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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, 16 Nov 2010 13:54:37 +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/16/t12899156390kjaaxagtse24ea.htm/, Retrieved Sun, 05 May 2024 07:43:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=95782, Retrieved Sun, 05 May 2024 07:43:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact171

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F     [Paired and Unpaired Two Samples Tests about the Mean] [Workshop 5 - Vraag 1] [2010-11-16 11:52:43] [1df589bc3feb749f1946d8c1ee38b85f]
-    D  [Paired and Unpaired Two Samples Tests about the Mean] [workshop 2 - vraag 2] [2010-11-16 12:11:32] [1df589bc3feb749f1946d8c1ee38b85f]
-    D    [Paired and Unpaired Two Samples Tests about the Mean] [Workshope - vraag 3] [2010-11-16 12:21:10] [1df589bc3feb749f1946d8c1ee38b85f]
-    D      [Paired and Unpaired Two Samples Tests about the Mean] [workshop 2 vraag ...] [2010-11-16 12:41:28] [1df589bc3feb749f1946d8c1ee38b85f]
F    D        [Paired and Unpaired Two Samples Tests about the Mean] [workshop 1 - vraa...] [2010-11-16 12:50:33] [1df589bc3feb749f1946d8c1ee38b85f]
-    D          [Paired and Unpaired Two Samples Tests about the Mean] [workshop 1 - vraa...] [2010-11-16 12:57:05] [1df589bc3feb749f1946d8c1ee38b85f]
F RM D            [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [workshop 2 - vraa...] [2010-11-16 13:07:10] [1df589bc3feb749f1946d8c1ee38b85f]
F                   [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [workshop 2 vraag ...] [2010-11-16 13:29:55] [1df589bc3feb749f1946d8c1ee38b85f]
F    D                [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [workshop 2- vraag...] [2010-11-16 13:36:19] [1df589bc3feb749f1946d8c1ee38b85f]
F                       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [workshop 2 - vraa...] [2010-11-16 13:46:32] [1df589bc3feb749f1946d8c1ee38b85f]
F RM D                      [Two-Way ANOVA] [workshop 2 - ques...] [2010-11-16 13:54:37] [36a5183bc8f6439b2481209b0fbe6bda] [Current]
F   PD                        [Two-Way ANOVA] [Question 8] [2010-11-18 14:51:36] [f1bd7399181c649098ca7b814ee0e027]
-   PD                        [Two-Way ANOVA] [Question 8] [2010-11-18 14:52:18] [caa3ae6143f673f211f626a62938a034]
-   PD                        [Two-Way ANOVA] [Question 8] [2010-11-18 14:54:13] [caa3ae6143f673f211f626a62938a034]
Feedback Forum
2010-11-20 11:12:51 [Hans Tierens] [reply] 
De berekening is volledig correct gemaakt. Het is jammer dat je niet meer conclusies trekt.
Je wist waarschijnlijk nog uit de les dat F de beste treatment was om toe te passen, omdat deze gunstig is voor zowel mannen als vrouwen...

Ik geef je hier nog wat extra conclusies die je had kunnen /moeten trekken:

F-E is niet significant verschillend... we kunnen ze dus niet met zekerheid ranken
H-E is significant verschillend van nul en negatief (E is beter dan H)
H-F is significant verschillend van nul en negatief (F is beter dan H)
0-1 (vrouwen-mannen) is niet significant verschillen van nul. Dit sluit echter geen interactie-effecten uit!!! Het is dus ook leerrijk om te kijken naar interacties, maar dat gaan we niet doen... we zijn op zoek naar de beste treatment om aan een gemengde groep voor te leggen!

F:0-E:0 is niet significant verschillend van nul
H:0-E:0 is significant verschillend van nul en negatief (E werkt beter voor vrouwen dan H)
H:0-F:0 is significant verschillend van nul en negatief (F werkt beter voor vrouwen dan H)

F:1-E:1 is niet significant verschillend van nul
H:1-E:1 is niet significant verschillend van nul
H:1-F:1 is significant verschillend van nul en negatief (F werkt beter voor mannen dan H)

Uit bovenstaande kunnen we besluiten dat F het best werkt voor mannen als vrouwen.

We kunnen ook nog een opmerking maken over E:1-E:0, we zien namelijk in de grafiek 'Possible Interactions Between Anova Groups' dat er een groot verschil is tussen deze twee. Aangezien dit in de tabel niet significant verschilt van nul kunnen we stellen dat het verschil te wijten in aan puur toeval... We kennen de type 2 fout (bèta) niet!!!

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0	0	'E'	0	1
0	1	'F'	1	0
0	0	'F'	0	1
0	0	'H'	0	1
0	0	'H'	0	1
0	0	'H'	0	1
0	1	'E'	1	1
0	1	'F'	1	1
0	0	'E'	0	1
0	1	'F'	1	0
0	0	'H'	0	0
0	0	'E'	0	0
0	1	'F'	1	1
0	0	'H'	0	0
0	1	'E'	1	0
0	0	'H'	0	0
0	0	'E'	0	1
0	0	'F'	0	1
0	0	'H'	0	0
0	1	'F'	1	0
0	0	'H'	0	0
0	0	'H'	0	1
0	0	'H'	0	0
0	0	'E'	0	0
0	1	'F'	1	0
0	1	'E'	1	0
0	1	'E'	1	0
1	1	'F'	0	1
0	0	'F'	0	0
0	0	'H'	0	0
0	0	'E'	0	1
0	1	'E'	1	1
0	0	'H'	0	1
0	1	'E'	1	1
0	1	'F'	1	1
0	0	'E'	0	1
0	1	'F'	1	0
0	0	'H'	0	0
0	1	'E'	1	0
0	1	'F'	1	0
0	1	'F'	1	0
0	0	'F'	0	0
0	1	'F'	1	0
0	1	'H'	1	1
0	1	'E'	1	0
0	0	'E'	0	0
0	0	'H'	0	0
0	1	'E'	1	1
0	0	'F'	0	1
0	0	'F'	0	0
0	0	'H'	0	0
0	0	'E'	0	1
0	1	'F'	1	1
0	1	'E'	1	1
0	0	'H'	0	1
0	0	'H'	0	1
0	0	'H'	0	1
0	0	'E'	0	1
0	0	'H'	0	0
0	1	'E'	1	0
0	0	'H'	0	1
0	0	'F'	0	1
0	0	'H'	0	1
0	1	'F'	1	0
0	0	'E'	0	1
0	1	'E'	1	1
0	0	'F'	0	0
0	0	'H'	0	1
0	0	'F'	0	0
0	0	'E'	0	1
1	0	'E'	-1	1
0	0	'H'	0	0
0	0	'H'	0	1
0	0	'F'	0	1
0	0	'H'	0	1
0	1	'E'	1	0
0	0	'F'	0	1
0	1	'E'	1	0
0	0	'E'	0	0
0	0	'E'	0	0
0	0	'F'	0	1
0	0	'E'	0	1
0	1	'F'	1	1
0	0	'H'	0	1
1	1	'H'	0	1
0	0	'H'	0	1
0	0	'F'	0	0
0	0	'H'	0	1
0	0	'H'	0	1
0	1	'F'	1	1
0	1	'F'	1	1
0	0	'H'	0	0
0	0	'F'	0	1
0	0	'H'	0	1
0	0	'E'	0	0
0	1	'F'	1	1
0	0	'E'	0	0
0	0	'H'	0	1
0	1	'F'	1	1
1	1	'F'	0	1
0	0	'H'	0	1
0	1	'E'	1	1
0	0	'F'	0	0
0	0	'H'	0	1
0	0	'E'	0	1
0	0	'F'	0	0
0	0	'H'	0	0
0	0	'H'	0	1
0	1	'F'	1	1
0	1	'F'	1	1
0	0	'H'	0	1
0	0	'E'	0	0
0	0	'H'	0	1
0	0	'E'	0	1
0	0	'E'	0	0
0	0	'F'	0	1
0	0	'F'	0	1

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
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 & 'Gwilym Jenkins' @ 72.249.127.135 \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=95782&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]
[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=95782&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95782&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
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
means0.4710.059-0.471-0.1710.1190.209

\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=95782&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=95782&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95782&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=95782&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=95782&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95782&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=95782&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=95782&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95782&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=95782&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=95782&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=95782&T=4

As an alternative you can also use a QR Code:  
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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 = 1 ; par2 = 2 ; par3 = 0.95 ; par4 = two.sided ; par5 = paired ; par6 = 0.0 ;
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')