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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 computationWed, 29 Oct 2014 23:23:43 +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/2014/Oct/29/t1414625047cuy7f8qrclkl4hd.htm/, Retrieved Sun, 12 May 2024 19:23:19 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=249344, Retrieved Sun, 12 May 2024 19:23:19 +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)
-     [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-01 13:37:53] [b98453cac15ba1066b407e146608df68]
- RMP   [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2014-10-21 08:05:52] [32b17a345b130fdf5cc88718ed94a974]
- RMPD      [Two-Way ANOVA] [two way ANOVA ] [2014-10-29 23:23:43] [8aa9b0b9e9cdf95f84c1d02ac9593640] [Current]
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Dataseries X:
'WWE'	0	0	0	0	0	0	1
'WWE'	0	0	0	0	0	0	0
'WWE'	0	1	1	1	1	2	1
'WWE'	0	0	0	0	0	0	1
'WWE'	0	1	1	1	1	2	1
'WWE'	0	0	1	0	1	1	1
'WWE'	0	0	0	0	0	0	1
'WWE'	0	1	1	1	1	2	1
'WWE'	0	0	0	0	0	0	1
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	0	0	0	0	1
'WWE'	0	0	NA	0	NA	NA	0
'WWE'	0	0	1	0	1	1	0
'WWE'	1	1	NA	0	NA	NA	0
'WWE'	1	0	0	-1	-1	-1	1
'WWE'	0	0	0	0	0	0	1
'WWE'	0	0	1	0	1	1	0
'WWE'	0	1	0	1	0	1	0
'WWE'	0	0	0	0	0	0	0
'WWE'	1	1	0	0	-1	0	1
'WWE'	0	0	0	0	0	0	0
'WWE'	0	1	0	1	0	1	0
'WWE'	0	1	1	1	1	2	0
'WWE'	0	1	1	1	1	2	0
'WWE'	0	0	0	0	0	0	0
'WWE'	1	1	0	0	-1	0	1
'WWE'	0	1	0	1	0	1	0
'WWE'	0	1	0	1	0	1	0
'WWE'	0	0	1	0	1	1	1
'WWE'	0	1	0	1	0	1	1
'WWE'	0	1	0	1	0	1	1
'WWE'	0	0	0	0	0	0	1
'WWE'	0	0	0	0	0	0	1
'WWE'	1	1	0	0	-1	0	1
'WWE'	1	1	0	0	-1	0	0
'WWE'	0	0	0	0	0	0	0
'WWE'	0	0	NA	0	NA	NA	0
'WWE'	0	0	1	0	1	1	0
'WWE'	0	0	0	0	0	0	0
'CSWE'	0	0	0	0	0	0	0
'CSWE'	1	0	NA	-1	NA	NA	0
'CSWE'	0	0	0	0	0	0	1
'CSWE'	0	1	NA	1	NA	NA	0
'CSWE'	0	1	0	1	0	1	0
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	0	0	0	0	1
'CSWE'	0	0	1	0	1	1	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	1	1	0	0	-1	0	0
'CSWE'	0	0	1	0	1	1	1
'CSWE'	0	1	0	1	0	1	1
'CSWE'	0	0	0	0	0	0	1
'CSWE'	0	1	NA	1	NA	NA	1
'CSWE'	0	1	0	1	0	1	1
'CSWE'	0	1	NA	1	NA	NA	1
'CSWE'	1	1	0	0	-1	0	1
'CSWE'	0	1	0	1	0	1	0
'CSWE'	0	0	NA	0	NA	NA	0
'CSWE'	0	1	0	1	0	1	1
'CSWE'	0	0	0	0	0	0	1
'CSWE'	0	0	NA	0	NA	NA	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	1	0	1	0	1	1
'CSWE'	0	0	0	0	0	0	1
'CSWE'	0	0	1	0	1	1	0
'CSWE'	0	1	0	1	0	1	1
'CSWE'	0	1	0	1	0	1	0
'CSWE'	1	1	0	0	-1	0	1
'CSWE'	0	0	1	0	1	1	1
'CSWE'	0	1	1	1	1	2	0
'CSWE'	0	1	0	1	0	1	1
'CSWE'	0	0	NA	0	NA	NA	1
'CSWE'	0	0	0	0	0	0	1
'CSWE'	0	1	NA	1	NA	NA	0
'CSWE'	0	0	0	0	0	0	1
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	0	0	0	0	0	0
'CSWE'	0	1	0	1	0	1	0
'CSWE'	0	1	0	1	0	1	1
'C'	0	0	0	0	0	0	1
'C'	0	0	1	0	1	1	1
'C'	1	0	0	-1	-1	-1	1
'C'	0	0	1	0	1	1	1
'C'	0	0	NA	0	NA	NA	1
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	1
'C'	0	1	0	1	0	1	1
'C'	1	1	0	0	-1	0	1
'C'	0	0	NA	0	NA	NA	1
'C'	0	0	0	0	0	0	0
'C'	0	1	0	1	0	1	1
'C'	0	1	0	1	0	1	1
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	1
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	1
'C'	0	0	NA	0	NA	NA	1
'C'	0	0	0	0	0	0	1
'C'	0	1	0	1	0	1	1
'C'	1	1	0	0	-1	0	1
'C'	0	1	0	1	0	1	0
'C'	0	0	0	0	0	0	1
'C'	0	0	0	0	0	0	1
'C'	1	1	0	0	-1	0	0
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	1
'C'	0	0	0	0	0	0	1
'C'	0	0	0	0	0	0	1
'C'	0	0	0	0	0	0	1
'C'	1	1	0	0	-1	0	0
'C'	0	0	1	0	1	1	1
'C'	0	0	0	0	0	0	1
'C'	0	0	0	0	0	0	0
'C'	0	0	0	0	0	0	1
'C'	0	0	1	0	1	1	1




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.

\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 & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Framework error message & 
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=249344&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]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Framework error message[/C][C]
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=249344&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=249344&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 Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time2 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Framework error message
The field 'Names of X columns' contains a hard return which cannot be interpreted.
Please, resubmit your request without hard returns in the 'Names of X columns'.







ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.1110.2420.16200.038-0.062

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.111 & 0.242 & 0.162 & 0 & 0.038 & -0.062 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=249344&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.111[/C][C]0.242[/C][C]0.162[/C][C]0[/C][C]0.038[/C][C]-0.062[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=249344&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=249344&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
means0.1110.2420.16200.038-0.062







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
2
Treatment_A21.320.662.7720.067
Treatment_B20.0030.0030.0110.918
Treatment_A:Treatment_B20.0510.0260.1080.898
Residuals11126.4380.238

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 2 &  &  &  &  \tabularnewline
Treatment_A & 2 & 1.32 & 0.66 & 2.772 & 0.067 \tabularnewline
Treatment_B & 2 & 0.003 & 0.003 & 0.011 & 0.918 \tabularnewline
Treatment_A:Treatment_B & 2 & 0.051 & 0.026 & 0.108 & 0.898 \tabularnewline
Residuals & 111 & 26.438 & 0.238 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=249344&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]1.32[/C][C]0.66[/C][C]2.772[/C][C]0.067[/C][/ROW]
[ROW][C]Treatment_B[/C][C]2[/C][C]0.003[/C][C]0.003[/C][C]0.011[/C][C]0.918[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]2[/C][C]0.051[/C][C]0.026[/C][C]0.108[/C][C]0.898[/C][/ROW]
[ROW][C]Residuals[/C][C]111[/C][C]26.438[/C][C]0.238[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=249344&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=249344&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)
2
Treatment_A21.320.662.7720.067
Treatment_B20.0030.0030.0110.918
Treatment_A:Treatment_B20.0510.0260.1080.898
Residuals11126.4380.238







Tukey Honest Significant Difference Comparisons
difflwruprp adj
CSWE-C0.264-0.0020.530.053
WWE-C0.133-0.1320.3980.461
WWE-CSWE-0.131-0.3890.1270.451
1-0-0.009-0.1910.1730.921
CSWE:0-C:00.242-0.3420.8250.835
WWE:0-C:00.162-0.3980.7220.96
C:1-C:00-0.5450.5451
CSWE:1-C:00.28-0.2760.8370.69
WWE:1-C:00.099-0.4730.6720.996
WWE:0-CSWE:0-0.08-0.5370.3770.996
C:1-CSWE:0-0.242-0.680.1960.6
CSWE:1-CSWE:00.038-0.4140.4911
WWE:1-CSWE:0-0.142-0.6150.330.952
C:1-WWE:0-0.162-0.5680.2450.858
CSWE:1-WWE:00.119-0.3040.5410.964
WWE:1-WWE:0-0.062-0.5050.3810.999
CSWE:1-C:10.28-0.1210.6820.336
WWE:1-C:10.099-0.3240.5230.984
WWE:1-CSWE:1-0.181-0.620.2580.838

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
CSWE-C & 0.264 & -0.002 & 0.53 & 0.053 \tabularnewline
WWE-C & 0.133 & -0.132 & 0.398 & 0.461 \tabularnewline
WWE-CSWE & -0.131 & -0.389 & 0.127 & 0.451 \tabularnewline
1-0 & -0.009 & -0.191 & 0.173 & 0.921 \tabularnewline
CSWE:0-C:0 & 0.242 & -0.342 & 0.825 & 0.835 \tabularnewline
WWE:0-C:0 & 0.162 & -0.398 & 0.722 & 0.96 \tabularnewline
C:1-C:0 & 0 & -0.545 & 0.545 & 1 \tabularnewline
CSWE:1-C:0 & 0.28 & -0.276 & 0.837 & 0.69 \tabularnewline
WWE:1-C:0 & 0.099 & -0.473 & 0.672 & 0.996 \tabularnewline
WWE:0-CSWE:0 & -0.08 & -0.537 & 0.377 & 0.996 \tabularnewline
C:1-CSWE:0 & -0.242 & -0.68 & 0.196 & 0.6 \tabularnewline
CSWE:1-CSWE:0 & 0.038 & -0.414 & 0.491 & 1 \tabularnewline
WWE:1-CSWE:0 & -0.142 & -0.615 & 0.33 & 0.952 \tabularnewline
C:1-WWE:0 & -0.162 & -0.568 & 0.245 & 0.858 \tabularnewline
CSWE:1-WWE:0 & 0.119 & -0.304 & 0.541 & 0.964 \tabularnewline
WWE:1-WWE:0 & -0.062 & -0.505 & 0.381 & 0.999 \tabularnewline
CSWE:1-C:1 & 0.28 & -0.121 & 0.682 & 0.336 \tabularnewline
WWE:1-C:1 & 0.099 & -0.324 & 0.523 & 0.984 \tabularnewline
WWE:1-CSWE:1 & -0.181 & -0.62 & 0.258 & 0.838 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=249344&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]CSWE-C[/C][C]0.264[/C][C]-0.002[/C][C]0.53[/C][C]0.053[/C][/ROW]
[ROW][C]WWE-C[/C][C]0.133[/C][C]-0.132[/C][C]0.398[/C][C]0.461[/C][/ROW]
[ROW][C]WWE-CSWE[/C][C]-0.131[/C][C]-0.389[/C][C]0.127[/C][C]0.451[/C][/ROW]
[ROW][C]1-0[/C][C]-0.009[/C][C]-0.191[/C][C]0.173[/C][C]0.921[/C][/ROW]
[ROW][C]CSWE:0-C:0[/C][C]0.242[/C][C]-0.342[/C][C]0.825[/C][C]0.835[/C][/ROW]
[ROW][C]WWE:0-C:0[/C][C]0.162[/C][C]-0.398[/C][C]0.722[/C][C]0.96[/C][/ROW]
[ROW][C]C:1-C:0[/C][C]0[/C][C]-0.545[/C][C]0.545[/C][C]1[/C][/ROW]
[ROW][C]CSWE:1-C:0[/C][C]0.28[/C][C]-0.276[/C][C]0.837[/C][C]0.69[/C][/ROW]
[ROW][C]WWE:1-C:0[/C][C]0.099[/C][C]-0.473[/C][C]0.672[/C][C]0.996[/C][/ROW]
[ROW][C]WWE:0-CSWE:0[/C][C]-0.08[/C][C]-0.537[/C][C]0.377[/C][C]0.996[/C][/ROW]
[ROW][C]C:1-CSWE:0[/C][C]-0.242[/C][C]-0.68[/C][C]0.196[/C][C]0.6[/C][/ROW]
[ROW][C]CSWE:1-CSWE:0[/C][C]0.038[/C][C]-0.414[/C][C]0.491[/C][C]1[/C][/ROW]
[ROW][C]WWE:1-CSWE:0[/C][C]-0.142[/C][C]-0.615[/C][C]0.33[/C][C]0.952[/C][/ROW]
[ROW][C]C:1-WWE:0[/C][C]-0.162[/C][C]-0.568[/C][C]0.245[/C][C]0.858[/C][/ROW]
[ROW][C]CSWE:1-WWE:0[/C][C]0.119[/C][C]-0.304[/C][C]0.541[/C][C]0.964[/C][/ROW]
[ROW][C]WWE:1-WWE:0[/C][C]-0.062[/C][C]-0.505[/C][C]0.381[/C][C]0.999[/C][/ROW]
[ROW][C]CSWE:1-C:1[/C][C]0.28[/C][C]-0.121[/C][C]0.682[/C][C]0.336[/C][/ROW]
[ROW][C]WWE:1-C:1[/C][C]0.099[/C][C]-0.324[/C][C]0.523[/C][C]0.984[/C][/ROW]
[ROW][C]WWE:1-CSWE:1[/C][C]-0.181[/C][C]-0.62[/C][C]0.258[/C][C]0.838[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=249344&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=249344&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
CSWE-C0.264-0.0020.530.053
WWE-C0.133-0.1320.3980.461
WWE-CSWE-0.131-0.3890.1270.451
1-0-0.009-0.1910.1730.921
CSWE:0-C:00.242-0.3420.8250.835
WWE:0-C:00.162-0.3980.7220.96
C:1-C:00-0.5450.5451
CSWE:1-C:00.28-0.2760.8370.69
WWE:1-C:00.099-0.4730.6720.996
WWE:0-CSWE:0-0.08-0.5370.3770.996
C:1-CSWE:0-0.242-0.680.1960.6
CSWE:1-CSWE:00.038-0.4140.4911
WWE:1-CSWE:0-0.142-0.6150.330.952
C:1-WWE:0-0.162-0.5680.2450.858
CSWE:1-WWE:00.119-0.3040.5410.964
WWE:1-WWE:0-0.062-0.5050.3810.999
CSWE:1-C:10.28-0.1210.6820.336
WWE:1-C:10.099-0.3240.5230.984
WWE:1-CSWE:1-0.181-0.620.2580.838







Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group51.3280.258
111

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 1.328 & 0.258 \tabularnewline
  & 111 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=249344&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]1.328[/C][C]0.258[/C][/ROW]
[ROW][C] [/C][C]111[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=249344&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=249344&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)
Group51.3280.258
111



Parameters (Session):
Parameters (R input):
par1 = 5 ; par2 = 1 ; par3 = 8 ; 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')