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Description of Statistical Computation

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 computationTue, 02 Nov 2010 14:42:14 +0000
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2010/Nov/02/t1288708918q70bwprmfyu7j1p.htm/, Retrieved Fri, 09 May 2025 10:09:29 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=91582, Retrieved Fri, 09 May 2025 10:09:29 +0000
QR Codes:

Original text written by user:
IsPrivate?This computation is/was private until 2010-11-03
User-defined keywords
Estimated Impact1330

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [] [2010-11-02 14:42:14] [d76b387543b13b5e3afd8ff9e5fdc89f] [Current]
-   PD    [Two-Way ANOVA] [W5-correction 8] [2010-11-07 14:12:04] [48146708a479232c43a8f6e52fbf83b4]
- R P     [Two-Way ANOVA] [] [2010-11-08 09:32:00] [22937c5b58c14f6c22964f32d64ff823]
- RMPD    [Chi-Squared and McNemar Tests] [Workshop 6 - deel...] [2010-11-14 13:33:34] [4eaa304e6a28c475ba490fccf4c01ad3]
-   P     [Two-Way ANOVA] [] [2010-11-17 15:25:17] [29e492448d11757ae0fad5ef6e7f8e86]
F   P       [Two-Way ANOVA] [Workshop 2 - Vraag 8] [2010-11-18 12:27:52] [91de8b765895d6ee0c73f0d2e284be17]
-   PD        [Two-Way ANOVA] [Workshop 5 - Ques...] [2010-11-18 16:11:47] [a48e3f697f1471e9c9650f8bf805cc06]
- R P     [Two-Way ANOVA] [] [2010-11-18 16:22:55] [69c775ce4d55db2aa75a88e773e8d700]
- R PD    [Two-Way ANOVA] [2 way anova Treat...] [2010-11-21 21:01:40] [97ad38b1c3b35a5feca8b85f7bc7b3ff]
-   P     [Two-Way ANOVA] [Vraag 8] [2010-11-22 20:52:27] [4f1a20f787b3465111b61213cdeef1a9]
-   P     [Two-Way ANOVA] [Vraag 8] [2010-11-22 20:52:27] [4f1a20f787b3465111b61213cdeef1a9]
-   P     [Two-Way ANOVA] [] [2010-11-23 13:35:21] [f47feae0308dca73181bb669fbad1c56]
-   PD    [Two-Way ANOVA] [Two-Way ANOVA: Po...] [2010-12-11 19:16:51] [26379b86c25fbf0febe6a7a428e65173]
-   P     [Two-Way ANOVA] [question 8] [2011-10-23 10:20:46] [27e29806e0b1d1351a97bc4ee4116294]
- R P     [Two-Way ANOVA] [2-way ANOVA (expe...] [2011-10-28 01:39:42] [7f54ec67e5798cc59f49446b41e2f221]
-   PD    [Two-Way ANOVA] [WS 5 Q 8] [2011-10-29 15:13:10] [a9671b130b33f9fcb98554992ce4582f]
-   PD    [Two-Way ANOVA] [vraag 8] [2011-10-30 14:00:47] [c26e829f03d42da3805b9c4b60f90f25]
- RMPD      [Histogram] [Histogram] [2011-11-11 15:56:27] [c26e829f03d42da3805b9c4b60f90f25]
- RMPD      [Kernel Density Estimation] [] [2011-11-11 15:58:58] [c26e829f03d42da3805b9c4b60f90f25]
-   P     [Two-Way ANOVA] [Workshop 5 - Task 8] [2011-10-30 18:53:53] [3deae35ae8526e36953f595ad65f3a1f]
-   P     [Two-Way ANOVA] [] [2011-11-02 10:06:32] [ee8c3a74bf3b349877806e9a50913c60]
-   P     [Two-Way ANOVA] [] [2011-11-02 14:40:01] [2d3d135c7070430a7cc2b1c9a86f42b1]
-   PD    [Two-Way ANOVA] [Workshop 5 - Ques...] [2011-11-02 16:53:41] [a9a952c1cbc7081c25fad93a34aab827]
- R P       [Two-Way ANOVA] [Paper deel 1: 2-w...] [2011-12-18 19:31:00] [a9a952c1cbc7081c25fad93a34aab827]
- RMP         [Two-Way ANOVA] [Paper deel 1: 2-w...] [2011-12-18 22:43:51] [1321c14511baa35aebbc5dda661708fe]
-   P     [Two-Way ANOVA] [] [2011-11-02 17:26:47] [ec2187f7727da5d5d939740b21b8b68a]
-   P     [Two-Way ANOVA] [] [2011-11-03 17:47:11] [86f7284edee3dbb8ea5c7e2dec87d892]
-   P     [Two-Way ANOVA] [] [2011-11-05 16:14:49] [1dc3906a3b5a6ec06dc921f387100c9e]
-   P     [Two-Way ANOVA] [] [2011-11-06 16:40:36] [b4c8fd31b0af00c33711722ddf8d2c4c]
-   P     [Two-Way ANOVA] [task 5.8] [2011-11-07 19:34:03] [d1ce18d003fa52f731d1c3ce8b58d5f9]
-   P     [Two-Way ANOVA] [] [2011-11-07 19:50:24] [77e355412ccdb651b3c7eae41c3da865]
- R P       [Two-Way ANOVA] [] [2011-11-08 07:58:43] [aefb5c2d4042694c5b6b82f93ac1885a]
- R P     [Two-Way ANOVA] [C5 vr 8] [2011-11-07 21:40:42] [74be16979710d4c4e7c6647856088456]
-   P     [Two-Way ANOVA] [Workshop 5 - Task 8] [2011-11-08 08:46:24] [3deae35ae8526e36953f595ad65f3a1f]
-   P     [Two-Way ANOVA] [] [2011-11-08 20:02:45] [a1957df0bc37aec4aa3c994e6a08412c]
- R P     [Two-Way ANOVA] [] [2011-11-08 20:10:41] [19d77e37efa419fdc040c74a96874aff]
- RMP     [Two-Way ANOVA] [WS5-Q8] [2011-11-08 20:30:13] [74be16979710d4c4e7c6647856088456]
-   P     [Two-Way ANOVA] [] [2011-11-09 13:34:56] [d0c153a232569da05656a074c1bdec10]
- R P     [Two-Way ANOVA] [PAPER] [2011-12-10 14:18:46] [27e29806e0b1d1351a97bc4ee4116294]
- R P     [Two-Way ANOVA] [] [2011-12-10 19:43:18] [80bca13c5f9401fbb753952fd2952f4a]
- R PD    [Two-Way ANOVA] [] [2011-12-11 08:35:33] [80bca13c5f9401fbb753952fd2952f4a]
- RM        [Two-Way ANOVA] [] [2011-12-11 08:36:38] [80bca13c5f9401fbb753952fd2952f4a]
- R P     [Two-Way ANOVA] [] [2011-12-15 19:48:39] [74be16979710d4c4e7c6647856088456]
- R P     [Two-Way ANOVA] [Paper: two-way a...] [2011-12-16 22:06:48] [59720598bc822822e6b5d0c431ef1d7f]
- R P     [Two-Way ANOVA] [] [2011-12-17 16:05:11] [b4c8fd31b0af00c33711722ddf8d2c4c]
- R P     [Two-Way ANOVA] [Paper] [2011-12-18 11:06:50] [aa6b3f8e5b050429abaad141c7204e84]
- R P     [Two-Way ANOVA] [Paper] [2011-12-18 11:06:50] [aa6b3f8e5b050429abaad141c7204e84]
- R P     [Two-Way ANOVA] [Paper] [2011-12-18 11:06:50] [aa6b3f8e5b050429abaad141c7204e84]
- R P     [Two-Way ANOVA] [Paper] [2011-12-18 11:06:50] [aa6b3f8e5b050429abaad141c7204e84]

[Truncated]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
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Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input & view raw input (R code)  \tabularnewline
Raw Output & view raw output of R engine  \tabularnewline
Computing time & 2 seconds \tabularnewline
R Server & 'Gwilym Jenkins' @ 72.249.127.135 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91582&T=0

[TABLE]
[ROW][C]Summary of computational transaction[/C][/ROW]
[ROW][C]Raw Input[/C][C]view raw input (R code) [/C][/ROW]
[ROW][C]Raw Output[/C][C]view raw output of R engine [/C][/ROW]
[ROW][C]Computing time[/C][C]2 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Gwilym Jenkins' @ 72.249.127.135[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91582&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91582&T=0

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Gwilym Jenkins' @ 72.249.127.135






ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.4710.059-0.471-0.1710.1190.209

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.471 & 0.059 & -0.471 & -0.171 & 0.119 & 0.209 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91582&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.471[/C][C]0.059[/C][C]-0.471[/C][C]-0.171[/C][C]0.119[/C][C]0.209[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91582&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91582&T=1

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.4710.059-0.471-0.1710.1190.209






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A24.8522.42612.6010
Treatment_B20.1050.1050.5460.462
Treatment_A:Treatment_B20.2010.1010.5230.594
Residuals11121.3710.193 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 4.852 & 2.426 & 12.601 & 0 \tabularnewline
Treatment_B & 2 & 0.105 & 0.105 & 0.546 & 0.462 \tabularnewline
Treatment_A:Treatment_B & 2 & 0.201 & 0.101 & 0.523 & 0.594 \tabularnewline
Residuals & 111 & 21.371 & 0.193 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91582&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]2[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]2[/C][C]4.852[/C][C]2.426[/C][C]12.601[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]0.105[/C][C]0.105[/C][C]0.546[/C][C]0.462[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]0.201[/C][C]0.101[/C][C]0.523[/C][C]0.594[/C][/ROW]
[ROW][C]Residuals[/C][C]111[/C][C]21.371[/C][C]0.193[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91582&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91582&T=2

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A24.8522.42612.6010
Treatment_B20.1050.1050.5460.462
Treatment_A:Treatment_B20.2010.1010.5230.594
Residuals11121.3710.193






Tukey Honest Significant Difference Comparisons
difflwruprp adj
F-E0.122-0.1160.3590.447
H-E-0.353-0.591-0.1160.002
H-F-0.475-0.708-0.2420
1-0-0.061-0.2240.1030.463
F:0-E:00.059-0.3780.4950.999
H:0-E:0-0.471-0.93-0.0110.041
E:1-E:0-0.171-0.590.2490.846
F:1-E:00.008-0.3990.4151
H:1-E:0-0.432-0.829-0.0350.024
H:0-F:0-0.529-0.989-0.070.014
E:1-F:0-0.229-0.6490.190.61
F:1-F:0-0.051-0.4580.3560.999
H:1-F:0-0.491-0.888-0.0940.006
E:1-H:00.3-0.1430.7430.371
F:1-H:00.4780.0470.910.021
H:1-H:00.038-0.3830.461
F:1-E:10.178-0.2110.5670.768
H:1-E:1-0.262-0.640.1170.347
H:1-F:1-0.44-0.804-0.0760.009

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
F-E & 0.122 & -0.116 & 0.359 & 0.447 \tabularnewline
H-E & -0.353 & -0.591 & -0.116 & 0.002 \tabularnewline
H-F & -0.475 & -0.708 & -0.242 & 0 \tabularnewline
1-0 & -0.061 & -0.224 & 0.103 & 0.463 \tabularnewline
F:0-E:0 & 0.059 & -0.378 & 0.495 & 0.999 \tabularnewline
H:0-E:0 & -0.471 & -0.93 & -0.011 & 0.041 \tabularnewline
E:1-E:0 & -0.171 & -0.59 & 0.249 & 0.846 \tabularnewline
F:1-E:0 & 0.008 & -0.399 & 0.415 & 1 \tabularnewline
H:1-E:0 & -0.432 & -0.829 & -0.035 & 0.024 \tabularnewline
H:0-F:0 & -0.529 & -0.989 & -0.07 & 0.014 \tabularnewline
E:1-F:0 & -0.229 & -0.649 & 0.19 & 0.61 \tabularnewline
F:1-F:0 & -0.051 & -0.458 & 0.356 & 0.999 \tabularnewline
H:1-F:0 & -0.491 & -0.888 & -0.094 & 0.006 \tabularnewline
E:1-H:0 & 0.3 & -0.143 & 0.743 & 0.371 \tabularnewline
F:1-H:0 & 0.478 & 0.047 & 0.91 & 0.021 \tabularnewline
H:1-H:0 & 0.038 & -0.383 & 0.46 & 1 \tabularnewline
F:1-E:1 & 0.178 & -0.211 & 0.567 & 0.768 \tabularnewline
H:1-E:1 & -0.262 & -0.64 & 0.117 & 0.347 \tabularnewline
H:1-F:1 & -0.44 & -0.804 & -0.076 & 0.009 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91582&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]F-E[/C][C]0.122[/C][C]-0.116[/C][C]0.359[/C][C]0.447[/C][/ROW]
[ROW][C]H-E[/C][C]-0.353[/C][C]-0.591[/C][C]-0.116[/C][C]0.002[/C][/ROW]
[ROW][C]H-F[/C][C]-0.475[/C][C]-0.708[/C][C]-0.242[/C][C]0[/C][/ROW]
[ROW][C]1-0[/C][C]-0.061[/C][C]-0.224[/C][C]0.103[/C][C]0.463[/C][/ROW]
[ROW][C]F:0-E:0[/C][C]0.059[/C][C]-0.378[/C][C]0.495[/C][C]0.999[/C][/ROW]
[ROW][C]H:0-E:0[/C][C]-0.471[/C][C]-0.93[/C][C]-0.011[/C][C]0.041[/C][/ROW]
[ROW][C]E:1-E:0[/C][C]-0.171[/C][C]-0.59[/C][C]0.249[/C][C]0.846[/C][/ROW]
[ROW][C]F:1-E:0[/C][C]0.008[/C][C]-0.399[/C][C]0.415[/C][C]1[/C][/ROW]
[ROW][C]H:1-E:0[/C][C]-0.432[/C][C]-0.829[/C][C]-0.035[/C][C]0.024[/C][/ROW]
[ROW][C]H:0-F:0[/C][C]-0.529[/C][C]-0.989[/C][C]-0.07[/C][C]0.014[/C][/ROW]
[ROW][C]E:1-F:0[/C][C]-0.229[/C][C]-0.649[/C][C]0.19[/C][C]0.61[/C][/ROW]
[ROW][C]F:1-F:0[/C][C]-0.051[/C][C]-0.458[/C][C]0.356[/C][C]0.999[/C][/ROW]
[ROW][C]H:1-F:0[/C][C]-0.491[/C][C]-0.888[/C][C]-0.094[/C][C]0.006[/C][/ROW]
[ROW][C]E:1-H:0[/C][C]0.3[/C][C]-0.143[/C][C]0.743[/C][C]0.371[/C][/ROW]
[ROW][C]F:1-H:0[/C][C]0.478[/C][C]0.047[/C][C]0.91[/C][C]0.021[/C][/ROW]
[ROW][C]H:1-H:0[/C][C]0.038[/C][C]-0.383[/C][C]0.46[/C][C]1[/C][/ROW]
[ROW][C]F:1-E:1[/C][C]0.178[/C][C]-0.211[/C][C]0.567[/C][C]0.768[/C][/ROW]
[ROW][C]H:1-E:1[/C][C]-0.262[/C][C]-0.64[/C][C]0.117[/C][C]0.347[/C][/ROW]
[ROW][C]H:1-F:1[/C][C]-0.44[/C][C]-0.804[/C][C]-0.076[/C][C]0.009[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91582&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91582&T=3

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Tukey Honest Significant Difference Comparisons
difflwruprp adj
F-E0.122-0.1160.3590.447
H-E-0.353-0.591-0.1160.002
H-F-0.475-0.708-0.2420
1-0-0.061-0.2240.1030.463
F:0-E:00.059-0.3780.4950.999
H:0-E:0-0.471-0.93-0.0110.041
E:1-E:0-0.171-0.590.2490.846
F:1-E:00.008-0.3990.4151
H:1-E:0-0.432-0.829-0.0350.024
H:0-F:0-0.529-0.989-0.070.014
E:1-F:0-0.229-0.6490.190.61
F:1-F:0-0.051-0.4580.3560.999
H:1-F:0-0.491-0.888-0.0940.006
E:1-H:00.3-0.1430.7430.371
F:1-H:00.4780.0470.910.021
H:1-H:00.038-0.3830.461
F:1-E:10.178-0.2110.5670.768
H:1-E:1-0.262-0.640.1170.347
H:1-F:1-0.44-0.804-0.0760.009






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group55.5040
111 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 5.504 & 0 \tabularnewline
  & 111 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=91582&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]5[/C][C]5.504[/C][C]0[/C][/ROW]
[ROW][C] [/C][C]111[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=91582&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=91582&T=4

As an alternative you can also use a QR Code:  
QR Code


The GUIDs for individual cells are displayed in the table below:

Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group55.5040
111


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 8 ; par2 = 11 ; par3 = TRUE ;
Parameters (R input):
par1 = 4 ; par2 = 3 ; par3 = 5 ; 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')