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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 computationSat, 25 Nov 2017 16:40:08 +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/Nov/25/t1511624455mz7iohrjunkx0hh.htm/, Retrieved Sat, 18 May 2024 09:55:04 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=308215, Retrieved Sat, 18 May 2024 09:55:04 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact140
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [test] [2017-11-25 15:40:08] [df69f135d5ff041b1c3aa0a11119be0d] [Current]
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Dataseries X:
22	'F'	'B'
39	'M'	'B'
40	'M'	'B'
34	'M'	'B'
38	'M'	'B'
39	'M'	'S'
39	'M'	'B'
38	'F'	'B'
31	'F'	'B'
34	'M'	'B'
32	'M'	'S'
37	'F'	'S'
36	'M'	'S'
38	'F'	'B'
29	'F'	'B'
33	'M'	'S'
35	'F'	'B'
34	'M'	'S'
45	'M'	'S'
30	'F'	'B'
33	'M'	'B'
30	'F'	'B'
40	'F'	'B'
34	'M'	'B'
31	'F'	'S'
27	'M'	'S'
33	'M'	'B'
42	'F'	'B'
36	'F'	'B'
33	'F'	'B'
42	'M'	'S'
33	'F'	'B'
21	'M'	'B'
43	'M'	'B'
34	'M'	'B'
32	'M'	'S'
34	'F'	'S'
28	'M'	'S'
30	'F'	'S'
27	'M'	'S'
29	'F'	'S'
40	'F'	'B'
29	'M'	'S'
41	'F'	'B'
33	'F'	'B'
42	'M'	'B'
39	'M'	'B'
35	'M'	'B'
33	'M'	'B'
33	'M'	'B'
44	'M'	'B'
34	'F'	'S'
30	'F'	'B'
30	'F'	'S'
35	'M'	'S'
39	'M'	'S'
34	'F'	'B'
39	'M'	'S'
25	'F'	'B'
39	'F'	'B'
33	'M'	'B'
34	'M'	'B'
36	'M'	'B'
34	'M'	'B'
31	'F'	'S'
35	'M'	'B'
34	'M'	'B'
36	'M'	'B'
40	'M'	'B'
31	'M'	'B'
33	'M'	'S'
28	'F'	'B'
42	'M'	'S'
38	'M'	'B'
35	'F'	'B'
34	'M'	'B'
28	'F'	'B'
35	'M'	'B'
25	'M'	'B'
39	'F'	'B'
25	'F'	'B'
32	'M'	'B'
35	'F'	'B'
41	'F'	'S'
34	'M'	'B'
33	'M'	'S'
32	'F'	'B'
34	'F'	'B'
25	'F'	'B'
38	'M'	'B'
37	'M'	'B'
38	'M'	'B'
36	'M'	'B'
39	'F'	'S'
31	'F'	'B'
40	'F'	'B'
34	'F'	'S'
33	'F'	'S'
32	'F'	'B'
33	'M'	'B'
32	'M'	'B'
28	'F'	'B'
32	'F'	'B'
34	'F'	'B'
36	'M'	'B'
38	'M'	'B'
31	'F'	'B'
36	'M'	'B'
27	'M'	'B'
31	'F'	'B'
28	'M'	'S'
30	'M'	'S'
29	'M'	'S'
29	'M'	'S'
31	'M'	'B'
35	'M'	'S'
42	'M'	'B'
28	'F'	'B'
38	'M'	'B'
34	'M'	'B'
28	'M'	'B'
30	'M'	'B'
26	'F'	'S'
27	'F'	'S'
31	'F'	'S'
35	'F'	'B'
33	'M'	'S'
34	'M'	'S'
30	'F'	'B'
28	'M'	'B'
30	'F'	'S'
29	'M'	'S'
32	'M'	'S'
34	'M'	'S'
34	'F'	'S'
35	'M'	'S'
40	'M'	'S'
34	'M'	'S'
28	'M'	'S'
35	'F'	'S'
31	'M'	'S'
33	'M'	'S'
36	'M'	'S'
30	'M'	'S'
27	'F'	'B'
30	'M'	'B'
25	'M'	'B'
39	'M'	'B'
36	'M'	'S'
31	'F'	'B'
33	'F'	'S'
30	'F'	'B'
31	'F'	'S'
32	'M'	'S'
33	'F'	'B'
43	'M'	'S'
35	'F'	'S'
36	'M'	'S'
42	'M'	'S'
31	'F'	'B'
26	'M'	'B'
38	'F'	'B'
27	'F'	'S'
27	'M'	'S'
31	'F'	'B'
32	'M'	'B'
36	'M'	'S'
36	'F'	'B'
25	'M'	'S'
33	'F'	'S'
32	'F'	'B'
40	'M'	'B'
36	'F'	'B'
36	'F'	'S'
35	'M'	'B'
31	'M'	'S'
31	'M'	'S'
36	'M'	'S'
36	'F'	'S'
37	'M'	'S'
31	'F'	'S'
31	'F'	'B'
26	'M'	'B'
35	'M'	'B'
32	'F'	'S'
36	'F'	'S'
37	'F'	'B'
34	'F'	'B'
33	'M'	'S'
35	'F'	'S'
31	'M'	'S'
38	'M'	'B'
36	'F'	'S'
32	'M'	'S'
28	'M'	'B'
33	'M'	'B'
31	'M'	'S'
34	'M'	'B'
33	'M'	'S'
36	'M'	'S'
36	'M'	'S'
29	'M'	'B'
31	'F'	'S'
35	'M'	'S'
31	'M'	'S'
35	'F'	'S'
36	'F'	'S'
35	'M'	'S'
38	'F'	'S'
28	'F'	'S'
28	'F'	'S'
28	'M'	'S'
34	'M'	'S'
31	'M'	'S'
44	'M'	'S'
36	'F'	'S'
36	'M'	'S'
34	'M'	'S'
32	'F'	'B'
36	'M'	'S'
38	'F'	'S'
28	'M'	'S'
37	'F'	'S'
32	'M'	'S'
36	'M'	'S'
30	'F'	'S'
38	'M'	'S'
37	'F'	'B'
33	'F'	'S'
43	'M'	'S'
26	'M'	'S'
33	'F'	'S'
34	'F'	'S'
36	'F'	'S'
36	'F'	'S'
36	'F'	'S'
36	'F'	'S'
39	'F'	'S'
33	'M'	'S'
35	'M'	'S'
25	'M'	'S'
26	'M'	'B'
35	'F'	'S'
16	'M'	'S'
40	'F'	'B'
14	'M'	'B'
22	'F'	'B'
21	'F'	'B'
38	'F'	'S'
38	'M'	'S'
27	'M'	'S'
40	'M'	'S'
40	'M'	'B'
19	'F'	'S'
29	'M'	'S'
37	'F'	'S'
27	'M'	'S'
26	'M'	'S'
24	'F'	'B'
29	'M'	'S'
26	'M'	'S'
27	'M'	'S'
35	'M'	'B'
39	'M'	'S'
38	'M'	'S'
36	'M'	'B'
37	'M'	'S'
36	'F'	'S'
32	'M'	'S'
33	'M'	'S'
39	'M'	'S'
34	'F'	'S'
39	'M'	'S'
36	'M'	'S'
33	'M'	'S'
30	'F'	'S'
39	'F'	'S'
37	'F'	'S'
37	'F'	'S'
35	'M'	'S'
32	'F'	'S'
36	'M'	'S'
36	'M'	'S'
41	'M'	'S'
36	'M'	'S'
37	'F'	'S'
29	'F'	'S'
39	'M'	'S'
37	'F'	'S'
32	'M'	'S'
36	'M'	'S'
43	'M'	'S'
30	'F'	'S'
33	'F'	'S'
28	'M'	'S'
30	'M'	'S'
28	'F'	'S'
39	'M'	'B'
34	'M'	'S'
34	'F'	'S'
29	'F'	'S'
32	'F'	'S'
33	'F'	'S'
27	'F'	'S'
35	'M'	'S'
38	'M'	'S'
40	'M'	'S'
34	'M'	'S'
34	'F'	'B'
26	'F'	'S'
39	'F'	'S'
34	'M'	'S'
39	'M'	'S'
26	'M'	'S'
30	'M'	'S'
34	'M'	'S'
34	'M'	'S'
29	'F'	'S'
41	'F'	'S'
43	'F'	'S'
31	'F'	'S'
33	'F'	'S'
34	'F'	'S'
30	'M'	'S'
23	'F'	'B'
29	'F'	'S'
35	'M'	'S'
40	'M'	'B'
27	'F'	'B'
30	'F'	'S'
27	'F'	'S'
29	'F'	'S'
33	'F'	'S'
32	'F'	'S'
33	'F'	'S'
36	'M'	'S'
34	'M'	'S'
45	'M'	'S'
30	'F'	'B'
22	'M'	'B'
24	'M'	'B'
25	'M'	'B'
26	'M'	'B'
27	'F'	'B'
27	'F'	'B'
35	'F'	'S'
36	'F'	'S'
32	'F'	'B'
35	'M'	'S'
35	'M'	'S'
36	'M'	'S'
37	'M'	'S'
33	'M'	'S'
25	'F'	'S'
35	'M'	'S'
37	'M'	'S'
36	'F'	'S'
35	'M'	'S'
29	'M'	'S'
35	'M'	'S'
31	'F'	'S'
30	'M'	'S'
37	'F'	'S'
36	'M'	'S'
35	'F'	'S'
32	'F'	'S'
34	'M'	'S'
37	'F'	'S'
36	'M'	'S'
39	'M'	'S'
37	'F'	'S'
31	'F'	'B'
40	'M'	'S'
38	'F'	'S'
35	'M'	'B'
38	'M'	'B'
32	'F'	'S'
41	'M'	'S'
28	'F'	'S'
40	'M'	'S'
25	'F'	'S'
28	'F'	'S'
37	'M'	'S'
37	'M'	'S'
40	'M'	'S'
26	'F'	'B'
30	'F'	'S'
32	'F'	'S'
31	'F'	'S'
28	'F'	'S'
34	'M'	'S'
39	'F'	'S'
33	'M'	'B'
43	'F'	'S'
37	'M'	'B'
31	'F'	'S'
31	'M'	'B'
34	'F'	'S'
32	'M'	'S'
27	'F'	'B'
34	'F'	'S'
28	'F'	'B'
32	'F'	'B'
39	'M'	'B'
28	'M'	'B'
39	'F'	'S'
32	'F'	'B'
36	'M'	'B'
31	'M'	'B'
39	'F'	'B'
23	'F'	'B'
25	'F'	'B'
32	'F'	'S'
32	'M'	'B'
36	'M'	'B'
39	'F'	'B'
31	'M'	'B'
32	'F'	'S'
28	'M'	'B'
34	'F'	'B'
28	'M'	'B'
38	'M'	'S'
35	'M'	'S'
32	'F'	'S'
26	'M'	'B'
32	'M'	'B'
28	'M'	'S'
31	'F'	'S'
33	'M'	'B'
38	'F'	'S'
38	'M'	'B'
36	'F'	'B'
31	'M'	'S'
36	'F'	'B'
43	'M'	'S'
37	'M'	'S'
28	'M'	'B'
35	'M'	'B'
34	'M'	'S'
40	'M'	'S'
31	'F'	'S'
41	'F'	'B'
35	'F'	'S'
38	'M'	'S'
37	'F'	'B'
31	'F'	'B'




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=308215&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=308215&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308215&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
means32.0981.3021.09-0.3

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 32.098 & 1.302 & 1.09 & -0.3 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308215&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]32.098[/C][C]1.302[/C][C]1.09[/C][C]-0.3[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308215&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308215&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
means32.0981.3021.09-0.3







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A1145.231145.2316.440.011
Treatment_B191.51591.5154.0580.045
Treatment_A:Treatment_B12.3732.3730.1050.746
Residuals4429967.3522.551

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 145.231 & 145.231 & 6.44 & 0.011 \tabularnewline
Treatment_B & 1 & 91.515 & 91.515 & 4.058 & 0.045 \tabularnewline
Treatment_A:Treatment_B & 1 & 2.373 & 2.373 & 0.105 & 0.746 \tabularnewline
Residuals & 442 & 9967.35 & 22.551 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308215&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]145.231[/C][C]145.231[/C][C]6.44[/C][C]0.011[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]91.515[/C][C]91.515[/C][C]4.058[/C][C]0.045[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]2.373[/C][C]2.373[/C][C]0.105[/C][C]0.746[/C][/ROW]
[ROW][C]Residuals[/C][C]442[/C][C]9967.35[/C][C]22.551[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308215&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308215&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_A1145.231145.2316.440.011
Treatment_B191.51591.5154.0580.045
Treatment_A:Treatment_B12.3732.3730.1050.746
Residuals4429967.3522.551







Tukey Honest Significant Difference Comparisons
difflwruprp adj
M-F1.1480.2592.0370.011
S-B0.9260.0221.8290.045
M:B-F:B1.302-0.5443.1480.265
F:S-F:B1.09-0.6732.8540.383
M:S-F:B2.0930.4153.7710.008
F:S-M:B-0.212-1.9031.4790.988
M:S-M:B0.791-0.8112.3920.58
M:S-F:S1.003-0.5032.5090.316

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
M-F & 1.148 & 0.259 & 2.037 & 0.011 \tabularnewline
S-B & 0.926 & 0.022 & 1.829 & 0.045 \tabularnewline
M:B-F:B & 1.302 & -0.544 & 3.148 & 0.265 \tabularnewline
F:S-F:B & 1.09 & -0.673 & 2.854 & 0.383 \tabularnewline
M:S-F:B & 2.093 & 0.415 & 3.771 & 0.008 \tabularnewline
F:S-M:B & -0.212 & -1.903 & 1.479 & 0.988 \tabularnewline
M:S-M:B & 0.791 & -0.811 & 2.392 & 0.58 \tabularnewline
M:S-F:S & 1.003 & -0.503 & 2.509 & 0.316 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308215&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]1.148[/C][C]0.259[/C][C]2.037[/C][C]0.011[/C][/ROW]
[ROW][C]S-B[/C][C]0.926[/C][C]0.022[/C][C]1.829[/C][C]0.045[/C][/ROW]
[ROW][C]M:B-F:B[/C][C]1.302[/C][C]-0.544[/C][C]3.148[/C][C]0.265[/C][/ROW]
[ROW][C]F:S-F:B[/C][C]1.09[/C][C]-0.673[/C][C]2.854[/C][C]0.383[/C][/ROW]
[ROW][C]M:S-F:B[/C][C]2.093[/C][C]0.415[/C][C]3.771[/C][C]0.008[/C][/ROW]
[ROW][C]F:S-M:B[/C][C]-0.212[/C][C]-1.903[/C][C]1.479[/C][C]0.988[/C][/ROW]
[ROW][C]M:S-M:B[/C][C]0.791[/C][C]-0.811[/C][C]2.392[/C][C]0.58[/C][/ROW]
[ROW][C]M:S-F:S[/C][C]1.003[/C][C]-0.503[/C][C]2.509[/C][C]0.316[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308215&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308215&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-F1.1480.2592.0370.011
S-B0.9260.0221.8290.045
M:B-F:B1.302-0.5443.1480.265
F:S-F:B1.09-0.6732.8540.383
M:S-F:B2.0930.4153.7710.008
F:S-M:B-0.212-1.9031.4790.988
M:S-M:B0.791-0.8112.3920.58
M:S-F:S1.003-0.5032.5090.316







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group31.4730.221
442

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 1.473 & 0.221 \tabularnewline
  & 442 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=308215&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]1.473[/C][C]0.221[/C][/ROW]
[ROW][C] [/C][C]442[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=308215&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=308215&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)
Group31.4730.221
442



Parameters (Session):
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')