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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 computationMon, 10 Nov 2008 03:32:47 -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/t1226313224on2c5tyr0czo91k.htm/, Retrieved Sun, 19 May 2024 21:02:31 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22915, Retrieved Sun, 19 May 2024 21:02:31 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact212

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] [Workshop 4 - EDA ...] [2007-11-01 08:12:46] [b3bb3ec527e23fa7d74d4348b38c8499]
F RMPD    [Trivariate Scatterplots] [Trivariate Scatte...] [2008-11-10 10:32:47] [6aa66640011d9b98524a5838bcf7301d] [Current]
Feedback Forum
2008-11-19 15:10:14 [Mehmet Yilmaz] [reply] 
De student geeft de berekening maar geen uitleg.

Trivariate Scatterplots:
Hiermee kan men verschillende berekeningen maken 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.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
4,43
4,61
4,54
4,2
4,08
3,95
4,19
4,23
3,89
3,92
4,14
4,24
4,08
4,37
4,43
4,3
4,27
4,06
3,96
4,21
4,31
4,35
4,25
4,06
4
3,87
3,71
3,63
3,48
3,6
3,66
3,45
3,3
3,14
3,21
3,12
3,14
3,4
3,42
3,29
3,49
3,52
3,81
4,03
3,98
4,1
3,96
3,83
3,72
3,82
3,76
3,98
4,14
4
4,13
4,28
4,46
4,63
4,49
4,41
4,5
4,39
4,33
4,45
4,17
4,13
4,33
4,47
4,63
4,9
Dataseries Y:
Download CSV
Histogram 
88,3
88,6
91
91,5
95,4
98,7
99,9
98,6
100,3
100,2
100,4
101,4
103
109,1
111,4
114,1
121,8
127,6
129,9
128
123,5
124
127,4
127,6
128,4
131,4
135,1
134
144,5
147,3
150,9
148,7
141,4
138,9
139,8
145,6
147,9
148,5
151,1
157,5
167,5
172,3
173,5
187,5
205,5
195,1
204,5
204,5
201,7
207
206,6
210,6
211,1
215
223,9
238,2
238,9
229,6
232,2
222,1
221,6
227,3
221
213,6
243,4
253,8
265,3
268,2
268,5
266,9
Dataseries Z:
Download CSV
Histogram 
109.1
113.8
97.4
72.5
82.7
88.9
105.9
100.8
94
105
58.5
87.6
113.1
112.5
89.6
74.5
82.7
90.1
109.4
96
89.2
109.1
49.1
92.9
107.7
103.5
91.1
79.8
71.9
82.9
90.1
100.7
90.7
108.8
44.1
93.6
107.4
96.5
93.6
76.5
76.7
84
103.3
88.5
99
105.9
44.7
94
107.1
104.8
102.5
77.7
85.2
91.3
106.5
92.4
97.5
107
51.1
98.6
102.2
114.3
99.4
72.5
92.3
99.4
85.9
109.4
97.6
104.7

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 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 & 3 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22915&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]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22915&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22915&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 time3 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Variable X ; par6 = Variable Y ; par7 = Variable Z ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = Variable X ; par6 = Variable Y ; par7 = Variable Z ;
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()