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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 14:08:19 -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/t1226437757iddyp1kzx3mugns.htm/, Retrieved Sun, 19 May 2024 10:09:32 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=23978, Retrieved Sun, 19 May 2024 10:09:32 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywordsQ3 Box-Cox Linearity Plot
Estimated Impact116

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] [Q3 Various EDA To...] [2008-11-11 21:08:19] [8da7502cfecb272886bc60b3f290b8b8] [Current]
Feedback Forum
2008-11-12 18:22:59 [Romina Machiels] [reply] 
Q3: Er werd een berekening gemaakt maar er werd geen uitleg gegeven. De box-cox linearity plot laat zien dat de optimale lambda-waarde 2 is, dit kan je zien doordat bij de optimale lambda-waarde zich de hoogste correlatie bevindt. Het punt waar de grafiek het hoogst is. Er word enkel tussen -2 en 2 omdat de lambda-waarden zich meestal tussen die 2 waarden liggen.
Q4 en Q5 werden niet beantwoord.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1190,8
728,8
995,6
1260,3
994
957,3
975,6
884,9
908,4
1022,8
958,6
825,1
1116,6
724,2
1004,5
1058,9
854,7
943,4
792,4
873,2
1101,4
987,1
1038,8
1060,7
1047,7
840
1044
1097,4
987,5
934
977
881,1
1083,3
1074,7
1182,2
1117,5
1117,4
936,2
1246,3
1175,1
1177,7
1035,8
1091,6
998,7
1247,9
1034,7
1287,7
994,0
1122,8
1017,3
1106,0
1191,8
1030,1
989,4
979,6
1088,0
1389,2
1043,9
1182,1
1109,6
1463,3
1276,2
1082,4
1360,4
1130,2
1019,6
1077,0
958,8
959,6
907,2
880,8
759,6
1137,2
Dataseries Y:
Download CSV
Histogram 
0,9922
0,9778
0,9808
0,9811
1,0014
1,0183
1,0622
1,0773
1,0807
1,0848
1,1582
1,1663
1,1372
1,1139
1,1222
1,1692
1,1702
1,2286
1,2613
1,2646
1,2262
1,1985
1,2007
1,2138
1,2266
1,2176
1,2218
1,249
1,2991
1,3408
1,3119
1,3014
1,3201
1,2938
1,2694
1,2165
1,2037
1,2292
1,2256
1,2015
1,1786
1,1856
1,2103
1,1938
1,202
1,2271
1,277
1,265
1,2684
1,2811
1,2727
1,2611
1,2881
1,3213
1,2999
1,3074
1,3242
1,3516
1,3511
1,3419
1,3716
1,3622
1,3896
1,4227
1,4684
1,457
1,4718
1,4748
1,5527
1,575
1,5557
1,5553
1,577

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=23978&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=23978&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23978&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 x73
maximum correlation0.101949533103790
optimal lambda(x)2
Residual SD (orginial)0.141739642077281
Residual SD (transformed)0.141704903878934

\begin{tabular}{lllllllll}
\hline
Box-Cox Linearity Plot \tabularnewline
# observations x & 73 \tabularnewline
maximum correlation & 0.101949533103790 \tabularnewline
optimal lambda(x) & 2 \tabularnewline
Residual SD (orginial) & 0.141739642077281 \tabularnewline
Residual SD (transformed) & 0.141704903878934 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=23978&T=1

[TABLE]
[ROW][C]Box-Cox Linearity Plot[/C][/ROW]
[ROW][C]# observations x[/C][C]73[/C][/ROW]
[ROW][C]maximum correlation[/C][C]0.101949533103790[/C][/ROW]
[ROW][C]optimal lambda(x)[/C][C]2[/C][/ROW]
[ROW][C]Residual SD (orginial)[/C][C]0.141739642077281[/C][/ROW]
[ROW][C]Residual SD (transformed)[/C][C]0.141704903878934[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=23978&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=23978&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 x73
maximum correlation0.101949533103790
optimal lambda(x)2
Residual SD (orginial)0.141739642077281
Residual SD (transformed)0.141704903878934


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')