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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 computationWed, 20 Dec 2017 09:47:02 +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/2017/Dec/20/t1513760804zyko6szya0pwl9k.htm/, Retrieved Tue, 14 May 2024 01:01:00 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310458, Retrieved Tue, 14 May 2024 01:01:00 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact136
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [Two-way Anova dat...] [2017-12-20 08:47:02] [2c7049bbcc29bc93573a73f5c62450a0] [Current]
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Dataseries X:
4	'F'	'B'
3	'M'	'B'
4	'M'	'B'
4	'M'	'B'
4	'M'	'B'
4	'M'	'S'
4	'M'	'B'
4	'F'	'B'
4	'F'	'B'
4	'M'	'B'
4	'M'	'S'
4	'F'	'S'
4	'M'	'S'
4	'F'	'B'
4	'F'	'B'
5	'M'	'S'
4	'F'	'B'
5	'M'	'S'
5	'M'	'S'
4	'F'	'B'
5	'M'	'B'
2	'F'	'B'
4	'F'	'B'
4	'M'	'B'
4	'F'	'S'
4	'M'	'S'
4	'M'	'B'
4	'F'	'B'
4	'F'	'B'
4	'F'	'B'
4	'M'	'S'
3	'F'	'B'
3	'M'	'B'
5	'M'	'B'
4	'M'	'B'
3	'M'	'S'
2	'F'	'S'
4	'M'	'S'
3	'F'	'S'
3	'M'	'S'
4	'F'	'S'
5	'F'	'B'
3	'M'	'S'
4	'F'	'B'
4	'F'	'B'
4	'M'	'B'
5	'M'	'B'
4	'M'	'B'
4	'M'	'B'
4	'M'	'B'
5	'M'	'B'
3	'F'	'S'
4	'F'	'B'
4	'F'	'S'
4	'M'	'S'
4	'M'	'S'
4	'F'	'B'
4	'M'	'S'
2	'F'	'B'
4	'F'	'B'
4	'M'	'B'
4	'M'	'B'
4	'M'	'B'
5	'M'	'B'
4	'F'	'S'
4	'M'	'B'
5	'M'	'B'
5	'M'	'B'
5	'M'	'B'
2	'M'	'B'
2	'M'	'S'
4	'F'	'B'
5	'M'	'S'
5	'M'	'B'
4	'F'	'B'
3	'M'	'B'
4	'F'	'B'
4	'M'	'B'
4	'M'	'B'
4	'F'	'B'
4	'F'	'B'
2	'M'	'B'
4	'F'	'B'
5	'F'	'S'
5	'M'	'B'
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4	'F'	'B'
4	'F'	'B'
3	'F'	'B'
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4	'M'	'B'
4	'M'	'B'
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3	'F'	'B'
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4	'F'	'B'
4	'F'	'B'
4	'F'	'B'
4	'M'	'B'
4	'M'	'B'
4	'F'	'B'
4	'M'	'B'
4	'M'	'B'
4	'F'	'B'
2	'M'	'S'
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4	'M'	'S'
4	'M'	'B'
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5	'M'	'B'
3	'F'	'B'
3	'M'	'B'
4	'M'	'B'
4	'M'	'B'
4	'M'	'B'
2	'F'	'S'
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3	'F'	'S'
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3	'M'	'S'
3	'F'	'S'
4	'M'	'S'
4	'M'	'S'
3	'M'	'S'
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5	'M'	'B'
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4	'F'	'B'
4	'F'	'S'
4	'F'	'B'
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4	'F'	'S'
4	'M'	'S'
4	'M'	'S'
3	'F'	'B'
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4	'F'	'B'
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3	'F'	'B'
4	'M'	'B'
4	'M'	'S'
5	'F'	'B'
3	'M'	'S'
4	'F'	'S'
4	'F'	'B'
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3	'M'	'S'
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4	'M'	'S'
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3	'M'	'S'
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4	'F'	'S'
3	'M'	'S'
4	'M'	'S'
2	'F'	'B'
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3	'M'	'S'
3	'M'	'S'
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5	'M'	'S'
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4	'M'	'S'
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5	'M'	'S'
5	'M'	'S'
2	'F'	'S'
4	'F'	'S'
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4	'F'	'S'
4	'M'	'S'
3	'F'	'S'
4	'M'	'S'
4	'M'	'S'
5	'M'	'S'
3	'M'	'S'
4	'F'	'S'
4	'F'	'S'
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4	'F'	'S'
3	'M'	'S'
3	'M'	'S'
5	'M'	'S'
3	'F'	'S'
4	'F'	'S'
4	'M'	'S'
4	'M'	'S'
4	'F'	'S'
4	'M'	'B'
4	'M'	'S'
4	'F'	'S'
4	'F'	'S'
4	'F'	'S'
4	'F'	'S'
3	'F'	'S'
4	'M'	'S'
4	'M'	'S'
5	'M'	'S'
4	'M'	'S'
4	'F'	'B'
4	'F'	'S'
4	'F'	'S'
3	'M'	'S'
4	'M'	'S'
4	'M'	'S'
3	'M'	'S'
3	'M'	'S'
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3	'F'	'S'
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5	'F'	'S'
4	'F'	'S'
4	'F'	'S'
3	'F'	'S'
4	'M'	'S'
4	'F'	'B'
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4	'M'	'B'
4	'F'	'B'
3	'F'	'S'
3	'F'	'S'
4	'F'	'S'
4	'F'	'S'
4	'F'	'S'
3	'F'	'S'
4	'M'	'S'
4	'M'	'S'
4	'M'	'S'
4	'F'	'B'
2	'M'	'B'
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3	'M'	'B'
2	'F'	'B'
3	'F'	'B'
4	'F'	'S'
4	'F'	'S'
3	'F'	'B'
4	'M'	'S'
5	'M'	'S'
5	'M'	'S'
4	'M'	'S'
4	'M'	'S'
4	'F'	'S'
4	'M'	'S'
5	'M'	'S'
4	'F'	'S'
4	'M'	'S'
3	'M'	'S'
4	'M'	'S'
3	'F'	'S'
4	'M'	'S'
3	'F'	'S'
2	'M'	'S'
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3	'F'	'S'
4	'M'	'S'
3	'F'	'S'
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5	'M'	'S'
4	'F'	'S'
3	'F'	'B'
5	'M'	'S'
4	'F'	'S'
4	'M'	'B'
4	'M'	'B'
4	'F'	'S'
4	'M'	'S'
3	'F'	'S'
4	'M'	'S'
4	'F'	'S'
3	'F'	'S'
4	'M'	'S'
5	'M'	'S'
4	'M'	'S'
4	'F'	'B'
4	'F'	'S'
4	'F'	'S'
3	'F'	'S'
3	'F'	'S'
4	'M'	'S'
4	'F'	'S'
4	'M'	'B'
5	'F'	'S'
4	'M'	'B'
4	'F'	'S'
4	'M'	'B'
4	'F'	'S'
2	'M'	'S'
3	'F'	'B'
4	'F'	'S'
3	'F'	'B'
3	'F'	'B'
4	'M'	'B'
2	'M'	'B'
4	'F'	'S'
2	'F'	'B'
4	'M'	'B'
4	'M'	'B'
4	'F'	'B'
3	'F'	'B'
3	'F'	'B'
2	'F'	'S'
4	'M'	'B'
4	'M'	'B'
4	'F'	'B'
4	'M'	'B'
3	'F'	'S'
2	'M'	'B'
4	'F'	'B'
1	'M'	'B'
3	'M'	'S'
3	'M'	'S'
3	'F'	'S'
1	'M'	'B'
4	'M'	'B'
4	'M'	'S'
3	'F'	'S'
4	'M'	'B'
4	'F'	'S'
2	'M'	'B'
3	'F'	'B'
4	'M'	'S'
4	'F'	'B'
5	'M'	'S'
3	'M'	'S'
3	'M'	'B'
4	'M'	'B'
4	'M'	'S'
4	'M'	'S'
4	'F'	'S'
3	'F'	'B'
3	'F'	'S'
4	'M'	'S'
4	'F'	'B'
4	'F'	'B'




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\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 time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310458&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]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=310458&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310458&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center







ANOVA Model
Response ~ Treatment_A * Treatment_B
means3.5850.1510.039-0.019

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 3.585 & 0.151 & 0.039 & -0.019 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310458&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]3.585[/C][C]0.151[/C][C]0.039[/C][C]-0.019[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310458&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310458&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
means3.5850.1510.039-0.019







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A12.1892.1893.1440.077
Treatment_B10.0850.0850.1220.727
Treatment_A:Treatment_B10.0090.0090.0130.908
Residuals442307.770.696

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 2.189 & 2.189 & 3.144 & 0.077 \tabularnewline
Treatment_B & 1 & 0.085 & 0.085 & 0.122 & 0.727 \tabularnewline
Treatment_A:Treatment_B & 1 & 0.009 & 0.009 & 0.013 & 0.908 \tabularnewline
Residuals & 442 & 307.77 & 0.696 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310458&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]1[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]1[/C][C]2.189[/C][C]2.189[/C][C]3.144[/C][C]0.077[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]0.085[/C][C]0.085[/C][C]0.122[/C][C]0.727[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]0.009[/C][C]0.009[/C][C]0.013[/C][C]0.908[/C][/ROW]
[ROW][C]Residuals[/C][C]442[/C][C]307.77[/C][C]0.696[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310458&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310458&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)
1
Treatment_A12.1892.1893.1440.077
Treatment_B10.0850.0850.1220.727
Treatment_A:Treatment_B10.0090.0090.0130.908
Residuals442307.770.696







Tukey Honest Significant Difference Comparisons
difflwruprp adj
M-F0.141-0.0150.2970.077
S-B0.028-0.1310.1870.727
M:B-F:B0.151-0.1730.4760.624
F:S-F:B0.039-0.2710.3480.989
M:S-F:B0.171-0.1240.4660.44
F:S-M:B-0.113-0.410.1840.761
M:S-M:B0.02-0.2620.3010.998
M:S-F:S0.133-0.1320.3970.568

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
M-F & 0.141 & -0.015 & 0.297 & 0.077 \tabularnewline
S-B & 0.028 & -0.131 & 0.187 & 0.727 \tabularnewline
M:B-F:B & 0.151 & -0.173 & 0.476 & 0.624 \tabularnewline
F:S-F:B & 0.039 & -0.271 & 0.348 & 0.989 \tabularnewline
M:S-F:B & 0.171 & -0.124 & 0.466 & 0.44 \tabularnewline
F:S-M:B & -0.113 & -0.41 & 0.184 & 0.761 \tabularnewline
M:S-M:B & 0.02 & -0.262 & 0.301 & 0.998 \tabularnewline
M:S-F:S & 0.133 & -0.132 & 0.397 & 0.568 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310458&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]M-F[/C][C]0.141[/C][C]-0.015[/C][C]0.297[/C][C]0.077[/C][/ROW]
[ROW][C]S-B[/C][C]0.028[/C][C]-0.131[/C][C]0.187[/C][C]0.727[/C][/ROW]
[ROW][C]M:B-F:B[/C][C]0.151[/C][C]-0.173[/C][C]0.476[/C][C]0.624[/C][/ROW]
[ROW][C]F:S-F:B[/C][C]0.039[/C][C]-0.271[/C][C]0.348[/C][C]0.989[/C][/ROW]
[ROW][C]M:S-F:B[/C][C]0.171[/C][C]-0.124[/C][C]0.466[/C][C]0.44[/C][/ROW]
[ROW][C]F:S-M:B[/C][C]-0.113[/C][C]-0.41[/C][C]0.184[/C][C]0.761[/C][/ROW]
[ROW][C]M:S-M:B[/C][C]0.02[/C][C]-0.262[/C][C]0.301[/C][C]0.998[/C][/ROW]
[ROW][C]M:S-F:S[/C][C]0.133[/C][C]-0.132[/C][C]0.397[/C][C]0.568[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310458&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310458&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
M-F0.141-0.0150.2970.077
S-B0.028-0.1310.1870.727
M:B-F:B0.151-0.1730.4760.624
F:S-F:B0.039-0.2710.3480.989
M:S-F:B0.171-0.1240.4660.44
F:S-M:B-0.113-0.410.1840.761
M:S-M:B0.02-0.2620.3010.998
M:S-F:S0.133-0.1320.3970.568







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group30.3320.802
442

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 0.332 & 0.802 \tabularnewline
  & 442 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310458&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]3[/C][C]0.332[/C][C]0.802[/C][/ROW]
[ROW][C] [/C][C]442[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310458&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310458&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)
Group30.3320.802
442



Parameters (Session):
par1 = 1 ; par2 = Do not include Seasonal Dummies ; par3 = No Linear Trend ; par4 = 0 ; par5 = 0 ; par6 = 12 ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; 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')