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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, 15 Dec 2017 11:55:35 +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/15/t151333707447p6bqydet2bfjg.htm/, Retrieved Wed, 15 May 2024 06:58:26 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=309655, Retrieved Wed, 15 May 2024 06:58:26 +0000
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Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact77
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [2-Way ANOVA 2] [2017-12-15 10:55:35] [6a3953600cf19f2574c53feb20fccf09] [Current]
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Dataseries X:
32	'B'	'F'
51	'B'	'M'
50	'B'	'M'
48	'B'	'M'
48	'B'	'M'
49	'S'	'M'
48	'B'	'M'
46	'B'	'F'
38	'B'	'F'
43	'B'	'M'
42	'S'	'M'
46	'S'	'F'
46	'S'	'M'
50	'B'	'F'
38	'B'	'F'
48	'S'	'M'
42	'B'	'F'
45	'S'	'M'
53	'S'	'M'
42	'B'	'F'
44	'B'	'M'
39	'B'	'F'
45	'B'	'F'
42	'B'	'M'
43	'S'	'F'
34	'S'	'M'
45	'B'	'M'
53	'B'	'F'
43	'B'	'F'
42	'B'	'F'
53	'S'	'M'
37	'B'	'F'
27	'B'	'M'
48	'B'	'M'
44	'B'	'M'
43	'S'	'M'
40	'S'	'F'
39	'S'	'M'
37	'S'	'F'
38	'S'	'M'
40	'S'	'F'
43	'B'	'F'
34	'S'	'M'
48	'B'	'F'
45	'B'	'F'
48	'B'	'M'
52	'B'	'M'
43	'B'	'M'
47	'B'	'M'
46	'B'	'M'
57	'B'	'M'
39	'S'	'F'
40	'B'	'F'
41	'S'	'F'
44	'S'	'M'
47	'S'	'M'
44	'B'	'F'
51	'S'	'M'
33	'B'	'F'
49	'B'	'F'
42	'B'	'M'
43	'B'	'M'
46	'B'	'M'
45	'B'	'M'
36	'S'	'F'
46	'B'	'M'
42	'B'	'M'
48	'B'	'M'
54	'B'	'M'
43	'B'	'M'
37	'S'	'M'
39	'B'	'F'
52	'S'	'M'
49	'B'	'M'
46	'B'	'F'
41	'B'	'M'
35	'B'	'F'
42	'B'	'M'
35	'B'	'M'
50	'B'	'F'
38	'B'	'F'
43	'B'	'M'
42	'B'	'F'
50	'S'	'F'
48	'B'	'M'
43	'S'	'M'
43	'B'	'F'
44	'B'	'F'
32	'B'	'F'
49	'B'	'M'
47	'B'	'M'
47	'B'	'M'
48	'B'	'M'
49	'S'	'F'
35	'B'	'F'
45	'B'	'F'
41	'S'	'F'
41	'S'	'F'
42	'B'	'F'
45	'B'	'M'
35	'B'	'M'
35	'B'	'F'
40	'B'	'F'
38	'B'	'F'
47	'B'	'M'
51	'B'	'M'
39	'B'	'F'
42	'B'	'M'
36	'B'	'M'
43	'B'	'F'
33	'S'	'M'
40	'S'	'M'
32	'S'	'M'
32	'S'	'M'
40	'B'	'M'
42	'S'	'M'
55	'B'	'M'
31	'B'	'F'
40	'B'	'M'
46	'B'	'M'
40	'B'	'M'
43	'B'	'M'
30	'S'	'F'
34	'S'	'F'
38	'S'	'F'
46	'B'	'F'
43	'S'	'M'
41	'S'	'M'
34	'B'	'F'
35	'B'	'M'
36	'S'	'F'
39	'S'	'M'
45	'S'	'M'
39	'S'	'M'
42	'S'	'F'
40	'S'	'M'
47	'S'	'M'
43	'S'	'M'
32	'S'	'M'
44	'S'	'F'
35	'S'	'M'
41	'S'	'M'
43	'S'	'M'
37	'S'	'M'
38	'B'	'F'
31	'B'	'M'
33	'B'	'M'
50	'B'	'M'
48	'S'	'M'
40	'B'	'F'
39	'S'	'F'
45	'B'	'F'
40	'S'	'F'
41	'S'	'M'
44	'B'	'F'
49	'S'	'M'
46	'S'	'F'
45	'S'	'M'
52	'S'	'M'
37	'B'	'F'
35	'B'	'M'
44	'B'	'F'
34	'S'	'F'
32	'S'	'M'
41	'B'	'F'
46	'B'	'M'
48	'S'	'M'
46	'B'	'F'
32	'S'	'M'
45	'S'	'F'
42	'B'	'F'
47	'B'	'M'
47	'B'	'F'
48	'S'	'F'
43	'B'	'M'
38	'S'	'M'
45	'S'	'M'
47	'S'	'M'
46	'S'	'F'
46	'S'	'M'
41	'S'	'F'
34	'B'	'F'
32	'B'	'M'
47	'B'	'M'
43	'S'	'F'
49	'S'	'F'
50	'B'	'F'
42	'B'	'F'
43	'S'	'M'
48	'S'	'F'
39	'S'	'M'
42	'B'	'M'
43	'S'	'F'
37	'S'	'M'
35	'B'	'M'
39	'B'	'M'
41	'S'	'M'
42	'B'	'M'
46	'S'	'M'
46	'S'	'M'
49	'S'	'M'
33	'B'	'M'
38	'S'	'F'
46	'S'	'M'
39	'S'	'M'
47	'S'	'F'
43	'S'	'F'
47	'S'	'M'
48	'S'	'F'
38	'S'	'F'
38	'S'	'F'
29	'S'	'M'
43	'S'	'M'
36	'S'	'M'
43	'S'	'M'
49	'S'	'F'
46	'S'	'M'
38	'S'	'M'
41	'B'	'F'
48	'S'	'M'
44	'S'	'F'
32	'S'	'M'
42	'S'	'F'
42	'S'	'M'
45	'S'	'M'
40	'S'	'F'
46	'S'	'M'
47	'B'	'F'
40	'S'	'F'
58	'S'	'M'
35	'S'	'M'
39	'S'	'F'
42	'S'	'F'
44	'S'	'F'
48	'S'	'F'
48	'S'	'F'
48	'S'	'F'
50	'S'	'F'
39	'S'	'M'
48	'S'	'M'
36	'S'	'M'
29	'B'	'M'
40	'S'	'F'
23	'S'	'M'
53	'B'	'F'
20	'B'	'M'
27	'B'	'F'
28	'B'	'F'
50	'S'	'F'
46	'S'	'M'
36	'S'	'M'
51	'S'	'M'
48	'B'	'M'
17	'S'	'F'
39	'S'	'M'
47	'S'	'F'
36	'S'	'M'
31	'S'	'M'
30	'B'	'F'
34	'S'	'M'
34	'S'	'M'
36	'S'	'M'
47	'B'	'M'
52	'S'	'M'
49	'S'	'M'
46	'B'	'M'
43	'S'	'M'
49	'S'	'F'
48	'S'	'M'
43	'S'	'M'
52	'S'	'M'
46	'S'	'F'
54	'S'	'M'
49	'S'	'M'
48	'S'	'M'
33	'S'	'F'
51	'S'	'F'
49	'S'	'F'
45	'S'	'F'
46	'S'	'M'
39	'S'	'F'
44	'S'	'M'
47	'S'	'M'
51	'S'	'M'
42	'S'	'M'
47	'S'	'F'
38	'S'	'F'
48	'S'	'M'
48	'S'	'F'
44	'S'	'M'
49	'S'	'M'
53	'S'	'M'
39	'S'	'F'
43	'S'	'F'
39	'S'	'M'
44	'S'	'M'
34	'S'	'F'
50	'B'	'M'
47	'S'	'M'
43	'S'	'F'
41	'S'	'F'
44	'S'	'F'
40	'S'	'F'
36	'S'	'F'
46	'S'	'M'
48	'S'	'M'
54	'S'	'M'
46	'S'	'M'
44	'B'	'F'
33	'S'	'F'
45	'S'	'F'
44	'S'	'M'
46	'S'	'M'
38	'S'	'M'
39	'S'	'M'
42	'S'	'M'
44	'S'	'M'
37	'S'	'F'
51	'S'	'F'
55	'S'	'F'
41	'S'	'F'
41	'S'	'F'
46	'S'	'F'
43	'S'	'M'
34	'B'	'F'
39	'S'	'F'
48	'S'	'M'
51	'B'	'M'
39	'B'	'F'
37	'S'	'F'
39	'S'	'F'
39	'S'	'F'
45	'S'	'F'
42	'S'	'F'
42	'S'	'F'
46	'S'	'M'
47	'S'	'M'
57	'S'	'M'
42	'B'	'F'
25	'B'	'M'
31	'B'	'M'
33	'B'	'M'
36	'B'	'M'
34	'B'	'F'
38	'B'	'F'
48	'S'	'F'
48	'S'	'F'
40	'B'	'F'
51	'S'	'M'
53	'S'	'M'
50	'S'	'M'
50	'S'	'M'
42	'S'	'M'
33	'S'	'F'
45	'S'	'M'
51	'S'	'M'
46	'S'	'F'
46	'S'	'M'
34	'S'	'M'
45	'S'	'M'
40	'S'	'F'
43	'S'	'M'
46	'S'	'F'
46	'S'	'M'
43	'S'	'F'
39	'S'	'F'
43	'S'	'M'
50	'S'	'F'
48	'S'	'M'
55	'S'	'M'
49	'S'	'F'
43	'B'	'F'
52	'S'	'M'
49	'S'	'F'
46	'B'	'M'
50	'B'	'M'
43	'S'	'F'
53	'S'	'M'
39	'S'	'F'
55	'S'	'M'
31	'S'	'F'
36	'S'	'F'
47	'S'	'M'
46	'S'	'M'
53	'S'	'M'
38	'B'	'F'
39	'S'	'F'
43	'S'	'F'
44	'S'	'F'
39	'S'	'F'
46	'S'	'M'
54	'S'	'F'
47	'B'	'M'
55	'S'	'F'
52	'B'	'M'
42	'S'	'F'
40	'B'	'M'
45	'S'	'F'
40	'S'	'M'
35	'B'	'F'
44	'S'	'F'
39	'B'	'F'
45	'B'	'F'
46	'B'	'M'
35	'B'	'M'
50	'S'	'F'
39	'B'	'F'
46	'B'	'M'
40	'B'	'M'
51	'B'	'F'
33	'B'	'F'
32	'B'	'F'
40	'S'	'F'
42	'B'	'M'
48	'B'	'M'
49	'B'	'F'
44	'B'	'M'
41	'S'	'F'
34	'B'	'M'
46	'B'	'F'
30	'B'	'M'
50	'S'	'M'
47	'S'	'M'
42	'S'	'F'
40	'B'	'M'
44	'B'	'M'
43	'S'	'M'
41	'S'	'F'
43	'B'	'M'
50	'S'	'F'
48	'B'	'M'
43	'B'	'F'
42	'S'	'M'
50	'B'	'F'
58	'S'	'M'
44	'S'	'M'
34	'B'	'M'
48	'B'	'M'
42	'S'	'M'
50	'S'	'M'
40	'S'	'F'
50	'B'	'F'
47	'S'	'F'
53	'S'	'M'
49	'B'	'F'
45	'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=309655&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=309655&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309655&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
means41.221.3621.644-0.205

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 41.22 & 1.362 & 1.644 & -0.205 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309655&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]41.22[/C][C]1.362[/C][C]1.644[/C][C]-0.205[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309655&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309655&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
means41.221.3621.644-0.205







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A1178.302178.3024.5630.033
Treatment_B1254.599254.5996.5160.011
Treatment_A:Treatment_B11.1121.1120.0280.866
Residuals44217270.68939.074

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 178.302 & 178.302 & 4.563 & 0.033 \tabularnewline
Treatment_B & 1 & 254.599 & 254.599 & 6.516 & 0.011 \tabularnewline
Treatment_A:Treatment_B & 1 & 1.112 & 1.112 & 0.028 & 0.866 \tabularnewline
Residuals & 442 & 17270.689 & 39.074 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309655&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]178.302[/C][C]178.302[/C][C]4.563[/C][C]0.033[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]254.599[/C][C]254.599[/C][C]6.516[/C][C]0.011[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]1.112[/C][C]1.112[/C][C]0.028[/C][C]0.866[/C][/ROW]
[ROW][C]Residuals[/C][C]442[/C][C]17270.689[/C][C]39.074[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309655&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309655&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_A1178.302178.3024.5630.033
Treatment_B1254.599254.5996.5160.011
Treatment_A:Treatment_B11.1121.1120.0280.866
Residuals44217270.68939.074







Tukey Honest Significant Difference Comparisons
difflwruprp adj
S-B1.2920.1032.4810.033
M-F1.5190.3492.690.011
S:F-B:F1.362-0.963.6830.431
B:M-B:F1.644-0.7864.0740.302
S:M-B:F2.80.5915.0090.006
B:M-S:F0.282-1.9442.5080.988
S:M-S:F1.439-0.5443.4210.242
S:M-B:M1.157-0.9523.2650.491

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
S-B & 1.292 & 0.103 & 2.481 & 0.033 \tabularnewline
M-F & 1.519 & 0.349 & 2.69 & 0.011 \tabularnewline
S:F-B:F & 1.362 & -0.96 & 3.683 & 0.431 \tabularnewline
B:M-B:F & 1.644 & -0.786 & 4.074 & 0.302 \tabularnewline
S:M-B:F & 2.8 & 0.591 & 5.009 & 0.006 \tabularnewline
B:M-S:F & 0.282 & -1.944 & 2.508 & 0.988 \tabularnewline
S:M-S:F & 1.439 & -0.544 & 3.421 & 0.242 \tabularnewline
S:M-B:M & 1.157 & -0.952 & 3.265 & 0.491 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309655&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]1.292[/C][C]0.103[/C][C]2.481[/C][C]0.033[/C][/ROW]
[ROW][C]M-F[/C][C]1.519[/C][C]0.349[/C][C]2.69[/C][C]0.011[/C][/ROW]
[ROW][C]S:F-B:F[/C][C]1.362[/C][C]-0.96[/C][C]3.683[/C][C]0.431[/C][/ROW]
[ROW][C]B:M-B:F[/C][C]1.644[/C][C]-0.786[/C][C]4.074[/C][C]0.302[/C][/ROW]
[ROW][C]S:M-B:F[/C][C]2.8[/C][C]0.591[/C][C]5.009[/C][C]0.006[/C][/ROW]
[ROW][C]B:M-S:F[/C][C]0.282[/C][C]-1.944[/C][C]2.508[/C][C]0.988[/C][/ROW]
[ROW][C]S:M-S:F[/C][C]1.439[/C][C]-0.544[/C][C]3.421[/C][C]0.242[/C][/ROW]
[ROW][C]S:M-B:M[/C][C]1.157[/C][C]-0.952[/C][C]3.265[/C][C]0.491[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309655&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309655&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-B1.2920.1032.4810.033
M-F1.5190.3492.690.011
S:F-B:F1.362-0.963.6830.431
B:M-B:F1.644-0.7864.0740.302
S:M-B:F2.80.5915.0090.006
B:M-S:F0.282-1.9442.5080.988
S:M-S:F1.439-0.5443.4210.242
S:M-B:M1.157-0.9523.2650.491







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group31.0720.361
442

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 3 & 1.072 & 0.361 \tabularnewline
  & 442 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=309655&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]1.072[/C][C]0.361[/C][/ROW]
[ROW][C] [/C][C]442[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=309655&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=309655&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)
Group31.0720.361
442



Parameters (Session):
par1 = TRUE ;
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')