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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_boxcoxlin.wasp
Title produced by softwareBox-Cox Linearity Plot
Date of computationTue, 11 Nov 2008 12:41:32 -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/11/t1226432574ampv0c25rmf36t0.htm/, Retrieved Sun, 19 May 2024 09:40:12 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23891, Retrieved Sun, 19 May 2024 09:40:12 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact105

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] [cf9c64468d04c2c4dd548cc66b4e3677]
F RMPD    [Box-Cox Linearity Plot] [Various EDA topic...] [2008-11-11 19:41:32] [e4cb5a8878d0401c2e8d19a1768b515b] [Current]
Feedback Forum
2008-11-22 17:40:19 [Kenny Simons] [reply] 
Een Box-Cox linearity plot is een manier om een tijdreeks te transformeren, zodat je een verband lineair kan maken. Om nu een verband lineair te maken, moet je gaan zoeken of er een lambda parameter bestaat, zodat je de tijdreeks op een juiste manier kan transformeren.

Grafisch moet je de lambdawaarde kiezen met de maximumwaarde, als je geen maximum kan aflezen, dan kan je uiteraard ook geen conclusies trekken.

Vraag 4 was eigenlijk simultaan aan vraag 3.

Post a new message

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 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 & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23891&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]2 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=23891&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23891&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 time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






Box-Cox Linearity Plot
# observations x60
maximum correlation0.927676036762314
optimal lambda(x)2
Residual SD (orginial)2.4509174949643
Residual SD (transformed)2.44035175621964

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 60 \tabularnewline
maximum correlation & 0.927676036762314 \tabularnewline
optimal lambda(x) & 2 \tabularnewline
Residual SD (orginial) & 2.4509174949643 \tabularnewline
Residual SD (transformed) & 2.44035175621964 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23891&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]60[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.927676036762314[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]2[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]2.4509174949643[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]2.44035175621964[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23891&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23891&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 x60
maximum correlation0.927676036762314
optimal lambda(x)2
Residual SD (orginial)2.4509174949643
Residual SD (transformed)2.44035175621964


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')