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Author's title

Author*Unverified author*
R Software Modulerwasp_boxcoxlin.wasp
Title produced by softwareBox-Cox Linearity Plot
Date of computationThu, 01 Nov 2007 05:49:12 -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/2007/Nov/01/tms3a6tf2fbulmv1193921480.htm/, Retrieved Tue, 07 May 2024 23:43:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=101, Retrieved Tue, 07 May 2024 23:43:46 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsQ3
Estimated Impact184
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Box-Cox Linearity Plot] [workshop 4 ] [2007-11-01 12:49:12] [d41d8cd98f00b204e9800998ecf8427e] [Current]
Feedback Forum
2008-11-15 15:45:31 [Philip Van Herck] [reply
Ook de grafiek omtrent de 'linear fit of orginal' en de 'linear fit of transformed data' kunnen opgenomen worden in het antwoord want hierop krijgen we te zien in welke mate de Box-Cox transformatie een effect heeft gehad. In dit geval kan er gesproken worden van een zeer miniem effect van de transformatie omdat de waarnemingen nog steeds niet sterk genoeg geconcentreerd zijn rondom de lineare regressierechte.
2008-11-19 20:15:13 [Toon Wouters] [reply
De box-cox lineairity plot transformeert de scatterplot en maakt hem lineair. En hier is ook goed een maximum waar te nemen, namelijk bij lamba gelijk aan -2 wat wil zeggen dat daar de correlatie optimaal is.

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Dataseries X:
103,1
100,6
103,1
95,5
90,5
90,9
88,8
90,7
94,3
104,6
111,1
110,8
107,2
99
99
91
96,2
96,9
96,2
100,1
99
115,4
106,9
107,1
99,3
99,2
108,3
105,6
99,5
107,4
93,1
88,1
110,7
113,1
99,6
93,6
98,6
99,6
114,3
107,8
101,2
112,5
100,5
93,9
116,2
112
106,4
95,7
96
95,8
103
102,2
98,4
111,4
86,6
91,3
107,9
101,8
104,4
93,4
100,1
98,5
112,9
101,4
107,1
110,8
90,3
95,5
111,4
113
107,5
95,9
106,3
105,2
117,2
106,9
108,2
110
96,1
100,6
Dataseries Y:
98,1
101,1
111,1
93,3
100
108
70,4
75,4
105,5
112,3
102,5
93,5
86,7
95,2
103,8
97
95,5
101
67,5
64
106,7
100,6
101,2
93,1
84,2
85,8
91,8
92,4
80,3
79,7
62,5
57,1
100,8
100,7
86,2
83,2
71,7
77,5
89,8
80,3
78,7
93,8
57,6
60,6
91
85,3
77,4
77,3
68,3
69,9
81,7
75,1
69,9
84
54,3
60
89,9
77
85,3
77,6
69,2
75,5
85,7
72,2
79,9
85,3
52,2
61,2
82,4
85,4
78,2
70,2
70,2
69,3
77,5
66,1
69
75,3
58,2
59,7




Summary of compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of compuational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 1 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101&T=0

[TABLE]
[ROW][C]Summary of compuational 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]1 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=101&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101&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 compuational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135







Box-Cox Linearity Plot
# observations x80
maximum correlation0.33917123291975
optimal lambda(x)-2
Residual SD (orginial)13.9406427423970
Residual SD (transformed)13.910389049071

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 80 \tabularnewline
maximum correlation & 0.33917123291975 \tabularnewline
optimal lambda(x) & -2 \tabularnewline
Residual SD (orginial) & 13.9406427423970 \tabularnewline
Residual SD (transformed) & 13.910389049071 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=101&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]80[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.33917123291975[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]-2[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]13.9406427423970[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]13.910389049071[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=101&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=101&T=1

As an alternative you can also use a QR Code:  

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

Box-Cox Linearity Plot
# observations x80
maximum correlation0.33917123291975
optimal lambda(x)-2
Residual SD (orginial)13.9406427423970
Residual SD (transformed)13.910389049071



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')