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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, 04 Nov 2008 13:21:52 -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/04/t1225830154mj2ulafjqops0u4.htm/, Retrieved Wed, 15 May 2024 23:33:15 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=21655, Retrieved Wed, 15 May 2024 23:33:15 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact215
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Bivariate Kernel Density Estimation] [Q1 Bivariate Dens...] [2007-11-03 14:50:57] [e2ec4dc832988c648c062d4cdc574d44]
- RMPD  [Hierarchical Clustering] [WS4 Q2 dendrogram] [2007-11-05 10:02:49] [74be16979710d4c4e7c6647856088456]
F RMPD      [Box-Cox Linearity Plot] [] [2008-11-04 20:21:52] [3817f5e632a8bfeb1be7b5e8c86bd450] [Current]
F   PD        [Box-Cox Linearity Plot] [] [2008-11-09 18:32:27] [4c8dfb519edec2da3492d7e6be9a5685]
F   P         [Box-Cox Linearity Plot] [Box-cox linearity...] [2008-11-11 16:06:09] [73d6180dc45497329efd1b6934a84aba]
F   P           [Box-Cox Linearity Plot] [Box-Cox linearity...] [2008-11-11 17:37:44] [6816386b1f3c2f6c0c9f2aa1e5bc9362]
F                 [Box-Cox Linearity Plot] [] [2008-11-11 19:54:20] [a7a7b7de998247cdf0f65ef79d563d66]
-                 [Box-Cox Linearity Plot] [] [2008-11-21 17:56:22] [888addc516c3b812dd7be4bd54caa358]
Feedback Forum
2008-11-18 20:07:14 [Glenn De Maeyer] [reply
Hier wordt in de R-code een nieuwe variabele gecreeerd x1. Deze X1 is eigenlijk de oorspronkelijke variabele x verheven tot de macht lambda -1 en dit opnieuw delen lambda. Met deze formule kan je tijdreeksen transformeren. Je moet op zoek gaan naar de optimale lambda om de tijdreeks te transformeren. De optimale lambda hier is 0.57.
2008-11-24 17:45:37 [Jan Cavents] [reply
inderdaad, als de juiste lambda gevonden is kan de tijdreeks aangepast worden.

Post a new message
Dataseries X:
3423.40
3242.80
3277.20
3833.00
2606.30
3643.80
3686.40
3281.60
3669.30
3191.50
3512.70
3970.70
3601.20
3610.00
4172.10
3956.20
3142.70
3884.30
3892.20
3613.00
3730.50
3481.30
3649.50
4215.20
4066.60
4196.80
4536.60
4441.60
3548.30
4735.90
4130.60
4356.20
4159.60
3988.00
4167.80
4902.20
3909.40
4697.60
4308.90
4420.40
3544.20
4433.00
4479.70
4533.20
4237.50
4207.40
4394.00
5148.40
4202.20
4682.50
4884.30
5288.90
4505.20
4611.50
5081.10
4523.10
4412.80
4647.40
4778.60
4495.30
Dataseries Y:
12300.00
12092.80
12380.80
12196.90
9455.00
13168.00
13427.90
11980.50
11884.80
11691.70
12233.80
14341.40
13130.70
12421.10
14285.80
12864.60
11160.20
14316.20
14388.70
14013.90
13419.00
12769.60
13315.50
15332.90
14243.00
13824.40
14962.90
13202.90
12199.00
15508.90
14199.80
15169.60
14058.00
13786.20
14147.90
16541.70
13587.50
15582.40
15802.80
14130.50
12923.20
15612.20
16033.70
16036.60
14037.80
15330.60
15038.30
17401.80
14992.50
16043.70
16929.60
15921.30
14417.20
15961.00
17851.90
16483.90
14215.50
17429.70
17839.50
17629.20




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time7 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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 & 7 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21655&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]7 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21655&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21655&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 time7 seconds
R Server'George Udny Yule' @ 72.249.76.132







Box-Cox Linearity Plot
# observations x60
maximum correlation0.903564928283787
optimal lambda(x)0.57
Residual SD (orginial)777.05148897948
Residual SD (transformed)774.385339777023

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 60 \tabularnewline
maximum correlation & 0.903564928283787 \tabularnewline
optimal lambda(x) & 0.57 \tabularnewline
Residual SD (orginial) & 777.05148897948 \tabularnewline
Residual SD (transformed) & 774.385339777023 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=21655&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]0.903564928283787[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]0.57[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]777.05148897948[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]774.385339777023[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=21655&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=21655&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 x60
maximum correlation0.903564928283787
optimal lambda(x)0.57
Residual SD (orginial)777.05148897948
Residual SD (transformed)774.385339777023



Parameters (Session):
par1 = TRUE ;
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')