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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 computationThu, 13 Nov 2008 09:07:05 -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/13/t1226592486me1d3cshne1ytu5.htm/, Retrieved Sun, 19 May 2024 09:36:35 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=24664, Retrieved Sun, 19 May 2024 09:36:35 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact131

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Box-Cox Linearity Plot] [marlies.polfliet_...] [2008-11-13 16:07:05] [e221948dd14811c7d88a6530ac2a8702] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
100	101.7	101.5	125.9	102.4	101.4	101.3	112	84.8	111	100.4	111.1	101.4	100.1	91.6	96.6	105.3	102.1	84.7	96	72.4	92.8
102.5	105.2	105.9	127.4	100.3	104.9	105.6	117.8	85.6	113.1	103.9	117.1	104.9	98.2	83.8	103.7	108.8	110.7	88.8	107.8	68.1	91.7
101.4	104.8	103.8	135.3	110.9	104.4	103.5	119.5	81	111.3	102.8	112.2	103.4	90.2	69.6	105.5	107.4	115.5	87.4	113.5	64.8	88.1
106	110	108.8	141.6	118	109.6	108.4	124.2	86.1	119.7	106.1	115.3	110.3	96.1	72.1	113	116.9	128.9	92.9	116.7	64.7	90.7
108.8	112.9	111.9	144	119.1	112.5	111.6	130.2	86.2	124.9	108.9	114.1	112.7	102.7	72.7	120.4	130.4	147	93.8	118.9	65	93.1
Dataseries Y:
Download CSV
Histogram 
100	101.7	101.5	125.9	102.4	101.4	101.3	112	84.8	111	100.4	111.1	101.4	100.1	91.6	96.6	105.3	102.1	84.7	96	72.4	92.8
102.5	105.2	105.9	127.4	100.3	104.9	105.6	117.8	85.6	113.1	103.9	117.1	104.9	98.2	83.8	103.7	108.8	110.7	88.8	107.8	68.1	91.7
101.4	104.8	103.8	135.3	110.9	104.4	103.5	119.5	81	111.3	102.8	112.2	103.4	90.2	69.6	105.5	107.4	115.5	87.4	113.5	64.8	88.1
106	110	108.8	141.6	118	109.6	108.4	124.2	86.1	119.7	106.1	115.3	110.3	96.1	72.1	113	116.9	128.9	92.9	116.7	64.7	90.7
108.8	112.9	111.9	144	119.1	112.5	111.6	130.2	86.2	124.9	108.9	114.1	112.7	102.7	72.7	120.4	130.4	147	93.8	118.9	65	93.1

Tables (Output of Computation)





Summary of computational 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 computational 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=24664&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]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=24664&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=24664&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 time1 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135


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')