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Author*The author of this computation has been verified*
R Software Modulerwasp_Two Factor ANOVA.wasp
Title produced by softwareTwo-Way ANOVA
Date of computationWed, 06 Nov 2013 20:34:49 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2013/Nov/06/t1383788131rxl6egst391onu8.htm/, Retrieved Fri, 03 May 2024 01:46:06 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=223195, Retrieved Fri, 03 May 2024 01:46:06 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact61
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [WS5 - Q7 (correct)] [2013-11-07 01:34:49] [022e78770bd486b2574ab5ebcc241092] [Current]
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Dataseries X:
'WWE' 0 0 0 0 0 0
'WWE' 0 0 0 0 0 0
'WWE' 0 1 1 1 1 2
'WWE' 0 0 0 0 0 0
'WWE' 0 1 1 1 1 2
'WWE' 0 0 1 0 1 1
'WWE' 0 0 0 0 0 0
'WWE' 0 1 1 1 1 2
'WWE' 0 0 0 0 0 0
'WWE' 0 0 0 0 0 0
'WWE' 0 0 0 0 0 0
'WWE' 0 0 0 0 0 0
'WWE' 0 0 0 0 0 0
'WWE' 0 0 NA 0 NA NA
'WWE' 0 0 1 0 1 1
'WWE' 1 1 NA 0 NA NA
'WWE' 1 0 0 -1 -1 -1
'WWE' 0 0 0 0 0 0
'WWE' 0 0 1 0 1 1
'WWE' 0 1 0 1 0 1
'WWE' 0 0 0 0 0 0
'WWE' 1 1 0 0 -1 0
'WWE' 0 0 0 0 0 0
'WWE' 0 1 0 1 0 1
'WWE' 0 1 1 1 1 2
'WWE' 0 1 1 1 1 2
'WWE' 0 0 0 0 0 0
'WWE' 1 1 0 0 -1 0
'WWE' 0 1 0 1 0 1
'WWE' 0 1 0 1 0 1
'WWE' 0 0 1 0 1 1
'WWE' 0 1 0 1 0 1
'WWE' 0 1 0 1 0 1
'WWE' 0 0 0 0 0 0
'WWE' 0 0 0 0 0 0
'WWE' 1 1 0 0 -1 0
'WWE' 1 1 0 0 -1 0
'WWE' 0 0 0 0 0 0
'WWE' 0 0 NA 0 NA NA
'WWE' 0 0 1 0 1 1
'WWE' 0 0 0 0 0 0
'CSWE' 0 0 0 0 0 0
'CSWE' 1 0 NA -1 NA NA
'CSWE' 0 0 0 0 0 0
'CSWE' 0 1 NA 1 NA NA
'CSWE' 0 1 0 1 0 1
'CSWE' 0 0 0 0 0 0
'CSWE' 0 0 0 0 0 0
'CSWE' 0 0 1 0 1 1
'CSWE' 0 0 0 0 0 0
'CSWE' 1 1 0 0 -1 0
'CSWE' 0 0 1 0 1 1
'CSWE' 0 1 0 1 0 1
'CSWE' 0 0 0 0 0 0
'CSWE' 0 1 NA 1 NA NA
'CSWE' 0 1 0 1 0 1
'CSWE' 0 1 NA 1 NA NA
'CSWE' 1 1 0 0 -1 0
'CSWE' 0 1 0 1 0 1
'CSWE' 0 0 NA 0 NA NA
'CSWE' 0 1 0 1 0 1
'CSWE' 0 0 0 0 0 0
'CSWE' 0 0 NA 0 NA NA
'CSWE' 0 0 0 0 0 0
'CSWE' 0 1 0 1 0 1
'CSWE' 0 0 0 0 0 0
'CSWE' 0 0 1 0 1 1
'CSWE' 0 1 0 1 0 1
'CSWE' 0 1 0 1 0 1
'CSWE' 1 1 0 0 -1 0
'CSWE' 0 0 1 0 1 1
'CSWE' 0 1 1 1 1 2
'CSWE' 0 1 0 1 0 1
'CSWE' 0 0 NA 0 NA NA
'CSWE' 0 0 0 0 0 0
'CSWE' 0 1 NA 1 NA NA
'CSWE' 0 0 0 0 0 0
'CSWE' 0 0 0 0 0 0
'CSWE' 0 0 0 0 0 0
'CSWE' 0 1 0 1 0 1
'CSWE' 0 1 0 1 0 1
'C' 0 0 0 0 0 0
'C' 0 0 1 0 1 1
'C' 1 0 0 -1 -1 -1
'C' 0 0 1 0 1 1
'C' 0 0 NA 0 NA NA
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 1 0 1 0 1
'C' 1 1 0 0 -1 0
'C' 0 0 NA 0 NA NA
'C' 0 0 0 0 0 0
'C' 0 1 0 1 0 1
'C' 0 1 0 1 0 1
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 NA 0 NA NA
'C' 0 0 0 0 0 0
'C' 0 1 0 1 0 1
'C' 1 1 0 0 -1 0
'C' 0 1 0 1 0 1
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 1 1 0 0 -1 0
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 1 1 0 0 -1 0
'C' 0 0 1 0 1 1
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 0 0 0 0
'C' 0 0 1 0 1 1
'C' 0 0 0 0 0 0
'C' 0 0 NA 0 NA NA
'C' 1 1 0 0 -1 0
  
  
 
 
 
 




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net

\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 & 5 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ jenkins.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=223195&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]5 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ jenkins.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=223195&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=223195&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 time5 seconds
R Server'Gwilym Jenkins' @ jenkins.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.0340.0130.0060.466-0.355-0.193

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.034 & 0.013 & 0.006 & 0.466 & -0.355 & -0.193 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=223195&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.034[/C][C]0.013[/C][C]0.006[/C][C]0.466[/C][C]-0.355[/C][C]-0.193[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=223195&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=223195&T=1

As an alternative you can also use a QR Code:  

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

ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.0340.0130.0060.466-0.355-0.193







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A20.0680.0340.3410.712
Treatment_B21.9191.91919.2870
Treatment_A:Treatment_B20.5380.2692.7050.071
Residuals11411.3420.099

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 0.068 & 0.034 & 0.341 & 0.712 \tabularnewline
Treatment_B & 2 & 1.919 & 1.919 & 19.287 & 0 \tabularnewline
Treatment_A:Treatment_B & 2 & 0.538 & 0.269 & 2.705 & 0.071 \tabularnewline
Residuals & 114 & 11.342 & 0.099 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=223195&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]0.068[/C][C]0.034[/C][C]0.341[/C][C]0.712[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]1.919[/C][C]1.919[/C][C]19.287[/C][C]0[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]0.538[/C][C]0.269[/C][C]2.705[/C][C]0.071[/C][/ROW]
[ROW][C]Residuals[/C][C]114[/C][C]11.342[/C][C]0.099[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=223195&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=223195&T=2

As an alternative you can also use a QR Code:  

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

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A20.0680.0340.3410.712
Treatment_B21.9191.91919.2870
Treatment_A:Treatment_B20.5380.2692.7050.071
Residuals11411.3420.099







Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C-0.054-0.2220.1150.729
WWE-C-0.008-0.1750.160.994
WWE-CSWE0.046-0.120.2130.786
1-00.2570.1390.3750
CSWE:0-C:00.013-0.2490.2751
WWE:0-C:00.006-0.2440.2551
C:1-C:00.4660.130.8010.001
CSWE:1-C:00.123-0.1460.3930.77
WWE:1-C:00.278-0.0070.5630.06
WWE:0-CSWE:0-0.008-0.2780.2631
C:1-CSWE:00.4520.1010.8040.004
CSWE:1-CSWE:00.11-0.1790.40.879
WWE:1-CSWE:00.265-0.0390.5680.124
C:1-WWE:00.460.1180.8020.002
CSWE:1-WWE:00.118-0.160.3960.822
WWE:1-WWE:00.273-0.020.5650.083
CSWE:1-C:1-0.342-0.6990.0150.069
WWE:1-C:1-0.188-0.5560.1810.681
WWE:1-CSWE:10.155-0.1560.4650.7

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
CSWE-C & -0.054 & -0.222 & 0.115 & 0.729 \tabularnewline
WWE-C & -0.008 & -0.175 & 0.16 & 0.994 \tabularnewline
WWE-CSWE & 0.046 & -0.12 & 0.213 & 0.786 \tabularnewline
1-0 & 0.257 & 0.139 & 0.375 & 0 \tabularnewline
CSWE:0-C:0 & 0.013 & -0.249 & 0.275 & 1 \tabularnewline
WWE:0-C:0 & 0.006 & -0.244 & 0.255 & 1 \tabularnewline
C:1-C:0 & 0.466 & 0.13 & 0.801 & 0.001 \tabularnewline
CSWE:1-C:0 & 0.123 & -0.146 & 0.393 & 0.77 \tabularnewline
WWE:1-C:0 & 0.278 & -0.007 & 0.563 & 0.06 \tabularnewline
WWE:0-CSWE:0 & -0.008 & -0.278 & 0.263 & 1 \tabularnewline
C:1-CSWE:0 & 0.452 & 0.101 & 0.804 & 0.004 \tabularnewline
CSWE:1-CSWE:0 & 0.11 & -0.179 & 0.4 & 0.879 \tabularnewline
WWE:1-CSWE:0 & 0.265 & -0.039 & 0.568 & 0.124 \tabularnewline
C:1-WWE:0 & 0.46 & 0.118 & 0.802 & 0.002 \tabularnewline
CSWE:1-WWE:0 & 0.118 & -0.16 & 0.396 & 0.822 \tabularnewline
WWE:1-WWE:0 & 0.273 & -0.02 & 0.565 & 0.083 \tabularnewline
CSWE:1-C:1 & -0.342 & -0.699 & 0.015 & 0.069 \tabularnewline
WWE:1-C:1 & -0.188 & -0.556 & 0.181 & 0.681 \tabularnewline
WWE:1-CSWE:1 & 0.155 & -0.156 & 0.465 & 0.7 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=223195&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]CSWE-C[/C][C]-0.054[/C][C]-0.222[/C][C]0.115[/C][C]0.729[/C][/ROW]
[ROW][C]WWE-C[/C][C]-0.008[/C][C]-0.175[/C][C]0.16[/C][C]0.994[/C][/ROW]
[ROW][C]WWE-CSWE[/C][C]0.046[/C][C]-0.12[/C][C]0.213[/C][C]0.786[/C][/ROW]
[ROW][C]1-0[/C][C]0.257[/C][C]0.139[/C][C]0.375[/C][C]0[/C][/ROW]
[ROW][C]CSWE:0-C:0[/C][C]0.013[/C][C]-0.249[/C][C]0.275[/C][C]1[/C][/ROW]
[ROW][C]WWE:0-C:0[/C][C]0.006[/C][C]-0.244[/C][C]0.255[/C][C]1[/C][/ROW]
[ROW][C]C:1-C:0[/C][C]0.466[/C][C]0.13[/C][C]0.801[/C][C]0.001[/C][/ROW]
[ROW][C]CSWE:1-C:0[/C][C]0.123[/C][C]-0.146[/C][C]0.393[/C][C]0.77[/C][/ROW]
[ROW][C]WWE:1-C:0[/C][C]0.278[/C][C]-0.007[/C][C]0.563[/C][C]0.06[/C][/ROW]
[ROW][C]WWE:0-CSWE:0[/C][C]-0.008[/C][C]-0.278[/C][C]0.263[/C][C]1[/C][/ROW]
[ROW][C]C:1-CSWE:0[/C][C]0.452[/C][C]0.101[/C][C]0.804[/C][C]0.004[/C][/ROW]
[ROW][C]CSWE:1-CSWE:0[/C][C]0.11[/C][C]-0.179[/C][C]0.4[/C][C]0.879[/C][/ROW]
[ROW][C]WWE:1-CSWE:0[/C][C]0.265[/C][C]-0.039[/C][C]0.568[/C][C]0.124[/C][/ROW]
[ROW][C]C:1-WWE:0[/C][C]0.46[/C][C]0.118[/C][C]0.802[/C][C]0.002[/C][/ROW]
[ROW][C]CSWE:1-WWE:0[/C][C]0.118[/C][C]-0.16[/C][C]0.396[/C][C]0.822[/C][/ROW]
[ROW][C]WWE:1-WWE:0[/C][C]0.273[/C][C]-0.02[/C][C]0.565[/C][C]0.083[/C][/ROW]
[ROW][C]CSWE:1-C:1[/C][C]-0.342[/C][C]-0.699[/C][C]0.015[/C][C]0.069[/C][/ROW]
[ROW][C]WWE:1-C:1[/C][C]-0.188[/C][C]-0.556[/C][C]0.181[/C][C]0.681[/C][/ROW]
[ROW][C]WWE:1-CSWE:1[/C][C]0.155[/C][C]-0.156[/C][C]0.465[/C][C]0.7[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=223195&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=223195&T=3

As an alternative you can also use a QR Code:  

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

Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C-0.054-0.2220.1150.729
WWE-C-0.008-0.1750.160.994
WWE-CSWE0.046-0.120.2130.786
1-00.2570.1390.3750
CSWE:0-C:00.013-0.2490.2751
WWE:0-C:00.006-0.2440.2551
C:1-C:00.4660.130.8010.001
CSWE:1-C:00.123-0.1460.3930.77
WWE:1-C:00.278-0.0070.5630.06
WWE:0-CSWE:0-0.008-0.2780.2631
C:1-CSWE:00.4520.1010.8040.004
CSWE:1-CSWE:00.11-0.1790.40.879
WWE:1-CSWE:00.265-0.0390.5680.124
C:1-WWE:00.460.1180.8020.002
CSWE:1-WWE:00.118-0.160.3960.822
WWE:1-WWE:00.273-0.020.5650.083
CSWE:1-C:1-0.342-0.6990.0150.069
WWE:1-C:1-0.188-0.5560.1810.681
WWE:1-CSWE:10.155-0.1560.4650.7







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group56.5110
114

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 6.511 & 0 \tabularnewline
  & 114 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=223195&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]6.511[/C][C]0[/C][/ROW]
[ROW][C] [/C][C]114[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=223195&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=223195&T=4

As an alternative you can also use a QR Code:  

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

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group56.5110
114



Parameters (Session):
par1 = 2 ; par2 = 1 ; par3 = 3 ; par4 = TRUE ;
Parameters (R input):
par1 = 2 ; par2 = 1 ; par3 = 3 ; par4 = TRUE ;
R code (references can be found in the software module):
par4 <- 'TRUE'
par3 <- '3'
par2 <- '2'
par1 <- '1'
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')