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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_cloud.wasp
Title produced by softwareTrivariate Scatterplots
Date of computationMon, 10 Nov 2008 11:41:03 -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/10/t1226342527x4ewy4mm3s57hkm.htm/, Retrieved Sun, 19 May 2024 11:37:28 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23184, Retrieved Sun, 19 May 2024 11:37:28 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact123
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Trivariate Scatterplots] [Trivariate Scatte...] [2008-11-10 18:41:03] [63302faa1e3976bf98d1de42298c0b24] [Current]
Feedback Forum
2008-11-19 13:55:25 [Mehmet Yilmaz] [reply
De student maakt hier de berekeningen maar geeft geen uitleg.

Trivariate Scatterplots:
Deze R-module biedt veel voordelen. Het maakt verschillende berekeningen in 1 keer met o. a de Bivarlate kernel density.
Je krijgt eerst 3 vierkanten te zien die telkens het verband tussen 3 variabelen weergeven. Het nadeel hiervan is dat je de afstand tussen de punten niet goed kan inschatten.
Vervolgens is er het 2-dimensioneel scatterplot. Je kan hieruit afleiden of een variabele een normaalverdeling heeft en of er correlatie is tussen de verschillende variabelen. Hoe dichter de punten hoe groter de waarschijnlijkheid. Het nadeel van deze methode is dat je een vertekend beeld kan krijgen.
2008-11-23 14:44:41 [Chi-Kwong Man] [reply
Trivariate Scatterplots: Hier wordt er een 3-dimensioneel projectie op een 2-dimensioneel grafiek gedaan. Dit kan een vertekend beeld geven.

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Dataseries X:
105,15
105,24
105,57
105,62
106,17
106,27
106,41
106,94
107,16
107,32
107,32
107,35
107,55
107,87
108,37
108,38
107,92
108,03
108,14
108,3
108,64
108,66
109,04
109,03
109,03
109,54
109,75
109,83
109,65
109,82
109,95
110,12
110,15
110,21
109,99
110,14
110,14
110,81
110,97
110,99
109,73
109,81
110,02
110,18
110,21
110,25
110,36
110,51
110,6
110,95
111,18
111,19
111,69
111,7
111,83
111,77
111,73
112,01
111,86
112,04
Dataseries Y:
118,63
121,83
119,97
124,98
129,99
126,6
121,71
119,28
122,63
116,74
114,23
113,23
112,75
113,54
115,3
121,05
119,51
116,78
117,17
117,5
119,65
120,97
117,18
116,87
119,46
122,52
124,1
118,39
113,1
113,94
114,58
118,79
120,44
118,37
118,44
117,93
117,76
118,29
121,11
124,86
131,17
130,16
131,76
134,7
135,32
140,23
136,31
131,62
128,9
133,89
138,21
146,12
144,69
149,18
156,6
158,87
164,85
162,89
153,31
150,91
Dataseries Z:
107,06
109,98
112,64
112,89
112,9
112,9
112,91
112,99
113,01
113
113,07
113,07
113,21
114,13
114,59
114,88
115,3
115,33
115,36
115,41
115,43
115,43
115,43
115,43
115,56
115,88
116,02
116,09
116,28
116,28
116,28
116,25
116,07
116,08
116,07
115,92
116,07
117,22
117,75
117,78
117,78
117,81
117,81
117,74
117,75
117,76
117,76
117,75
117,8
118,09
118,95
119,03
118,9
118,9
118,9
118,87
118,88
119,36
119,39
119,47




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23184&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]3 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=23184&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23184&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ 72.249.76.132



Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Wagens ; par6 = Brandstof ; par7 = Fietsen ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Wagens ; par6 = Brandstof ; par7 = Fietsen ;
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()