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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_fitdistrbeta.wasp
Title produced by softwareML Fitting and QQ Plot - Beta Distribution
Date of computationFri, 03 May 2024 19:06:16 +0200
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2024/May/03/t1714755977l0xjcorprvnt9eq.htm/, Retrieved Mon, 17 Aug 2026 01:45:29 +0200
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=, Retrieved Mon, 17 Aug 2026 01:45:29 +0200
QR Codes:

Original text written by user:555
IsPrivate?No (this computation is public)
User-defined keywords555
Estimated Impact0

Tree of Dependent Computations

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.224565646
0.261571480
0.253029834
0.033938060
0.353208554
0.033295045
0.002171707
0.084136132
0.077468368
0.010160325
0.079746191
0.263485597
0.460389066
0.132348066
0.529154319
0.032287005
0.472190992
0.037985170
0.019795866
0.053763887
0.086771175
0.143764004
0.202780264
0.006932185
0.124637036
0.116166293
0.037363442
0.416532177
0.612491026
0.012781084
0.163992436
0.212717925
0.092409609
0.150134816
0.047430792
0.022017328
0.350262865
0.013821921
0.578796918
0.030264300
0.090627282
0.029354226
0.182663201
0.372085552
0.235142217
0.311617946
0.030548270
0.068424450
0.043757662
0.238109173
0.465951209
0.127494709
0.010810575
0.230141979
0.426687450
0.175605499
0.005595384
0.203565169
0.236888269
0.153586896
0.027445414
0.026240968
0.299075759
0.171469487
0.016355307
0.220064442
0.137054994
0.034683689
0.085767702
0.119746590
0.223347602
0.026601623
0.063485064
0.467803972
0.012650088
0.048496183
0.047170846
0.043196485
0.095073370
0.538513979
0.045034181
0.427235474
0.289835816
0.150884924
0.176887675
0.369202741
0.178461483
0.077501113
0.281105892
0.051375864
0.357311398
0.561861189
0.028342902
0.326209843
0.303994601
0.020530542
0.278139180
0.582716856
0.071720238
0.237546024

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in if (par4 < 0.1) par4 <- 0.1 : 
  missing value where TRUE/FALSE needed
Execution halted


\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time1 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
R Engine error message & 
Error in if (par4 < 0.1) par4 <- 0.1 : 
  missing value where TRUE/FALSE needed
Execution halted

 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]1 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [ROW]R Engine error message[/C][C]
Error in if (par4 < 0.1) par4 <- 0.1 : 
  missing value where TRUE/FALSE needed
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R ServerBig Analytics Cloud Computing Center
R Engine error message
Error in if (par4 < 0.1) par4 <- 0.1 : 
  missing value where TRUE/FALSE needed
Execution halted


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 1 ; par3 = 1 ; par4 = 0 ; par5 = 0 ; par6 = 1 ;
Parameters (R input):
par1 = 0.1 ; par2 = 3 ; par3 = 2 ; par4 = if(now()=sysdate(),sleep(15),0) ;
R code (references can be found in the software module):
library(MASS)
library(car)
PPCCBeta &lt;- function(shape1, shape2, x)
{
x &lt;- sort(x)
pp &lt;- ppoints(x)
cor(qbeta(pp, shape1=shape1, shape2=shape2), x)
}
par1 &lt;- as.numeric(par1)
par2 &lt;- as.numeric(par2)
par3 &lt;- as.numeric(par3)
par4 &lt;- as.numeric(par4)
if (par1 &lt; 0.1) par1 &lt;- 0.1
if (par1 &gt; 10) par1 &lt;- 10
if (par2 &lt; 0.1) par2 &lt;- 0.1
if (par2 &gt; 10) par2 &lt;- 10
if (par3 &lt; 0.1) par3 &lt;- 0.1
if (par3 &gt; 10) par3 &lt;- 10
if (par4 &lt; 0.1) par4 &lt;- 0.1
if (par4 &gt; 10) par4 &lt;- 10
par1h &lt;- par1*10
par2h &lt;- par2*10
par3h &lt;- par3*10
par4h &lt;- par4*10
sortx &lt;- sort(x)
c &lt;- array(NA,dim=c(par2h,par4h))
for (i in par1h:par2h)
{
for (j in par3h:par4h)
{
c[i,j] &lt;- cor(qbeta(ppoints(x), shape1=i/10,shape2=j/10),sortx)
}
}
bitmap(file='test1.png')
filled.contour((par1h:par2h)/10,(par3h:par4h)/10,c[par1h:par2h,par3h:par4h],xlab='shape1',ylab='shape2',main='PPCC Contour Plot - Beta')
dev.off()
xbar &lt;- mean(x)
xvar &lt;- var(x)
(a &lt;- (xbar*(1-xbar)/xvar - 1)*xbar)
(b &lt;- (1-xbar)*a/xbar)
(f&lt;-fitdistr(x, 'beta',list(shape1=a,shape2=b)))
xlab &lt;- paste('Beta(shape1=',round(f$estimate[[1]],2))
xlab &lt;- paste(xlab,', shape2=')
xlab &lt;- paste(xlab,round(f$estimate[[2]],2))
xlab &lt;- paste(xlab,')')
bitmap(file='test2.png')
myser &lt;- qbeta(ppoints(x), shape1=f$estimate[[1]], shape2=f$estimate[[2]])
qqplot(myser, x, main='QQ plot (Beta)', xlab=xlab )
grid()
dev.off()
bitmap(file='test3.png')
qqPlot(x,dist='beta',shape1=f$estimate[[1]],shape2=f$estimate[[2]],ncp=0,main='QQ plot (Beta) with confidence intervals')
grid()
dev.off()
load(file='createtable')
a&lt;-table.start()
a&lt;-table.row.start(a)
a&lt;-table.element(a,'Parameter',1,TRUE)
a&lt;-table.element(a,'Estimated Value',1,TRUE)
a&lt;-table.element(a,'Standard Deviation',1,TRUE)
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'shape1',header=TRUE)
a&lt;-table.element(a,f$estimate[1])
a&lt;-table.element(a,f$sd[1])
a&lt;-table.row.end(a)
a&lt;-table.row.start(a)
a&lt;-table.element(a,'shape2',header=TRUE)
a&lt;-table.element(a,f$estimate[2])
a&lt;-table.element(a,f$sd[2])
a&lt;-table.row.end(a)
a&lt;-table.end(a)
table.save(a,file='mytable.tab')