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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 13:18:17 -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/t1226434849p9fnvc7vjle6g0d.htm/, Retrieved Sun, 19 May 2024 05:04:46 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23928, Retrieved Sun, 19 May 2024 05:04:46 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact104
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Box-Cox Linearity Plot] [Q4] [2008-11-11 20:18:17] [787873b6436f665b5b192a0bdb2e43c9] [Current]
Feedback Forum
2008-11-14 10:39:30 [Tamara Witters] [reply
Het verschil met de vorige vraag is dat er nu wel een maximum bereikt werd. In dit geval kunnen we wel zien waar de correlatiie het grootst is nl bij lambda 1.
2008-11-23 20:10:30 [Isabel Wilms] [reply
box-cox normality plot: hier transformeren we ook de tijdreeks. Omdat we eerst geen normaalverdeling hadden en voor sommige tests een normaalverdeling aangewezen is, proberen we de r-code te veranderen zodat we een normaalverdeling krijgen. Hier veranderen we dus niet de x (zoals bij box-cox lineairity plot) maar wel de y gaan we bewerken. Dit wordt dan, ((y tot de macht lambda)-1)/ lambda. Hier zoeken we dan ook het max uit de grafiek, dus de lambdawaarde met de hoogste correlatie. (wanneer deze niet in de grafiek ligt, moet je de grafieklimieten aanpassen). Deze waarde gebruiken we dan om de tijdreeks normaal te maken.

Hier wordt een max correlatie bereikt bij de lambdawaarde 1, deze 1 gebruiken we om de tijdreeks normaal te maken.

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Dataseries X:
13.92
13.22
13.31
12.91
13.19
12.92
13.43
13.72
13.97
14.91
14.46
14.12
14.23
15.04
14.80
14.49
15.14
14.34
15.12
15.14
14.34
14.36
14.91
15.56
16.50
15.57
15.14
15.19
15.07
14.48
14.27
14.72
14.65
14.38
13.95
14.85
14.87
14.83
15.03
15.47
16.21
16.55
17.04
17.22
17.47
17.75
17.84
18.47
18.38
18.55
18.39
18.88
20.21
19.67
20.09
18.78
19.74
20.64
20.34
21.75
22.10
22.81
22.91
22.46
21.78
25.05
23.70
23.02
24.34
24.15
25.85
26.42
26.54
26.36
26.99
27.52
26.63
26.26
24.86
26.84
26.57
24.67
27.24
27.77
27.61
27.27
28.46
26.97
29.95
29.88
29.67
31.19
30.24
30.03
31.02
30.45
31.70
32.10
32.32
32.18
33.43
33.07
35.32
35.17
35.29
37.89
38.32
37.07
39.77
39.20
40.46
44.95
41.69
41.88
45.86
Dataseries Y:
13.92
13.22
13.31
12.91
13.19
12.92
13.43
13.72
13.97
14.91
14.46
14.12
14.23
15.04
14.80
14.49
15.14
14.34
15.12
15.14
14.34
14.36
14.91
15.56
16.50
15.57
15.14
15.19
15.07
14.48
14.27
14.72
14.65
14.38
13.95
14.85
14.87
14.83
15.03
15.47
16.21
16.55
17.04
17.22
17.47
17.75
17.84
18.47
18.38
18.55
18.39
18.88
20.21
19.67
20.09
18.78
19.74
20.64
20.34
21.75
22.10
22.81
22.91
22.46
21.78
25.05
23.70
23.02
24.34
24.15
25.85
26.42
26.54
26.36
26.99
27.52
26.63
26.26
24.86
26.84
26.57
24.67
27.24
27.77
27.61
27.27
28.46
26.97
29.95
29.88
29.67
31.19
30.24
30.03
31.02
30.45
31.70
32.10
32.32
32.18
33.43
33.07
35.32
35.17
35.29
37.89
38.32
37.07
39.77
39.20
40.46
44.95
41.69
41.88
45.86




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001

\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 & 'Herman Ole Andreas Wold' @ 193.190.124.10:1001 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23928&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]'Herman Ole Andreas Wold' @ 193.190.124.10:1001[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23928&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23928&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 computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'Herman Ole Andreas Wold' @ 193.190.124.10:1001







Box-Cox Linearity Plot
# observations x115
maximum correlation1
optimal lambda(x)1
Residual SD (orginial)6.52792497401619e-16
Residual SD (transformed)1.73278575982415e-15

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 115 \tabularnewline
maximum correlation & 1 \tabularnewline
optimal lambda(x) & 1 \tabularnewline
Residual SD (orginial) & 6.52792497401619e-16 \tabularnewline
Residual SD (transformed) & 1.73278575982415e-15 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23928&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]115[/C][/ROW]
[ROW][C]maximum correlation[/C][C]1[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]1[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]6.52792497401619e-16[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]1.73278575982415e-15[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23928&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23928&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 x115
maximum correlation1
optimal lambda(x)1
Residual SD (orginial)6.52792497401619e-16
Residual SD (transformed)1.73278575982415e-15



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')