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Description of Statistical Computation

Author's title

Author*Unverified author*
R Software Modulerwasp_boxcoxlin.wasp
Title produced by softwareBox-Cox Linearity Plot
Date of computationMon, 10 Nov 2008 04:44:43 -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/t1226317530z40nlbnvhk0v1xq.htm/, Retrieved Sun, 19 May 2024 10:07:40 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=22960, Retrieved Sun, 19 May 2024 10:07:40 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact139

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Box-Cox Linearity Plot] [Various EDA Topic...] [2008-11-10 11:44:43] [33f3d2151f6019d17feb8eee7259f239] [Current]
Feedback Forum
2008-11-19 15:14:10 [Sam De Cuyper] [reply] 
Weer geen interpretatie aan de berekeningen gegeven. De box-cox linearity plot geeft de weegave van het verband tussen 2 variabelen die met elkaar in verband staan. Het resultaat is een stijgende of een dalende rechte (bestudeerd wetmatigheid) met geconcentreerde punten. Het is de bedoeling om de variabelen te transformeren (X-variabele) en zo de scatterplot meer lineair te maken. Nu kan echter de vraag gesteld worden of de transfomatie nuttig is. Indien de grafiek een maximum vertoont zal de waarde van het maximum gekozen worden als lambda. Na transformatie is er visueel weinig verschil te merken, waardoor de transformatie onnuttig is. Ze heeft geen of toch zeer weinig effect.
2008-11-20 13:53:03 [Steven Vanhooreweghe] [reply] 
De transformatie is niet nuttig aangezien er geen verschil is met de oude en de nieuwe scatterplot.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
107,2
107
119
110,4
101,7
102,4
98,8
105,6
104,4
106,3
107,2
108,5
106,9
114,2
125,9
110,6
110,5
106,7
104,7
107,4
109,8
103,4
114,8
114,3
109,6
118,3
127,3
112,3
114,9
108,2
105,4
122,1
113,5
110
125,3
114,3
115,6
127,1
123
122,2
126,4
112,7
105,8
120,9
116,3
115,7
127,9
108,3
121,1
128,6
123,1
127,7
126,6
118,4
110
129,6
115,8
125,9
128,4
114
125,6
128,5
136,6
133,1
124,6
123,5
117,2
135,5
124,8
127,8
133,1
125,7
128,4
131,9
146,3
140,6
129,5
132,4
125,9
126,9
135,8
Dataseries Y:
Download CSV
Histogram 
80,6
104,1
108,2
93,4
71,9
94,1
94,9
96,4
91,1
84,4
86,4
88
75,1
109,7
103
82,1
68
96,4
94,3
90
88
76,1
82,5
81,4
66,5
97,2
94,1
80,7
70,5
87,8
89,5
99,6
84,2
75,1
92
80,8
73,1
99,8
90
83,1
72,4
78,8
87,3
91
80,1
73,6
86,4
74,5
71,2
92,4
81,5
85,3
69,9
84,2
90,7
100,3
79,4
84,8
92,9
81,6
76
98,7
89,1
88,7
67,1
93,6
97
100,8
80,1
80,7
89,4
81,5
73,6
90,9
97,3
84,3
65,6
87,3
90,5
82,4
80,4

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'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 & 3 seconds \tabularnewline
R Server & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22960&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22960&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22960&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






Box-Cox Linearity Plot
# observations x81
maximum correlation0.0935459167632933
optimal lambda(x)2
Residual SD (orginial)9.97296132555403
Residual SD (transformed)9.96640352065174

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 81 \tabularnewline
maximum correlation & 0.0935459167632933 \tabularnewline
optimal lambda(x) & 2 \tabularnewline
Residual SD (orginial) & 9.97296132555403 \tabularnewline
Residual SD (transformed) & 9.96640352065174 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=22960&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]81[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.0935459167632933[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]2[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]9.97296132555403[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]9.96640352065174[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=22960&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=22960&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Box-Cox Linearity Plot
# observations x81
maximum correlation0.0935459167632933
optimal lambda(x)2
Residual SD (orginial)9.97296132555403
Residual SD (transformed)9.96640352065174


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
n <- length(x)
c <- array(NA,dim=c(401))
l <- array(NA,dim=c(401))
mx <- 0
mxli <- -999
for (i in 1:401)
{
l[i] <- (i-201)/100
if (l[i] != 0)
{
x1 <- (x^l[i] - 1) / l[i]
} else {
x1 <- log(x)
}
c[i] <- cor(x1,y)
if (mx < abs(c[i]))
{
mx <- abs(c[i])
mxli <- l[i]
}
}
c
mx
mxli
if (mxli != 0)
{
x1 <- (x^mxli - 1) / mxli
} else {
x1 <- log(x)
}
r<-lm(y~x)
se <- sqrt(var(r$residuals))
r1 <- lm(y~x1)
se1 <- sqrt(var(r1$residuals))
bitmap(file='test1.png')
plot(l,c,main='Box-Cox Linearity Plot',xlab='Lambda',ylab='correlation')
grid()
dev.off()
bitmap(file='test2.png')
plot(x,y,main='Linear Fit of Original Data',xlab='x',ylab='y')
abline(r)
grid()
mtext(paste('Residual Standard Deviation = ',se))
dev.off()
bitmap(file='test3.png')
plot(x1,y,main='Linear Fit of Transformed Data',xlab='x',ylab='y')
abline(r1)
grid()
mtext(paste('Residual Standard Deviation = ',se1))
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Box-Cox Linearity Plot',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'# observations x',header=TRUE)
a<-table.element(a,n)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'maximum correlation',header=TRUE)
a<-table.element(a,mx)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'optimal lambda(x)',header=TRUE)
a<-table.element(a,mxli)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Residual SD (orginial)',header=TRUE)
a<-table.element(a,se)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Residual SD (transformed)',header=TRUE)
a<-table.element(a,se1)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')