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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 computationFri, 08 Dec 2017 15:02:10 +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/08/t1512741804lvc463iew234fus.htm/, Retrieved Mon, 13 May 2024 20:43:54 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308803, Retrieved Mon, 13 May 2024 20:43:54 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact106
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [] [2017-12-08 14:02:10] [97cb41d201d00a446ae5b9683850817f] [Current]
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Dataseries X:
'B'	'F'	10
'B'	'M'	11
'B'	'M'	11
'B'	'M'	13
'B'	'M'	7
'S'	'M'	12
'B'	'M'	11
'B'	'F'	12
'B'	'F'	12
'B'	'M'	12
'S'	'M'	11
'S'	'F'	12
'S'	'M'	10
'B'	'F'	12
'B'	'F'	10
'S'	'M'	15
'B'	'F'	12
'S'	'M'	15
'S'	'M'	13
'B'	'F'	12
'B'	'M'	12
'B'	'F'	9
'B'	'F'	12
'B'	'M'	8
'S'	'F'	10
'S'	'M'	11
'B'	'M'	14
'B'	'F'	14
'B'	'F'	11
'B'	'F'	11
'S'	'M'	11
'B'	'F'	10
'B'	'M'	8
'B'	'M'	12
'B'	'M'	10
'S'	'M'	12
'S'	'F'	8
'S'	'M'	10
'S'	'F'	9
'S'	'M'	12
'S'	'F'	15
'B'	'F'	13
'S'	'M'	11
'B'	'F'	12
'B'	'F'	12
'B'	'M'	9
'B'	'M'	14
'B'	'M'	10
'B'	'M'	14
'B'	'M'	12
'B'	'M'	15
'S'	'F'	9
'B'	'F'	14
'S'	'F'	10
'S'	'M'	11
'S'	'M'	11
'B'	'F'	12
'S'	'M'	11
'B'	'F'	10
'B'	'F'	13
'B'	'M'	8
'B'	'M'	9
'B'	'M'	9
'B'	'M'	15
'S'	'F'	8
'B'	'M'	13
'B'	'M'	10
'B'	'M'	13
'B'	'M'	15
'B'	'M'	10
'S'	'M'	9
'B'	'F'	12
'S'	'M'	12
'B'	'M'	12
'B'	'F'	12
'B'	'M'	10
'B'	'F'	9
'B'	'M'	12
'B'	'M'	12
'B'	'F'	12
'B'	'F'	8
'B'	'M'	11
'B'	'F'	10
'S'	'F'	15
'B'	'M'	13
'S'	'M'	11
'B'	'F'	12
'B'	'F'	11
'B'	'F'	11
'B'	'M'	14
'B'	'M'	14
'B'	'M'	14
'B'	'M'	12
'S'	'F'	10
'B'	'F'	12
'B'	'F'	11
'S'	'F'	15
'S'	'F'	9
'B'	'F'	11
'B'	'M'	14
'B'	'M'	10
'B'	'F'	12
'B'	'F'	10
'B'	'F'	12
'B'	'M'	13
'B'	'M'	13
'B'	'F'	12
'B'	'M'	12
'B'	'M'	9
'B'	'F'	10
'S'	'M'	7
'S'	'M'	12
'S'	'M'	12
'S'	'M'	12
'B'	'M'	13
'S'	'M'	12
'B'	'M'	15
'B'	'F'	8
'B'	'M'	7
'B'	'M'	9
'B'	'M'	11
'B'	'M'	11
'S'	'F'	6
'S'	'F'	6
'S'	'F'	8
'B'	'F'	11
'S'	'M'	12
'S'	'M'	13
'B'	'F'	9
'B'	'M'	10
'S'	'F'	9
'S'	'M'	10
'S'	'M'	12
'S'	'M'	7
'S'	'F'	13
'S'	'M'	10
'S'	'M'	10
'S'	'M'	11
'S'	'M'	8
'S'	'F'	12
'S'	'M'	4
'S'	'M'	13
'S'	'M'	13
'S'	'M'	10
'B'	'F'	12
'B'	'M'	6
'B'	'M'	8
'B'	'M'	13
'S'	'M'	12
'B'	'F'	10
'S'	'F'	10
'B'	'F'	10
'S'	'F'	8
'S'	'M'	11
'B'	'F'	12
'S'	'M'	12
'S'	'F'	12
'S'	'M'	11
'S'	'M'	12
'B'	'F'	10
'B'	'M'	12
'B'	'F'	13
'S'	'F'	10
'S'	'M'	7
'B'	'F'	11
'B'	'M'	10
'S'	'M'	12
'B'	'F'	11
'S'	'M'	10
'S'	'F'	12
'B'	'F'	11
'B'	'M'	14
'B'	'F'	12
'S'	'F'	12
'B'	'M'	10
'S'	'M'	11
'S'	'M'	12
'S'	'M'	12
'S'	'F'	12
'S'	'M'	8
'S'	'F'	11
'B'	'F'	8
'B'	'M'	6
'B'	'M'	13
'S'	'F'	11
'S'	'F'	9
'B'	'F'	11
'B'	'F'	14
'S'	'M'	9
'S'	'F'	13
'S'	'M'	12
'B'	'M'	12
'S'	'F'	11
'S'	'M'	9
'B'	'M'	7
'B'	'M'	10
'S'	'M'	12
'B'	'M'	11
'S'	'M'	11
'S'	'M'	11
'S'	'M'	13
'B'	'M'	11
'S'	'F'	8
'S'	'M'	11
'S'	'M'	10
'S'	'F'	12
'S'	'F'	8
'S'	'M'	13
'S'	'F'	10
'S'	'F'	11
'S'	'F'	11
'S'	'M'	8
'S'	'M'	13
'S'	'M'	12
'S'	'M'	9
'S'	'F'	15
'S'	'M'	12
'S'	'M'	14
'B'	'F'	7
'S'	'M'	11
'S'	'F'	10
'S'	'M'	7
'S'	'F'	11
'S'	'M'	11
'S'	'M'	10
'S'	'F'	9
'S'	'M'	11
'B'	'F'	12
'S'	'F'	10
'S'	'M'	13
'S'	'M'	10
'S'	'F'	10
'S'	'F'	12
'S'	'F'	12
'S'	'F'	12
'S'	'F'	12
'S'	'F'	12
'S'	'F'	12
'S'	'M'	7
'S'	'M'	14
'S'	'M'	9
'B'	'M'	8
'S'	'F'	13
'S'	'M'	6
'B'	'F'	12
'B'	'M'	8
'B'	'F'	6
'B'	'F'	6
'S'	'F'	12
'S'	'M'	10
'S'	'M'	9
'S'	'M'	11
'B'	'M'	14
'S'	'F'	6
'S'	'M'	9
'S'	'F'	11
'S'	'M'	9
'S'	'M'	8
'B'	'F'	6
'S'	'M'	10
'S'	'M'	6
'S'	'M'	9
'B'	'M'	12
'S'	'M'	12
'S'	'M'	13
'B'	'M'	12
'S'	'M'	12
'S'	'F'	10
'S'	'M'	9
'S'	'M'	12
'S'	'M'	14
'S'	'F'	6
'S'	'M'	13
'S'	'M'	12
'S'	'M'	13
'S'	'F'	6
'S'	'F'	12
'S'	'F'	10
'S'	'F'	9
'S'	'M'	12
'S'	'F'	7
'S'	'M'	10
'S'	'M'	11
'S'	'M'	15
'S'	'M'	10
'S'	'F'	12
'S'	'F'	10
'S'	'M'	12
'S'	'F'	11
'S'	'M'	11
'S'	'M'	12
'S'	'M'	15
'S'	'F'	12
'S'	'F'	11
'S'	'M'	9
'S'	'M'	11
'S'	'F'	11
'B'	'M'	9
'S'	'M'	15
'S'	'F'	12
'S'	'F'	9
'S'	'F'	12
'S'	'F'	12
'S'	'F'	9
'S'	'M'	9
'S'	'M'	11
'S'	'M'	12
'S'	'M'	12
'B'	'F'	12
'S'	'F'	12
'S'	'F'	6
'S'	'M'	11
'S'	'M'	12
'S'	'M'	9
'S'	'M'	11
'S'	'M'	9
'S'	'M'	10
'S'	'F'	10
'S'	'F'	9
'S'	'F'	12
'S'	'F'	11
'S'	'F'	9
'S'	'F'	9
'S'	'M'	12
'B'	'F'	6
'S'	'F'	10
'S'	'M'	12
'B'	'M'	11
'B'	'F'	14
'S'	'F'	8
'S'	'F'	9
'S'	'F'	10
'S'	'F'	10
'S'	'F'	10
'S'	'F'	11
'S'	'M'	10
'S'	'M'	12
'S'	'M'	14
'B'	'F'	10
'B'	'M'	8
'B'	'M'	8
'B'	'M'	7
'B'	'M'	11
'B'	'F'	6
'B'	'F'	9
'S'	'F'	12
'S'	'F'	12
'B'	'F'	12
'S'	'M'	9
'S'	'M'	15
'S'	'M'	15
'S'	'M'	13
'S'	'M'	9
'S'	'F'	12
'S'	'M'	9
'S'	'M'	15
'S'	'F'	11
'S'	'M'	11
'S'	'M'	6
'S'	'M'	14
'S'	'F'	11
'S'	'M'	8
'S'	'F'	10
'S'	'M'	10
'S'	'F'	9
'S'	'F'	8
'S'	'M'	9
'S'	'F'	10
'S'	'M'	11
'S'	'M'	14
'S'	'F'	12
'B'	'F'	9
'S'	'M'	13
'S'	'F'	8
'B'	'M'	12
'B'	'M'	14
'S'	'F'	9
'S'	'M'	10
'S'	'F'	12
'S'	'M'	12
'S'	'F'	9
'S'	'F'	9
'S'	'M'	12
'S'	'M'	15
'S'	'M'	12
'B'	'F'	11
'S'	'F'	8
'S'	'F'	11
'S'	'F'	11
'S'	'F'	10
'S'	'M'	12
'S'	'F'	9
'B'	'M'	11
'S'	'F'	15
'B'	'M'	14
'S'	'F'	6
'B'	'M'	9
'S'	'F'	9
'S'	'M'	8
'B'	'F'	7
'S'	'F'	10
'B'	'F'	6
'B'	'F'	9
'B'	'M'	9
'B'	'M'	7
'S'	'F'	11
'B'	'F'	9
'B'	'M'	12
'B'	'M'	9
'B'	'F'	10
'B'	'F'	11
'B'	'F'	7
'S'	'F'	12
'B'	'M'	8
'B'	'M'	13
'B'	'F'	11
'B'	'M'	11
'S'	'F'	12
'B'	'M'	11
'B'	'F'	12
'B'	'M'	3
'S'	'M'	10
'S'	'M'	13
'S'	'F'	10
'B'	'M'	6
'B'	'M'	11
'S'	'M'	12
'S'	'F'	9
'B'	'M'	10
'S'	'F'	15
'B'	'M'	9
'B'	'F'	6
'S'	'M'	9
'B'	'F'	15
'S'	'M'	15
'S'	'M'	9
'B'	'M'	11
'B'	'M'	9
'S'	'M'	11
'S'	'M'	10
'S'	'F'	9
'B'	'F'	6
'S'	'F'	12
'S'	'M'	13
'B'	'F'	12
'B'	'F'	12




Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time5 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 time5 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308803&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]5 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=308803&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308803&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 time5 seconds
R ServerBig Analytics Cloud Computing Center







ANOVA Model
Response ~ Treatment_A * Treatment_B
means10.537-0.1610.2630.4

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 10.537 & -0.161 & 0.263 & 0.4 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308803&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]10.537[/C][C]-0.161[/C][C]0.263[/C][C]0.4[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308803&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308803&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
means10.537-0.1610.2630.4







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A10.5680.5680.1190.73
Treatment_B127.92327.9235.8580.016
Treatment_A:Treatment_B14.2274.2270.8870.347
Residuals4422106.8064.767

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 0.568 & 0.568 & 0.119 & 0.73 \tabularnewline
Treatment_B & 1 & 27.923 & 27.923 & 5.858 & 0.016 \tabularnewline
Treatment_A:Treatment_B & 1 & 4.227 & 4.227 & 0.887 & 0.347 \tabularnewline
Residuals & 442 & 2106.806 & 4.767 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308803&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]0.568[/C][C]0.568[/C][C]0.119[/C][C]0.73[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]27.923[/C][C]27.923[/C][C]5.858[/C][C]0.016[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]4.227[/C][C]4.227[/C][C]0.887[/C][C]0.347[/C][/ROW]
[ROW][C]Residuals[/C][C]442[/C][C]2106.806[/C][C]4.767[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308803&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308803&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_A10.5680.5680.1190.73
Treatment_B127.92327.9235.8580.016
Treatment_A:Treatment_B14.2274.2270.8870.347
Residuals4422106.8064.767







Tukey Honest Significant Difference Comparisons
difflwruprp adj
S-B0.073-0.3420.4880.73
M-F0.5030.0940.9120.016
S:F-B:F-0.161-0.9710.650.957
B:M-B:F0.263-0.5851.1120.854
S:M-B:F0.503-0.2691.2740.335
B:M-S:F0.424-0.3541.2010.496
S:M-S:F0.663-0.0291.3560.066
S:M-B:M0.239-0.4970.9760.836

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
S-B & 0.073 & -0.342 & 0.488 & 0.73 \tabularnewline
M-F & 0.503 & 0.094 & 0.912 & 0.016 \tabularnewline
S:F-B:F & -0.161 & -0.971 & 0.65 & 0.957 \tabularnewline
B:M-B:F & 0.263 & -0.585 & 1.112 & 0.854 \tabularnewline
S:M-B:F & 0.503 & -0.269 & 1.274 & 0.335 \tabularnewline
B:M-S:F & 0.424 & -0.354 & 1.201 & 0.496 \tabularnewline
S:M-S:F & 0.663 & -0.029 & 1.356 & 0.066 \tabularnewline
S:M-B:M & 0.239 & -0.497 & 0.976 & 0.836 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308803&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]0.073[/C][C]-0.342[/C][C]0.488[/C][C]0.73[/C][/ROW]
[ROW][C]M-F[/C][C]0.503[/C][C]0.094[/C][C]0.912[/C][C]0.016[/C][/ROW]
[ROW][C]S:F-B:F[/C][C]-0.161[/C][C]-0.971[/C][C]0.65[/C][C]0.957[/C][/ROW]
[ROW][C]B:M-B:F[/C][C]0.263[/C][C]-0.585[/C][C]1.112[/C][C]0.854[/C][/ROW]
[ROW][C]S:M-B:F[/C][C]0.503[/C][C]-0.269[/C][C]1.274[/C][C]0.335[/C][/ROW]
[ROW][C]B:M-S:F[/C][C]0.424[/C][C]-0.354[/C][C]1.201[/C][C]0.496[/C][/ROW]
[ROW][C]S:M-S:F[/C][C]0.663[/C][C]-0.029[/C][C]1.356[/C][C]0.066[/C][/ROW]
[ROW][C]S:M-B:M[/C][C]0.239[/C][C]-0.497[/C][C]0.976[/C][C]0.836[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308803&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308803&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-B0.073-0.3420.4880.73
M-F0.5030.0940.9120.016
S:F-B:F-0.161-0.9710.650.957
B:M-B:F0.263-0.5851.1120.854
S:M-B:F0.503-0.2691.2740.335
B:M-S:F0.424-0.3541.2010.496
S:M-S:F0.663-0.0291.3560.066
S:M-B:M0.239-0.4970.9760.836







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group31.560.199
442

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 1.56 & 0.199 \tabularnewline
  & 442 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308803&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]1.56[/C][C]0.199[/C][/ROW]
[ROW][C] [/C][C]442[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308803&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308803&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)
Group31.560.199
442



Parameters (Session):
par1 = 22less3333 ; par2 = 110.951111 ; par3 = FALSETRUE02222 ; par4 = TRUETRUETRUETRUE ;
Parameters (R input):
par1 = 3 ; par2 = 1 ; par3 = 2 ; par4 = TRUE ;
R code (references can be found in the software module):
par4 <- 'TRUE'
par3 <- '2'
par2 <- '1'
par1 <- '3'
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')