FreeStatistics.org

Free Statistics

of Irreproducible Research!

Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software ModuleIan.Hollidayrwasp_Two Factor ANOVA.wasp
Title produced by softwareAnalysis of Variance Free Statistics Software (Calculator)
Date of computationSat, 12 Dec 2009 16:32:32 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2009/Dec/12/t1260631983wgpdf6ws2nkb9ko.htm/, Retrieved Mon, 29 Apr 2024 16:09:59 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=67020, Retrieved Mon, 29 Apr 2024 16:09:59 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact111

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Analysis of Variance Free Statistics Software (Calculator)] [] [2009-12-12 15:32:32] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
Feedback Forum

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.18	'Control'	'Male'
-0.45	'Control'	'Male'
1.19	'Control'	'Male'
0.63	'Control'	'Female'
0.80	'Control'	'Female'
-1.09	'Control'	'Female'
-1.24	'Control'	'Female'
1.17	'Control'	'Female'
-0.11	'TreatA'	'Male'
1.68	'TreatA'	'Male'
2.44	'TreatA'	'Male'
0.30	'TreatA'	'Male'
2.59	'TreatA'	'Female'
2.01	'TreatA'	'Female'
1.01	'TreatA'	'Female'
1.95	'TreatA'	'Female'
-0.23	'TreatB'	'Male'
1.87	'TreatB'	'Male'
-0.23	'TreatB'	'Male'
-0.42	'TreatB'	'Male'
3.09	'TreatB'	'Female'
0.79	'TreatB'	'Female'
-0.82	'TreatB'	'Female'
3.23	'TreatB'	'Female'

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67020&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67020&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67020&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'RServer@AstonUniversity' @ vre.aston.ac.uk






ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.0541.8361.5180.253-1.065-1.578

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.054 & 1.836 & 1.518 & 0.253 & -1.065 & -1.578 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67020&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.054[/C][C]1.836[/C][C]1.518[/C][C]0.253[/C][C]-1.065[/C][C]-1.578[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67020&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67020&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
means0.0541.8361.5180.253-1.065-1.578






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A27.1763.5882.3990.119
Treatment_B22.4592.4591.6440.216
Treatment_A:Treatment_B22.4921.2460.8330.451
Residuals226.9231.496 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 7.176 & 3.588 & 2.399 & 0.119 \tabularnewline
Treatment_B & 2 & 2.459 & 2.459 & 1.644 & 0.216 \tabularnewline
Treatment_A:Treatment_B & 2 & 2.492 & 1.246 & 0.833 & 0.451 \tabularnewline
Residuals & 2 & 26.923 & 1.496 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67020&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]7.176[/C][C]3.588[/C][C]2.399[/C][C]0.119[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]2.459[/C][C]2.459[/C][C]1.644[/C][C]0.216[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]2.492[/C][C]1.246[/C][C]0.833[/C][C]0.451[/C][/ROW]
[ROW][C]Residuals[/C][C]2[/C][C]26.923[/C][C]1.496[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67020&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67020&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_A27.1763.5882.3990.119
Treatment_B22.4592.4591.6440.216
Treatment_A:Treatment_B22.4921.2460.8330.451
Residuals226.9231.496






Tukey Honest Significant Difference Comparisons
difflwruprp adj
TreatA-Control1.335-0.2262.8960.101
TreatB-Control0.761-0.7992.3220.443
TreatB-TreatA-0.574-2.1340.9870.624

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
TreatA-Control & 1.335 & -0.226 & 2.896 & 0.101 \tabularnewline
TreatB-Control & 0.761 & -0.799 & 2.322 & 0.443 \tabularnewline
TreatB-TreatA & -0.574 & -2.134 & 0.987 & 0.624 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67020&T=3

[TABLE]
[ROW][C]Tukey Honest Significant Difference Comparisons[/C][/ROW]
[ROW][C] [/C][C]diff[/C][C]lwr[/C][C]upr[/C][C]p adj[/C][/ROW]
[ROW][C]TreatA-Control[/C][C]1.335[/C][C]-0.226[/C][C]2.896[/C][C]0.101[/C][/ROW]
[ROW][C]TreatB-Control[/C][C]0.761[/C][C]-0.799[/C][C]2.322[/C][C]0.443[/C][/ROW]
[ROW][C]TreatB-TreatA[/C][C]-0.574[/C][C]-2.134[/C][C]0.987[/C][C]0.624[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67020&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67020&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:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
TreatA-Control1.335-0.2262.8960.101
TreatB-Control0.761-0.7992.3220.443
TreatB-TreatA-0.574-2.1340.9870.624






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group51.3470.29
18 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 1.347 & 0.29 \tabularnewline
  & 18 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=67020&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]1.347[/C][C]0.29[/C][/ROW]
[ROW][C] [/C][C]18[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=67020&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=67020&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)
Group51.3470.29
18


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
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$Df[i],,FALSE)
a<-table.element(a, round(anova.xdf$'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)
dev.off()
if(intercept==TRUE){
bitmap(file='TukeyHSDPlot.png')
thsd<-TukeyHSD(aov.xdf)


op <- par(mfrow=c(3,1))
plot(thsd, las=1)
par(op)

dev.off()
}
if(intercept==TRUE){
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(i in 1:length(rownames(thsd[[1]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
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')