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Description of Statistical Computation

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 computationTue, 19 Oct 2010 15:17:54 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Oct/19/t1287501389e74ypl5kxlhfg9w.htm/, Retrieved Mon, 29 Apr 2024 01:45:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=86632, Retrieved Mon, 29 Apr 2024 01:45:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact103

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Tukey lambda PPCC Plot] [Intrinsic Motivat...] [2010-10-12 12:09:04] [b98453cac15ba1066b407e146608df68]
F    D    [Tukey lambda PPCC Plot] [] [2010-10-19 15:17:54] [9003764b6a75599accb6eea9154ba195] [Current]
Feedback Forum
2010-10-25 17:01:44 [6f5a430a34dfbeab884e51a2f2a26434] [reply] 
Je hebt niet alle nodige grafieken gepost. Het was de bedoeling dat je een histogram, een QQ-plot en een Tukey Lambda opstelde voor de extrinsieke en intrinsieke motivaties. Hierbij moest je per grafiek of op z’n minst per drie grafieken per motivatie een conclusie trok.

Let bij de grafieken op de volgende punten, zo weet je of de normaalverdeling van toepassing zal zijn op de gegevensverzameling of niet.

De normaalverdeling kan men vaststellen aan de volle lijn die gelijk loopt met het histogram, als deze volle lijn de vorm aanneemt van een normaalverdeling, wijst dit er ook op. Soms kan de histogram iets boven de volle lijn uitkomen, maar dit kan veranderen als je het aantal klassen aanpast.

De QQ- plot laat zien waar er zich outliers bevinden, als het merendeel zich op de rechte bevindt van de QQ- plot, dan duidt ook dit op een normaalverdeling.
De Tukey lambda laat zien via de lambda- waarde welke verdeling van toepassing is, de hoogste correlatiewaarde duidt op de beste verdeling.

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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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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'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 & 1 seconds \tabularnewline
R Server & 'George Udny Yule' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86632&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]'George Udny Yule' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86632&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86632&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'George Udny Yule' @ 72.249.76.132






Tukey Lambda - Key Values
Distribution (lambda)Correlation
Approx. Cauchy (lambda=-1)0.558794830555915
Exact Logistic (lambda=0)0.97026728171214
Approx. Normal (lambda=0.14)0.971831002278158
U-shaped (lambda=0.5)0.959811559184502
Exactly Uniform (lambda=1)0.944782426561482

\begin{tabular}{lllllllll}
\hline
Tukey Lambda - Key Values \tabularnewline
Distribution (lambda) & Correlation \tabularnewline
Approx. Cauchy (lambda=-1) & 0.558794830555915 \tabularnewline
Exact Logistic (lambda=0) & 0.97026728171214 \tabularnewline
Approx. Normal (lambda=0.14) & 0.971831002278158 \tabularnewline
U-shaped (lambda=0.5) & 0.959811559184502 \tabularnewline
Exactly Uniform (lambda=1) & 0.944782426561482 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=86632&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]0.558794830555915[/C][/ROW]
[ROW][C]Exact Logistic (lambda=0)[/C][C]0.97026728171214[/C][/ROW]
[ROW][C]Approx. Normal (lambda=0.14)[/C][C]0.971831002278158[/C][/ROW]
[ROW][C]U-shaped (lambda=0.5)[/C][C]0.959811559184502[/C][/ROW]
[ROW][C]Exactly Uniform (lambda=1)[/C][C]0.944782426561482[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=86632&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=86632&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:

Tukey Lambda - Key Values
Distribution (lambda)Correlation
Approx. Cauchy (lambda=-1)0.558794830555915
Exact Logistic (lambda=0)0.97026728171214
Approx. Normal (lambda=0.14)0.971831002278158
U-shaped (lambda=0.5)0.959811559184502
Exactly Uniform (lambda=1)0.944782426561482


Figures (Output of Computation)

Input Parameters & R Code

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')