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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 computationTue, 11 Nov 2008 10:33:56 -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/11/t1226424930ugghpvfx4ma8f4a.htm/, Retrieved Sun, 19 May 2024 10:05:53 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23764, Retrieved Sun, 19 May 2024 10:05:53 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

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...] [2008-11-11 17:33:56] [e4cb5a8878d0401c2e8d19a1768b515b] [Current]
F RMPD    [Hierarchical Clustering] [Various EDA topic...] [2008-11-11 19:14:32] [cf9c64468d04c2c4dd548cc66b4e3677]
F RMPD    [Box-Cox Linearity Plot] [Various EDA topic...] [2008-11-11 19:41:32] [cf9c64468d04c2c4dd548cc66b4e3677]
Feedback Forum
2008-11-22 17:27:05 [Kenny Simons] [reply] 
Deze vraag heb je goed opgelost, toch moet je opletten met de kubussen, deze zorgen altijd voor vertekening omdat deze driedimensionaal zijn. Je kan namelijk niet goed zien hoe de afstanden tussen de punten zich verhouden. Hierdoor is het beter te gaan zien naar de tweedimensionale scatterplots. Hieruit kan je beter aflezen of er een relatie is tussen bepaalde tijdreeksen of niet.
Zoals je zelf goed opgemerkt hebt, is de relatie tussen X en Y het sterkst. Dit kan je goed aflezen op de tweedimensionale scatterplot alsook op de eerste bivariate density plot.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
101.5
101.3
99.3
100.6
101.2
99.8
100.6
101.1
101.2
101.5
102.2
102.5
101.4
103.8
105.2
105.3
104.4
104.9
106.9
107.6
106.7
106.1
106.3
105.8
104.4
103.8
102.4
103.3
103.5
104.5
103.5
103.9
103.1
102.2
104.7
105.9
106.6
106.6
107.5
107.2
109
108.4
107
108
110.8
110.9
109.7
111
111.5
111
111.8
111.4
110.8
111.9
112.9
111.8
111
112.3
112.4
111.1
Dataseries Y:
Download CSV
Histogram 
110.4
112.9
109.4
111.9
108.9
113.8
114.5
113.2
111
114.6
113.1
113.2
115.1
117.6
117.8
115.7
115.7
118.3
117.9
117.3
119.4
117.1
119
120
118.9
116
115.6
119.7
119.7
120.8
120
120.2
121.7
116.9
122.4
122.6
123.7
120.9
124.2
122.6
125.7
123.1
122.2
126.2
124.4
127.8
124.2
126.7
126.1
128.2
130.4
130.2
129.2
129.7
131
129.2
131.1
132.9
135.2
132.3
Dataseries Z:
Download CSV
Histogram 
92.1
88.5
84.6
87
83.6
84.8
84.1
84.1
80.5
82.6
85.6
83.3
86.1
84.7
85.7
84.9
84.2
85.2
86.1
86
84.5
87.2
83.5
81.9
78.5
81.1
79.2
80.9
81.8
79.4
83.4
81.1
79.8
79.7
84
83.7
83.5
83.6
86
86.8
86.9
89
87.8
86.8
88.8
85.9
86.7
87.9
88.5
88.7
88.1
85.7
86.1
85.7
84.3
86.4
85.4
86.9
85.6
86.4

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=23764&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=23764&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23764&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 = totale verwerkende industrie ; par6 = voeding en genotsmiddelen ; par7 = textiel kleding leer ;
Parameters (R input):
par1 = 50 ; par2 = 50 ; par3 = Y ; par4 = Y ; par5 = totale verwerkende industrie ; par6 = voeding en genotsmiddelen ; par7 = textiel kleding leer ;
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()