
-27
26
23
5
19
-19
24
17
1
-9
-16
-21
-14
31
27
10
12
-23
13
26
-1
4
-16
-5
9
23
9
2
10
-29
17
9
9
-10
-23
13
13
-9
9
5
8
-18
7
4
Dataseries Y:
Download CSV
Histogram 
-7
-13
-11
-9
8
24
4
7
16
-30
26
19
2
-12
-29
-24
-16
25
22
-7
17
-29
18
15
1
6
-21
-23
-15
24
15
15
14
-25
14
21
13
4
-16
13
20
27
-8
13
12
-25
20
22
16
-12
-13
7
12
-8
12
-13
12
-25
0
18
Dataseries Z:
Download CSV
Histogram 
-6
-17
-44
-36
4
29
8
3
8
-49
32
25
-1
-20
-34
-31
-12
25
25
7
13
-40
32
14
-5
-14
-42
-24
-11
20
7
12
4
-37
19
16
2
-9
-36
-29
3
33
9
13
3
-47
18
7
16
-12
-23
-18
11
-4
17
-4
-1
-41
26
3

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 & 5 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=24830&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]5 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=24830&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24830&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 time5 seconds
R Server'Sir Ronald Aylmer Fisher' @ 193.190.124.24


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = woongebouwen ; par6 = niet-woongebouwen ; par7 = openbare werken ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = woongebouwen ; par6 = niet-woongebouwen ; par7 = openbare werken ;
R code (references can be found in the software module):
x <- array(x,dim=c(length(x),1))
colnames(x) <- par5
y <- array(y,dim=c(length(y),1))
colnames(y) <- par6
z <- array(z,dim=c(length(z),1))
colnames(z) <- par7
d <- data.frame(cbind(z,y,x))
colnames(d) <- list(par7,par6,par5)
par1 <- as.numeric(par1)
par2 <- as.numeric(par2)
if (par1>500) par1 <- 500
if (par2>500) par2 <- 500
if (par1<10) par1 <- 10
if (par2<10) par2 <- 10
library(GenKern)
library(lattice)
panel.hist <- function(x, ...)
{
usr <- par('usr'); on.exit(par(usr))
par(usr = c(usr[1:2], 0, 1.5) )
h <- hist(x, plot = FALSE)
breaks <- h$breaks; nB <- length(breaks)
y <- h$counts; y <- y/max(y)
rect(breaks[-nB], 0, breaks[-1], y, col='black', ...)
}
bitmap(file='cloud1.png')
cloud(z~x*y, screen = list(x=-45, y=45, z=35),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='cloud2.png')
cloud(z~x*y, screen = list(x=35, y=45, z=25),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='cloud3.png')
cloud(z~x*y, screen = list(x=35, y=-25, z=90),xlab=par5,ylab=par6,zlab=par7)
dev.off()
bitmap(file='pairs.png')
pairs(d,diag.panel=panel.hist)
dev.off()
x <- as.vector(x)
y <- as.vector(y)
z <- as.vector(z)
bitmap(file='bidensity1.png')
op <- KernSur(x,y, xgridsize=par1, ygridsize=par2, correlation=cor(x,y), xbandwidth=dpik(x), ybandwidth=dpik(y))
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,y)',xlab=par5,ylab=par6)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(x,y)
(r<-lm(y ~ x))
abline(r)
box()
dev.off()
bitmap(file='bidensity2.png')
op <- KernSur(y,z, xgridsize=par1, ygridsize=par2, correlation=cor(y,z), xbandwidth=dpik(y), ybandwidth=dpik(z))
op
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (y,z)',xlab=par6,ylab=par7)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(y,z)
(r<-lm(z ~ y))
abline(r)
box()
dev.off()
bitmap(file='bidensity3.png')
op <- KernSur(x,z, xgridsize=par1, ygridsize=par2, correlation=cor(x,z), xbandwidth=dpik(x), ybandwidth=dpik(z))
op
image(op$xords, op$yords, op$zden, col=terrain.colors(100), axes=TRUE,main='Bivariate Kernel Density Plot (x,z)',xlab=par5,ylab=par7)
if (par3=='Y') contour(op$xords, op$yords, op$zden, add=TRUE)
if (par4=='Y') points(x,z)
(r<-lm(z ~ x))
abline(r)
box()
dev.off()