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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 05:27:32 -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/t1226320106mhnrj902h3xahzx.htm/, Retrieved Mon, 20 May 2024 02:42:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22993, Retrieved Mon, 20 May 2024 02:42:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact170

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] [Trivariate Scatte...] [2008-11-10 12:27:32] [e515c0250d6233b5d2604259ab52cebe] [Current]
- RMPD    [Testing Mean with known Variance - Critical Value] [Q1 Pork quality test] [2008-11-10 12:49:50] [5161246d1ccc1b670cc664d03050f084]
F RMPD    [Testing Mean with known Variance - Critical Value] [q1 pork quality test] [2008-11-10 12:51:44] [5161246d1ccc1b670cc664d03050f084]
F RMPD    [Testing Mean with known Variance - p-value] [Q2 pork quality test] [2008-11-10 12:58:58] [5161246d1ccc1b670cc664d03050f084]
F RMPD    [Testing Mean with known Variance - Type II Error] [Q3 pork qality test] [2008-11-10 13:08:48] [5161246d1ccc1b670cc664d03050f084]
F RMPD    [Testing Mean with known Variance - Sample Size] [q4 pork quality test] [2008-11-10 13:17:48] [5161246d1ccc1b670cc664d03050f084]
F RMPD    [Testing Population Mean with known Variance - Confidence Interval] [q5 pork quality test] [2008-11-10 13:23:22] [5161246d1ccc1b670cc664d03050f084]
F RMPD    [Testing Sample Mean with known Variance - Confidence Interval] [q6 pork quality test] [2008-11-10 13:30:42] [5161246d1ccc1b670cc664d03050f084]
Feedback Forum
2008-11-21 11:56:27 [Hidde Van Kerckhoven] [reply] 
De trivariate scatterplot geeft 3D de correlatie weer tussen 3 verschillende variabelen. Daaronder zijn de 3D tekeningen 2D toegepast, dit geeft soms een vertekend beeld. Ik wil hier wel op wijzen dat deze plots niet allemaal juist zijn! Zoals we bij partiele correlatie hebben gezien is er een duidelijk negatief verband tussen de variabelen x en Y.. Hier toont de plot ons een duidelijk postief verband... Opletten dus!
2008-11-23 16:30:09 [Davy De Nef] [reply] 
De student berekent hier de triviate scatterplot. Hier worden 3 variabelen weergegeven in de vorm van een kubus. Een 3D weergave op een 2 dimensionaal scherm geeft echter vaak een vertekend beeld. Bijgevolg is het niet makkelijk om juiste conclusies te trekken uit deze kubus. Om dit te vergemakkelijken wordt de kubus weergegeven vanuit 3 perspectieven. In de 4de kader wordt een doorsnede gegeven van hoe de punten liggen. Daaruit kunnen we in alle gevallen een positieve correlatie afleiden.
Verder zie je ook van de 3 variabelen de Bivariate Kernel Density plot.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
136.5
146.4
157.7
148.7
154.6
152.1
144.8
142.1
157
159.1
164
151.5
135.9
138.5
161
151.7
142.9
157.4
138.9
141
150.9
149.9
153
144.3
128.1
123.3
155.9
144.1
134.1
153.1
131
129.8
139.9
135.6
126.8
134.4
113.5
107.5
133.8
119
125.9
130.1
114.2
111.6
131.2
124.1
127.1
123.4
100.7
100.3
121.6
110.5
110.3
122.7
102.6
101.8
113.6
107.2
116.8
112.5
Dataseries Y:
Download CSV
Histogram 
154.2
175.8
187.6
169.9
182.3
179.4
171.5
166.6
171.8
164.3
172.6
163
142.2
149.7
168.4
156.5
150.4
170.6
150.1
151.5
159.5
155.2
154.8
156.5
134.2
135.9
156.4
144.2
143.7
164.8
140.2
132.2
162
145
146.3
145.5
124.4
126
146.8
130.8
133.6
145.7
122.8
118.5
149.5
129.9
134.8
133.3
117.2
120.2
136.4
118.5
126.7
137.5
116.3
113.3
136.6
112.9
128.6
126.8
Dataseries Z:
Download CSV
Histogram 
147.5
164.7
176.2
161.8
171.7
169
161.4
157.2
166.2
162.1
169.1
158.4
139.7
145.2
165.3
154.4
147.4
165.3
145.7
147.2
156.1
152.9
153.8
151.7
131.8
131
155.8
143.8
139.8
160.1
136.5
131
153.7
141.3
138.9
141.2
120.3
118.9
141.7
126.2
130.6
139.8
119.5
115.8
142.6
127.7
131.8
129.5
111.1
112.6
130.8
115.4
120.5
131.9
111.2
108.9
128.1
110.7
124.1
121.5

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time6 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 & 6 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22993&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]6 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=22993&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22993&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 time6 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 = 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()