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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 computationTue, 19 Dec 2017 16:21:19 +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/19/t1513697244mjqvewfpg0ua28l.htm/, Retrieved Wed, 15 May 2024 06:13:34 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=310362, Retrieved Wed, 15 May 2024 06:13:34 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact86
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [] [2017-12-19 15:21:19] [a05a61c21185f3f8a89ce513fde9525a] [Current]
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Dataseries X:
3	'B'	'F'
4	'B'	'M'
5	'B'	'M'
4	'B'	'M'
4	'B'	'M'
5	'S'	'M'
5	'B'	'M'
4	'B'	'F'
4	'B'	'F'
4	'B'	'M'
4	'S'	'M'
4	'S'	'F'
4	'S'	'M'
4	'B'	'F'
4	'B'	'F'
4	'S'	'M'
4	'B'	'F'
4	'S'	'M'
5	'S'	'M'
3	'B'	'F'
4	'B'	'M'
3	'B'	'F'
4	'B'	'F'
3	'B'	'M'
4	'S'	'F'
4	'S'	'M'
4	'B'	'M'
4	'B'	'F'
4	'B'	'F'
4	'B'	'F'
4	'S'	'M'
3	'B'	'F'
2	'B'	'M'
5	'B'	'M'
4	'B'	'M'
4	'S'	'M'
3	'S'	'F'
3	'S'	'M'
3	'S'	'F'
3	'S'	'M'
4	'S'	'F'
4	'B'	'F'
3	'S'	'M'
5	'B'	'F'
4	'B'	'F'
4	'B'	'M'
5	'B'	'M'
4	'B'	'M'
3	'B'	'M'
4	'B'	'M'
5	'B'	'M'
3	'S'	'F'
4	'B'	'F'
4	'S'	'F'
4	'S'	'M'
4	'S'	'M'
4	'B'	'F'
4	'S'	'M'
3	'B'	'F'
4	'B'	'F'
4	'B'	'M'
4	'B'	'M'
4	'B'	'M'
5	'B'	'M'
4	'S'	'F'
4	'B'	'M'
4	'B'	'M'
4	'B'	'M'
5	'B'	'M'
3	'B'	'M'
2	'S'	'M'
4	'B'	'F'
5	'S'	'M'
4	'B'	'M'
4	'B'	'F'
3	'B'	'M'
3	'B'	'F'
4	'B'	'M'
4	'B'	'M'
4	'B'	'F'
4	'B'	'F'
4	'B'	'M'
3	'B'	'F'
4	'S'	'F'
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4	'B'	'F'
3	'B'	'F'
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4	'B'	'M'
4	'B'	'M'
4	'B'	'M'
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4	'B'	'F'
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4	'B'	'F'
4	'B'	'M'
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4	'B'	'F'
4	'B'	'M'
3	'B'	'M'
4	'B'	'F'
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3	'S'	'M'
3	'S'	'M'
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4	'S'	'M'
5	'B'	'M'
3	'B'	'F'
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4	'B'	'M'
4	'B'	'M'
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3	'S'	'F'
3	'S'	'M'
4	'S'	'M'
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4	'S'	'F'
4	'S'	'M'
4	'S'	'M'
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4	'S'	'F'
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4	'S'	'M'
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2	'B'	'M'
4	'B'	'M'
4	'S'	'M'
4	'B'	'F'
4	'S'	'F'
4	'B'	'F'
3	'S'	'F'
3	'S'	'M'
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5	'S'	'M'
3	'S'	'F'
4	'S'	'M'
5	'S'	'M'
3	'B'	'F'
3	'B'	'M'
4	'B'	'F'
2	'S'	'F'
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3	'B'	'F'
4	'B'	'M'
4	'S'	'M'
4	'B'	'F'
3	'S'	'M'
4	'S'	'F'
4	'B'	'F'
5	'B'	'M'
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4	'B'	'M'
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3	'B'	'M'
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4	'S'	'M'
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2	'S'	'F'
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4	'S'	'F'
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4	'S'	'F'
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3	'B'	'F'
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4	'S'	'F'
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4	'S'	'M'
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3	'S'	'F'
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2	'B'	'F'
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3	'S'	'M'
4	'S'	'M'
5	'B'	'M'
1	'S'	'F'
3	'S'	'M'
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3	'S'	'M'
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2	'B'	'F'
3	'S'	'M'
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4	'S'	'M'
5	'S'	'M'
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4	'S'	'M'
4	'S'	'F'
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3	'S'	'F'
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3	'S'	'F'
4	'S'	'M'
2	'S'	'F'
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4	'S'	'M'
5	'S'	'M'
4	'S'	'M'
4	'S'	'F'
4	'S'	'F'
5	'S'	'M'
4	'S'	'F'
3	'S'	'M'
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3	'S'	'F'
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4	'S'	'M'
3	'S'	'M'
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4	'B'	'M'
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4	'S'	'F'
4	'S'	'F'
4	'S'	'F'
4	'S'	'F'
3	'S'	'F'
4	'S'	'M'
4	'S'	'M'
4	'S'	'M'
4	'S'	'M'
4	'B'	'F'
4	'S'	'F'
3	'S'	'F'
3	'S'	'M'
4	'S'	'M'
4	'S'	'M'
4	'S'	'M'
4	'S'	'M'
4	'S'	'M'
3	'S'	'F'
5	'S'	'F'
5	'S'	'F'
4	'S'	'F'
4	'S'	'F'
4	'S'	'F'
4	'S'	'M'
3	'B'	'F'
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4	'S'	'M'
4	'B'	'M'
4	'B'	'F'
3	'S'	'F'
4	'S'	'F'
4	'S'	'F'
5	'S'	'F'
5	'S'	'F'
4	'S'	'F'
4	'S'	'M'
3	'S'	'M'
5	'S'	'M'
4	'B'	'F'
2	'B'	'M'
2	'B'	'M'
3	'B'	'M'
2	'B'	'M'
3	'B'	'F'
2	'B'	'F'
4	'S'	'F'
4	'S'	'F'
3	'B'	'F'
4	'S'	'M'
5	'S'	'M'
5	'S'	'M'
4	'S'	'M'
4	'S'	'M'
4	'S'	'F'
5	'S'	'M'
5	'S'	'M'
4	'S'	'F'
4	'S'	'M'
3	'S'	'M'
4	'S'	'M'
4	'S'	'F'
3	'S'	'M'
4	'S'	'F'
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4	'S'	'F'
3	'S'	'F'
3	'S'	'M'
4	'S'	'F'
4	'S'	'M'
5	'S'	'M'
4	'S'	'F'
4	'B'	'F'
5	'S'	'M'
4	'S'	'F'
4	'B'	'M'
4	'B'	'M'
4	'S'	'F'
4	'S'	'M'
4	'S'	'F'
4	'S'	'M'
3	'S'	'F'
3	'S'	'F'
5	'S'	'M'
5	'S'	'M'
4	'S'	'M'
3	'B'	'F'
4	'S'	'F'
4	'S'	'F'
4	'S'	'F'
3	'S'	'F'
4	'S'	'M'
5	'S'	'F'
4	'B'	'M'
5	'S'	'F'
4	'B'	'M'
4	'S'	'F'
4	'B'	'M'
3	'S'	'F'
3	'S'	'M'
3	'B'	'F'
4	'S'	'F'
4	'B'	'F'
4	'B'	'F'
4	'B'	'M'
2	'B'	'M'
4	'S'	'F'
2	'B'	'F'
4	'B'	'M'
4	'B'	'M'
4	'B'	'F'
3	'B'	'F'
3	'B'	'F'
4	'S'	'F'
3	'B'	'M'
3	'B'	'M'
4	'B'	'F'
4	'B'	'M'
3	'S'	'F'
2	'B'	'M'
3	'B'	'F'
1	'B'	'M'
4	'S'	'M'
4	'S'	'M'
4	'S'	'F'
3	'B'	'M'
4	'B'	'M'
4	'S'	'M'
3	'S'	'F'
4	'B'	'M'
5	'S'	'F'
3	'B'	'M'
3	'B'	'F'
3	'S'	'M'
5	'B'	'F'
5	'S'	'M'
4	'S'	'M'
3	'B'	'M'
5	'B'	'M'
4	'S'	'M'
4	'S'	'M'
3	'S'	'F'
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4	'S'	'F'
4	'S'	'M'
4	'B'	'F'
4	'B'	'F'




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=310362&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=310362&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310362&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
means3.6220.1130.010.065

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 3.622 & 0.113 & 0.01 & 0.065 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310362&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]3.622[/C][C]0.113[/C][C]0.01[/C][C]0.065[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310362&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310362&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
means3.6220.1130.010.065







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A12.3972.3973.8150.051
Treatment_B10.2580.2580.410.522
Treatment_A:Treatment_B10.110.110.1750.676
Residuals442277.6390.628

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 2.397 & 2.397 & 3.815 & 0.051 \tabularnewline
Treatment_B & 1 & 0.258 & 0.258 & 0.41 & 0.522 \tabularnewline
Treatment_A:Treatment_B & 1 & 0.11 & 0.11 & 0.175 & 0.676 \tabularnewline
Residuals & 442 & 277.639 & 0.628 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310362&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]2.397[/C][C]2.397[/C][C]3.815[/C][C]0.051[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]0.258[/C][C]0.258[/C][C]0.41[/C][C]0.522[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]0.11[/C][C]0.11[/C][C]0.175[/C][C]0.676[/C][/ROW]
[ROW][C]Residuals[/C][C]442[/C][C]277.639[/C][C]0.628[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310362&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310362&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_A12.3972.3973.8150.051
Treatment_B10.2580.2580.410.522
Treatment_A:Treatment_B10.110.110.1750.676
Residuals442277.6390.628







Tukey Honest Significant Difference Comparisons
difflwruprp adj
S-B0.15-0.0010.3010.051
M-F0.048-0.10.1970.522
S:F-B:F0.113-0.1810.4070.755
B:M-B:F0.01-0.2980.3181
S:M-B:F0.187-0.0930.4670.312
B:M-S:F-0.103-0.3860.1790.78
S:M-S:F0.074-0.1770.3260.872
S:M-B:M0.178-0.090.4450.318

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
S-B & 0.15 & -0.001 & 0.301 & 0.051 \tabularnewline
M-F & 0.048 & -0.1 & 0.197 & 0.522 \tabularnewline
S:F-B:F & 0.113 & -0.181 & 0.407 & 0.755 \tabularnewline
B:M-B:F & 0.01 & -0.298 & 0.318 & 1 \tabularnewline
S:M-B:F & 0.187 & -0.093 & 0.467 & 0.312 \tabularnewline
B:M-S:F & -0.103 & -0.386 & 0.179 & 0.78 \tabularnewline
S:M-S:F & 0.074 & -0.177 & 0.326 & 0.872 \tabularnewline
S:M-B:M & 0.178 & -0.09 & 0.445 & 0.318 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310362&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]S-B[/C][C]0.15[/C][C]-0.001[/C][C]0.301[/C][C]0.051[/C][/ROW]
[ROW][C]M-F[/C][C]0.048[/C][C]-0.1[/C][C]0.197[/C][C]0.522[/C][/ROW]
[ROW][C]S:F-B:F[/C][C]0.113[/C][C]-0.181[/C][C]0.407[/C][C]0.755[/C][/ROW]
[ROW][C]B:M-B:F[/C][C]0.01[/C][C]-0.298[/C][C]0.318[/C][C]1[/C][/ROW]
[ROW][C]S:M-B:F[/C][C]0.187[/C][C]-0.093[/C][C]0.467[/C][C]0.312[/C][/ROW]
[ROW][C]B:M-S:F[/C][C]-0.103[/C][C]-0.386[/C][C]0.179[/C][C]0.78[/C][/ROW]
[ROW][C]S:M-S:F[/C][C]0.074[/C][C]-0.177[/C][C]0.326[/C][C]0.872[/C][/ROW]
[ROW][C]S:M-B:M[/C][C]0.178[/C][C]-0.09[/C][C]0.445[/C][C]0.318[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310362&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310362&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
S-B0.15-0.0010.3010.051
M-F0.048-0.10.1970.522
S:F-B:F0.113-0.1810.4070.755
B:M-B:F0.01-0.2980.3181
S:M-B:F0.187-0.0930.4670.312
B:M-S:F-0.103-0.3860.1790.78
S:M-S:F0.074-0.1770.3260.872
S:M-B:M0.178-0.090.4450.318







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group31.4420.23
442

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 1.442 & 0.23 \tabularnewline
  & 442 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=310362&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]1.442[/C][C]0.23[/C][/ROW]
[ROW][C] [/C][C]442[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=310362&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=310362&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)
Group31.4420.23
442



Parameters (Session):
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$'Df'[i+1],,FALSE)
a<-table.element(a, round(anova.xdf$'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')