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Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_tukeylambda.wasp
Title produced by softwareTukey lambda PPCC Plot
Date of computationMon, 27 Oct 2008 09:56:12 -0600
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/Oct/27/t12251230498wdz20cgvjswrcs.htm/, Retrieved Sun, 19 May 2024 14:41:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=19248, Retrieved Sun, 19 May 2024 14:41:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact121
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Tukey lambda PPCC Plot] [Investigating Dis...] [2007-10-21 16:01:20] [b9964c45117f7aac638ab9056d451faa]
F    D    [Tukey lambda PPCC Plot] [Tukey Lambda] [2008-10-27 15:56:12] [7ed4ec9f8cdf7df79ef87b9dc09dff20] [Current]
Feedback Forum
2008-11-01 12:08:37 [2df1bcd103d52957f4a39bd4617794c8] [reply
Student gebruikt juiste Tukey Lamba methode om conclusie te trekken. De hoogste correlatie vinden we inderdaad terug bij lamba0.14. Dit wijst op een normaalverdeling.

Onafhankelijke metingen, die allen evenveel kans hebben om met de steekproef mee te doen hebben steeds een normaalverdeling. We kunnen bijgevolg niet spreken van autocorrelatie.
2008-11-03 22:59:43 [Martjin De Swert] [reply
Juiste methode is gebruikt om de resultaten te verifiëren. Er wordt ook terecht besloten dat het om een normaalverdeling gaat maar de student had wel mogen/kunnen vermelden waarom deze zo belangrijk is.

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Dataseries X:
110.40
96.40
101.90
106.20
81.00
94.70
101.00
109.40
102.30
90.70
96.20
96.10
106.00
103.10
102.00
104.70
86.00
92.10
106.90
112.60
101.70
92.00
97.40
97.00
105.40
102.70
98.10
104.50
87.40
89.90
109.80
111.70
98.60
96.90
95.10
97.00
112.70
102.90
97.40
111.40
87.40
96.80
114.10
110.30
103.90
101.60
94.60
95.90
104.70
102.80
98.10
113.90
80.90
95.70
113.20
105.90
108.80
102.30
99.00
100.70
115.50




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=19248&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=19248&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19248&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







Tukey Lambda - Key Values
Distribution (lambda)Correlation
Approx. Cauchy (lambda=-1)0.681034394717584
Exact Logistic (lambda=0)0.984820721672163
Approx. Normal (lambda=0.14)0.989505916159088
U-shaped (lambda=0.5)0.985385537255734
Exactly Uniform (lambda=1)0.97511352322751

\begin{tabular}{lllllllll}
\hline
Tukey Lambda - Key Values \tabularnewline
Distribution (lambda) & Correlation \tabularnewline
Approx. Cauchy (lambda=-1) & 0.681034394717584 \tabularnewline
Exact Logistic (lambda=0) & 0.984820721672163 \tabularnewline
Approx. Normal (lambda=0.14) & 0.989505916159088 \tabularnewline
U-shaped (lambda=0.5) & 0.985385537255734 \tabularnewline
Exactly Uniform (lambda=1) & 0.97511352322751 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=19248&T=1

[TABLE]
[ROW][C]Tukey Lambda - Key Values[/C][/ROW]
[ROW][C]Distribution (lambda)[/C][C]Correlation[/C][/ROW]
[ROW][C]Approx. Cauchy (lambda=-1)[/C][C]0.681034394717584[/C][/ROW]
[ROW][C]Exact Logistic (lambda=0)[/C][C]0.984820721672163[/C][/ROW]
[ROW][C]Approx. Normal (lambda=0.14)[/C][C]0.989505916159088[/C][/ROW]
[ROW][C]U-shaped (lambda=0.5)[/C][C]0.985385537255734[/C][/ROW]
[ROW][C]Exactly Uniform (lambda=1)[/C][C]0.97511352322751[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=19248&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=19248&T=1

As an alternative you can also use a QR Code:  

The GUIDs for individual cells are displayed in the table below:

Tukey Lambda - Key Values
Distribution (lambda)Correlation
Approx. Cauchy (lambda=-1)0.681034394717584
Exact Logistic (lambda=0)0.984820721672163
Approx. Normal (lambda=0.14)0.989505916159088
U-shaped (lambda=0.5)0.985385537255734
Exactly Uniform (lambda=1)0.97511352322751



Parameters (Session):
Parameters (R input):
R code (references can be found in the software module):
gp <- function(lambda, p)
{
(p^lambda-(1-p)^lambda)/lambda
}
sortx <- sort(x)
c <- array(NA,dim=c(201))
for (i in 1:201)
{
if (i != 101) c[i] <- cor(gp(ppoints(x), lambda=(i-101)/100),sortx)
}
bitmap(file='test1.png')
plot((-100:100)/100,c[1:201],xlab='lambda',ylab='correlation',main='PPCC Plot - Tukey lambda')
grid()
dev.off()
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tukey Lambda - Key Values',2,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Distribution (lambda)',1,TRUE)
a<-table.element(a,'Correlation',1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Approx. Cauchy (lambda=-1)',header=TRUE)
a<-table.element(a,c[1])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Exact Logistic (lambda=0)',header=TRUE)
a<-table.element(a,(c[100]+c[102])/2)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Approx. Normal (lambda=0.14)',header=TRUE)
a<-table.element(a,c[115])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'U-shaped (lambda=0.5)',header=TRUE)
a<-table.element(a,c[151])
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Exactly Uniform (lambda=1)',header=TRUE)
a<-table.element(a,c[201])
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')