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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationTue, 02 Nov 2010 21:01:13 +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/Nov/02/t1288731635bvpndf4ddgp9bax.htm/, Retrieved Sat, 27 Apr 2024 17:57:49 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=92075, Retrieved Sat, 27 Apr 2024 17:57:49 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact163

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Factor Analysis] [Sleep in Mammals ...] [2010-03-21 11:39:53] [b98453cac15ba1066b407e146608df68]
- RMPD  [Testing Mean with unknown Variance - Critical Value] [Hypothesis Test a...] [2010-10-19 11:45:26] [b98453cac15ba1066b407e146608df68]
F RMPD    [Paired and Unpaired Two Samples Tests about the Mean] [Q1, W5] [2010-11-02 19:21:54] [b3140021f9a1a3896de9ecbfce0f1101]
-    D      [Paired and Unpaired Two Samples Tests about the Mean] [Q2, W5] [2010-11-02 19:28:48] [b3140021f9a1a3896de9ecbfce0f1101]
F    D        [Paired and Unpaired Two Samples Tests about the Mean] [Q3, W5] [2010-11-02 19:32:03] [b3140021f9a1a3896de9ecbfce0f1101]
F    D          [Paired and Unpaired Two Samples Tests about the Mean] [Q5 a, W5] [2010-11-02 20:03:56] [b3140021f9a1a3896de9ecbfce0f1101]
F    D            [Paired and Unpaired Two Samples Tests about the Mean] [Q5 b,W5] [2010-11-02 20:06:39] [b3140021f9a1a3896de9ecbfce0f1101]
F RM D              [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Q6, KT] [2010-11-02 20:27:53] [b3140021f9a1a3896de9ecbfce0f1101]
F RM D                  [Two-Way ANOVA] [Q8] [2010-11-02 21:01:13] [0605ea080d54454c99180f574351b8e4] [Current]
Feedback Forum
2010-11-03 16:33:23 [Pascal Wijnen] [reply] 
De student heeft de verkeerde data gebruikt en komt zo tot een verkeerde blog. een mogelijke oplossing is de volgende: http://www.freestatistics.org/blog/date/2010/Oct/29/t1288344744d4iccuz620r4ne3.htm/
Uit de gegevens van de link kunnen we concluderen dat de vrouwen het bij elke treatment, behalve treatment H, betere resultaten boeken dan de mannen. Bij treatment H halen de mannen betere resultaten, er was dan ook ‘interactie’ tussen mannen en vrouwen bij de overgang van F naar H.
Verder kunnen we stellen dat treatment F de beste resultaten opgeeft in vergelijking met de andere treatments.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	1	'E'
1	0	'F'
0	1	'F'
0	1	'H'
0	1	'H'
0	1	'H'
1	1	'E'
1	1	'F'
0	1	'E'
1	0	'F'
0	0	'H'
0	0	'E'
1	1	'F'
0	0	'H'
1	0	'E'
0	0	'H'
0	1	'E'
0	1	'F'
0	0	'H'
1	0	'F'
0	0	'H'
0	1	'H'
0	0	'H'
0	0	'E'
1	0	'F'
1	0	'E'
1	0	'E'
1	1	'F'
0	0	'F'
0	0	'H'
0	1	'E'
1	1	'E'
0	1	'H'
1	1	'E'
1	1	'F'
0	1	'E'
1	0	'F'
0	0	'H'
1	0	'E'
1	0	'F'
1	0	'F'
0	0	'F'
1	0	'F'
1	1	'H'
1	0	'E'
0	0	'E'
0	0	'H'
1	1	'E'
0	1	'F'
0	0	'F'
0	0	'H'
0	1	'E'
1	1	'F'
1	1	'E'
0	1	'H'
0	1	'H'
0	1	'H'
0	1	'E'
0	0	'H'
1	0	'E'
0	1	'H'
0	1	'F'
0	1	'H'
1	0	'F'
0	1	'E'
1	1	'E'
0	0	'F'
0	1	'H'
0	0	'F'
0	1	'E'
0	1	'E'
0	0	'H'
0	1	'H'
0	1	'F'
0	1	'H'
1	0	'E'
0	1	'F'
1	0	'E'
0	0	'E'
0	0	'E'
0	1	'F'
0	1	'E'
1	1	'F'
0	1	'H'
1	1	'H'
0	1	'H'
0	0	'F'
0	1	'H'
0	1	'H'
1	1	'F'
1	1	'F'
0	0	'H'
0	1	'F'
0	1	'H'
0	0	'E'
1	1	'F'
0	0	'E'
0	1	'H'
1	1	'F'
1	1	'F'
0	1	'H'
1	1	'E'
0	0	'F'
0	1	'H'
0	1	'E'
0	0	'F'
0	0	'H'
0	1	'H'
1	1	'F'
1	1	'F'
0	1	'H'
0	0	'E'
0	1	'H'
0	1	'E'
0	0	'E'
0	1	'F'
0	1	'F'

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'Sir Ronald Aylmer Fisher' @ 193.190.124.24

\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 & 'Sir Ronald Aylmer Fisher' @ 193.190.124.24 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92075&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]'Sir Ronald Aylmer Fisher' @ 193.190.124.24[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92075&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92075&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'Sir Ronald Aylmer Fisher' @ 193.190.124.24






ANOVA Model
Response ~ Treatment_A * Treatment_B - 1
means0.4710.350.059-0.4710.1560.198

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B - 1 \tabularnewline
means & 0.471 & 0.35 & 0.059 & -0.471 & 0.156 & 0.198 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92075&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B - 1[/C][/ROW]
[ROW][C]means[/C][C]0.471[/C][C]0.35[/C][C]0.059[/C][C]-0.471[/C][C]0.156[/C][C]0.198[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92075&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92075&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:

ANOVA Model
Response ~ Treatment_A * Treatment_B - 1
means0.4710.350.059-0.4710.1560.198






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A213.0356.51835.2580
Treatment_B25.2462.62314.1890
Treatment_A:Treatment_B20.20.10.5410.584
Residuals11120.5190.185 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 13.035 & 6.518 & 35.258 & 0 \tabularnewline
Treatment_B & 2 & 5.246 & 2.623 & 14.189 & 0 \tabularnewline
Treatment_A:Treatment_B & 2 & 0.2 & 0.1 & 0.541 & 0.584 \tabularnewline
Residuals & 111 & 20.519 & 0.185 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92075&T=2

[TABLE]
[ROW][C]ANOVA Statistics[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]Sum Sq[/C][C]Mean Sq[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C][/C][C]2[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]2[/C][C]13.035[/C][C]6.518[/C][C]35.258[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]5.246[/C][C]2.623[/C][C]14.189[/C][C]0[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]0.2[/C][C]0.1[/C][C]0.541[/C][C]0.584[/C][/ROW]
[ROW][C]Residuals[/C][C]111[/C][C]20.519[/C][C]0.185[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92075&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92075&T=2

As an alternative you can also use a QR Code:  
QR Code


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

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A213.0356.51835.2580
Treatment_B25.2462.62314.1890
Treatment_A:Treatment_B20.20.10.5410.584
Residuals11120.5190.185






Must Include Intercept to use Tukey Test 

\begin{tabular}{lllllllll}
\hline
Must Include Intercept to use Tukey Test  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92075&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92075&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92075&T=3

As an alternative you can also use a QR Code:  
QR Code


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

Must Include Intercept to use Tukey Test






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group54.340.001
111 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 4.34 & 0.001 \tabularnewline
  & 111 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=92075&T=4

[TABLE]
[ROW][C]Levenes Test for Homogeneity of Variance[/C][/ROW]
[ROW][C] [/C][C]Df[/C][C]F value[/C][C]Pr(>F)[/C][/ROW]
[ROW][C]Group[/C][C]5[/C][C]4.34[/C][C]0.001[/C][/ROW]
[ROW][C] [/C][C]111[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=92075&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=92075&T=4

As an alternative you can also use a QR Code:  
QR Code


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

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group54.340.001
111


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = unpaired ; par6 = 0.0 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = FALSE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
cat3 <- as.numeric(par3)
intercept<-as.logical(par4)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
f2 <- as.character(x[,cat3])
xdf<-data.frame(x1,f1, f2)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
(V3 <-dimnames(y)[[1]][cat3])
names(xdf)<-c('Response', 'Treatment_A', 'Treatment_B')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment_A * Treatment_B- 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment_A * Treatment_B, data = xdf) )
(aov.xdf<-aov(lmxdf) )
(anova.xdf<-anova(lmxdf) )
load(file='createtable')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Model', length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, lmxdf$call['formula'],length(lmxdf$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'means',,TRUE)
for(i in 1:length(lmxdf$coefficients)){
a<-table.element(a, round(lmxdf$coefficients[i], digits=3),,FALSE)
}
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable.tab')
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'ANOVA Statistics', 5+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ',,TRUE)
a<-table.element(a, 'Df',,FALSE)
a<-table.element(a, 'Sum Sq',,FALSE)
a<-table.element(a, 'Mean Sq',,FALSE)
a<-table.element(a, 'F value',,FALSE)
a<-table.element(a, 'Pr(>F)',,FALSE)
a<-table.row.end(a)
for(i in 1 : length(rownames(anova.xdf))-1){
a<-table.row.start(a)
a<-table.element(a,rownames(anova.xdf)[i] ,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[i], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[i], digits=3),,FALSE)
a<-table.row.end(a)
}
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$'Df'[i+1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[i+1], digits=3),,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.element(a, ' ',,FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable1.tab')
bitmap(file='anovaplot.png')
boxplot(Response ~ Treatment_A + Treatment_B, data=xdf, xlab=V2, ylab=V1, main='Boxplots of ANOVA Groups')
dev.off()
bitmap(file='designplot.png')
xdf2 <- xdf # to preserve xdf make copy for function
names(xdf2) <- c(V1, V2, V3)
plot.design(xdf2, main='Design Plot of Group Means')
dev.off()
bitmap(file='interactionplot.png')
interaction.plot(xdf$Treatment_A, xdf$Treatment_B, xdf$Response, xlab=V2, ylab=V1, trace.label=V3, main='Possible Interactions Between Anova Groups')
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
names(thsd) <- c(V2, V3, paste(V2, ':', V3, sep=''))
bitmap(file='TukeyHSDPlot.png')
layout(matrix(c(1,2,3,3), 2,2))
plot(thsd, las=1)
dev.off()
}
if(intercept==TRUE){
ntables<-length(names(thsd))
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Tukey Honest Significant Difference Comparisons', 5,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, ' ', 1, TRUE)
for(i in 1:4){
a<-table.element(a,colnames(thsd[[1]])[i], 1, TRUE)
}
a<-table.row.end(a)
for(nt in 1:ntables){
for(i in 1:length(rownames(thsd[[nt]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[nt]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[nt]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
} # end nt
a<-table.end(a)
table.save(a,file='hsdtable.tab')
}#end if hsd tables
if(intercept==FALSE){
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'TukeyHSD Message', 1,TRUE)
a<-table.row.end(a)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Must Include Intercept to use Tukey Test ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
library(car)
lt.lmxdf<-levene.test(lmxdf)
a<-table.start()
a<-table.row.start(a)
a<-table.element(a,'Levenes Test for Homogeneity of Variance', 4,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
for (i in 1:3){
a<-table.element(a,names(lt.lmxdf)[i], 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,'Group', 1, TRUE)
for (i in 1:3){
a<-table.element(a,round(lt.lmxdf[[i]][1], digits=3), 1, FALSE)
}
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a,' ', 1, TRUE)
a<-table.element(a,lt.lmxdf[[1]][2], 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.element(a,' ', 1, FALSE)
a<-table.row.end(a)
a<-table.end(a)
table.save(a,file='mytable3.tab')