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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software ModuleIan.Hollidayrwasp_One Factor ANOVA.wasp
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationTue, 30 Nov 2010 01:54:10 +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/30/t12910820124v4t304rshcxn5l.htm/, Retrieved Mon, 29 Apr 2024 09:53:48 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=103210, Retrieved Mon, 29 Apr 2024 09:53:48 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact153

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA wit...] [2009-11-29 13:09:19] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   PD  [Chi Square Measure of Association- Free Statistics Software (Calculator)] [One Way ANOVA for...] [2009-12-01 13:05:10] [3fdd735c61ad38cbc9b3393dc997cdb7]
- R P     [Chi Square Measure of Association- Free Statistics Software (Calculator)] [CARE date with Tu...] [2009-12-01 18:33:48] [98fd0e87c3eb04e0cc2efde01dbafab6]
-   P       [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [CARE Data with Tu...] [2010-11-23 12:09:38] [3fdd735c61ad38cbc9b3393dc997cdb7]
-    D          [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [w8q2] [2010-11-30 01:54:10] [72bf2beca6175a64cd7bd78316b2d56f] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	3
2	6
2	8
3	8
1	7
1	5
2	7
2	8
1	9
3	9
1	3
3	9
2	7
1	9
2	8
2	6
2	7
1	8
3	9
2	7
1	6
1	8
1	7
1	7
2	8
3	9
2	9
2	7
2	4
2	7
2	7
3	9
2	7
2	9
2	10
1	5
2	6
2	9
3	9
1	8
2	6
1	6
2	5
3	8
2	8
2	5
3	6
1	9
1	8
2	4
2	8
1	9
3	7
1	7
1	6
1	9
2	9
2	8
2	4
1	6
1	10
2	8
2	7
2	7
2	8
2	3
3	8
2	10
1	7
1	5
2	10
2	5
2	8
3	9
2	6
2	9
3	8
1	5
2	8
1	3
2	7
2	8
2	10
2	9
3	10
3	9
2	8
2	8
2	8
2	9
2	4
2	6
1	7
1	4
1	9
3	7
2	8
2	0
2	8
2	7
1	7
3	9
2	8
2	8
1	9
2	9
3	10
2	7
1	8
1	5
1	9
1	8
2	7
2	8
3	8
2	7
2	6
2	7
3	7
1	6
3	6
1	7
2	9
2	6
2	10
1	4
2	8
1	7
3	10
3	0
2	5
2	9
1	8
3	9
2	8
1	8
1	9
1	8
1	9
2	7
3	6
2	8
2	6
1	5
2	3
1	6
1	8
2	7
2	8
2	6
2	9
3	9
3	10
3	7
2	5
2	8
2	9
1	8
2	8
1	4

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'RServer@AstonUniversity' @ vre.aston.ac.uk

\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 & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103210&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]'RServer@AstonUniversity' @ vre.aston.ac.uk[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103210&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103210&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'RServer@AstonUniversity' @ vre.aston.ac.uk






ANOVA Model
MVRBIQ0 ~ MWARM30
means2.5-0.2-1.1-0.929-1.045-0.658-0.661-0.643-0.47

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
MVRBIQ0  ~  MWARM30 \tabularnewline
means & 2.5 & -0.2 & -1.1 & -0.929 & -1.045 & -0.658 & -0.661 & -0.643 & -0.47 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103210&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]MVRBIQ0  ~  MWARM30[/C][/ROW]
[ROW][C]means[/C][C]2.5[/C][C]-0.2[/C][C]-1.1[/C][C]-0.929[/C][C]-1.045[/C][C]-0.658[/C][C]-0.661[/C][C]-0.643[/C][C]-0.47[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103210&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103210&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
MVRBIQ0 ~ MWARM30
means2.5-0.2-1.1-0.929-1.045-0.658-0.661-0.643-0.47






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
MWARM3087.170.8962.0180.048
Residuals15167.0740.444 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
MWARM30 & 8 & 7.17 & 0.896 & 2.018 & 0.048 \tabularnewline
Residuals & 151 & 67.074 & 0.444 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103210&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]MWARM30[/C][C]8[/C][C]7.17[/C][C]0.896[/C][C]2.018[/C][C]0.048[/C][/ROW]
[ROW][C]Residuals[/C][C]151[/C][C]67.074[/C][C]0.444[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103210&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103210&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)
MWARM3087.170.8962.0180.048
Residuals15167.0740.444






Tukey Honest Significant Difference Comparisons
difflwruprp adj
10-0-0.2-1.8251.4251
3-0-1.1-2.8550.6550.565
4-0-0.929-2.610.7530.722
5-0-1.045-2.6580.5670.518
6-0-0.658-2.2170.9010.921
7-0-0.661-2.1920.8690.911
8-0-0.643-2.1610.8750.92
9-0-0.47-1.9971.0580.988
3-10-0.9-2.0490.2490.257
4-10-0.729-1.7620.3050.399
5-10-0.845-1.7620.0710.096
6-10-0.458-1.2770.3620.709
7-10-0.461-1.2240.3020.613
8-10-0.443-1.1810.2950.623
9-10-0.27-1.0270.4870.97
4-30.171-1.0571.41
5-30.055-1.0771.1861
6-30.442-0.6121.4960.924
7-30.439-0.5721.450.909
8-30.457-0.5351.450.876
9-30.63-0.3761.6370.566
5-4-0.117-1.1310.8971
6-40.271-0.6571.1980.992
7-40.267-0.6111.1450.989
8-40.286-0.5711.1420.98
9-40.459-0.4141.3320.773
6-50.388-0.4071.1820.837
7-50.384-0.3521.120.78
8-50.403-0.3081.1130.693
9-50.576-0.1551.3060.25
7-6-0.003-0.6150.6081
8-60.015-0.5650.5951
9-60.188-0.4160.7920.987
8-70.018-0.4780.5151
9-70.192-0.3330.7160.965
9-80.173-0.3150.6610.971

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
10-0 & -0.2 & -1.825 & 1.425 & 1 \tabularnewline
3-0 & -1.1 & -2.855 & 0.655 & 0.565 \tabularnewline
4-0 & -0.929 & -2.61 & 0.753 & 0.722 \tabularnewline
5-0 & -1.045 & -2.658 & 0.567 & 0.518 \tabularnewline
6-0 & -0.658 & -2.217 & 0.901 & 0.921 \tabularnewline
7-0 & -0.661 & -2.192 & 0.869 & 0.911 \tabularnewline
8-0 & -0.643 & -2.161 & 0.875 & 0.92 \tabularnewline
9-0 & -0.47 & -1.997 & 1.058 & 0.988 \tabularnewline
3-10 & -0.9 & -2.049 & 0.249 & 0.257 \tabularnewline
4-10 & -0.729 & -1.762 & 0.305 & 0.399 \tabularnewline
5-10 & -0.845 & -1.762 & 0.071 & 0.096 \tabularnewline
6-10 & -0.458 & -1.277 & 0.362 & 0.709 \tabularnewline
7-10 & -0.461 & -1.224 & 0.302 & 0.613 \tabularnewline
8-10 & -0.443 & -1.181 & 0.295 & 0.623 \tabularnewline
9-10 & -0.27 & -1.027 & 0.487 & 0.97 \tabularnewline
4-3 & 0.171 & -1.057 & 1.4 & 1 \tabularnewline
5-3 & 0.055 & -1.077 & 1.186 & 1 \tabularnewline
6-3 & 0.442 & -0.612 & 1.496 & 0.924 \tabularnewline
7-3 & 0.439 & -0.572 & 1.45 & 0.909 \tabularnewline
8-3 & 0.457 & -0.535 & 1.45 & 0.876 \tabularnewline
9-3 & 0.63 & -0.376 & 1.637 & 0.566 \tabularnewline
5-4 & -0.117 & -1.131 & 0.897 & 1 \tabularnewline
6-4 & 0.271 & -0.657 & 1.198 & 0.992 \tabularnewline
7-4 & 0.267 & -0.611 & 1.145 & 0.989 \tabularnewline
8-4 & 0.286 & -0.571 & 1.142 & 0.98 \tabularnewline
9-4 & 0.459 & -0.414 & 1.332 & 0.773 \tabularnewline
6-5 & 0.388 & -0.407 & 1.182 & 0.837 \tabularnewline
7-5 & 0.384 & -0.352 & 1.12 & 0.78 \tabularnewline
8-5 & 0.403 & -0.308 & 1.113 & 0.693 \tabularnewline
9-5 & 0.576 & -0.155 & 1.306 & 0.25 \tabularnewline
7-6 & -0.003 & -0.615 & 0.608 & 1 \tabularnewline
8-6 & 0.015 & -0.565 & 0.595 & 1 \tabularnewline
9-6 & 0.188 & -0.416 & 0.792 & 0.987 \tabularnewline
8-7 & 0.018 & -0.478 & 0.515 & 1 \tabularnewline
9-7 & 0.192 & -0.333 & 0.716 & 0.965 \tabularnewline
9-8 & 0.173 & -0.315 & 0.661 & 0.971 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103210&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]10-0[/C][C]-0.2[/C][C]-1.825[/C][C]1.425[/C][C]1[/C][/ROW]
[ROW][C]3-0[/C][C]-1.1[/C][C]-2.855[/C][C]0.655[/C][C]0.565[/C][/ROW]
[ROW][C]4-0[/C][C]-0.929[/C][C]-2.61[/C][C]0.753[/C][C]0.722[/C][/ROW]
[ROW][C]5-0[/C][C]-1.045[/C][C]-2.658[/C][C]0.567[/C][C]0.518[/C][/ROW]
[ROW][C]6-0[/C][C]-0.658[/C][C]-2.217[/C][C]0.901[/C][C]0.921[/C][/ROW]
[ROW][C]7-0[/C][C]-0.661[/C][C]-2.192[/C][C]0.869[/C][C]0.911[/C][/ROW]
[ROW][C]8-0[/C][C]-0.643[/C][C]-2.161[/C][C]0.875[/C][C]0.92[/C][/ROW]
[ROW][C]9-0[/C][C]-0.47[/C][C]-1.997[/C][C]1.058[/C][C]0.988[/C][/ROW]
[ROW][C]3-10[/C][C]-0.9[/C][C]-2.049[/C][C]0.249[/C][C]0.257[/C][/ROW]
[ROW][C]4-10[/C][C]-0.729[/C][C]-1.762[/C][C]0.305[/C][C]0.399[/C][/ROW]
[ROW][C]5-10[/C][C]-0.845[/C][C]-1.762[/C][C]0.071[/C][C]0.096[/C][/ROW]
[ROW][C]6-10[/C][C]-0.458[/C][C]-1.277[/C][C]0.362[/C][C]0.709[/C][/ROW]
[ROW][C]7-10[/C][C]-0.461[/C][C]-1.224[/C][C]0.302[/C][C]0.613[/C][/ROW]
[ROW][C]8-10[/C][C]-0.443[/C][C]-1.181[/C][C]0.295[/C][C]0.623[/C][/ROW]
[ROW][C]9-10[/C][C]-0.27[/C][C]-1.027[/C][C]0.487[/C][C]0.97[/C][/ROW]
[ROW][C]4-3[/C][C]0.171[/C][C]-1.057[/C][C]1.4[/C][C]1[/C][/ROW]
[ROW][C]5-3[/C][C]0.055[/C][C]-1.077[/C][C]1.186[/C][C]1[/C][/ROW]
[ROW][C]6-3[/C][C]0.442[/C][C]-0.612[/C][C]1.496[/C][C]0.924[/C][/ROW]
[ROW][C]7-3[/C][C]0.439[/C][C]-0.572[/C][C]1.45[/C][C]0.909[/C][/ROW]
[ROW][C]8-3[/C][C]0.457[/C][C]-0.535[/C][C]1.45[/C][C]0.876[/C][/ROW]
[ROW][C]9-3[/C][C]0.63[/C][C]-0.376[/C][C]1.637[/C][C]0.566[/C][/ROW]
[ROW][C]5-4[/C][C]-0.117[/C][C]-1.131[/C][C]0.897[/C][C]1[/C][/ROW]
[ROW][C]6-4[/C][C]0.271[/C][C]-0.657[/C][C]1.198[/C][C]0.992[/C][/ROW]
[ROW][C]7-4[/C][C]0.267[/C][C]-0.611[/C][C]1.145[/C][C]0.989[/C][/ROW]
[ROW][C]8-4[/C][C]0.286[/C][C]-0.571[/C][C]1.142[/C][C]0.98[/C][/ROW]
[ROW][C]9-4[/C][C]0.459[/C][C]-0.414[/C][C]1.332[/C][C]0.773[/C][/ROW]
[ROW][C]6-5[/C][C]0.388[/C][C]-0.407[/C][C]1.182[/C][C]0.837[/C][/ROW]
[ROW][C]7-5[/C][C]0.384[/C][C]-0.352[/C][C]1.12[/C][C]0.78[/C][/ROW]
[ROW][C]8-5[/C][C]0.403[/C][C]-0.308[/C][C]1.113[/C][C]0.693[/C][/ROW]
[ROW][C]9-5[/C][C]0.576[/C][C]-0.155[/C][C]1.306[/C][C]0.25[/C][/ROW]
[ROW][C]7-6[/C][C]-0.003[/C][C]-0.615[/C][C]0.608[/C][C]1[/C][/ROW]
[ROW][C]8-6[/C][C]0.015[/C][C]-0.565[/C][C]0.595[/C][C]1[/C][/ROW]
[ROW][C]9-6[/C][C]0.188[/C][C]-0.416[/C][C]0.792[/C][C]0.987[/C][/ROW]
[ROW][C]8-7[/C][C]0.018[/C][C]-0.478[/C][C]0.515[/C][C]1[/C][/ROW]
[ROW][C]9-7[/C][C]0.192[/C][C]-0.333[/C][C]0.716[/C][C]0.965[/C][/ROW]
[ROW][C]9-8[/C][C]0.173[/C][C]-0.315[/C][C]0.661[/C][C]0.971[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103210&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103210&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
10-0-0.2-1.8251.4251
3-0-1.1-2.8550.6550.565
4-0-0.929-2.610.7530.722
5-0-1.045-2.6580.5670.518
6-0-0.658-2.2170.9010.921
7-0-0.661-2.1920.8690.911
8-0-0.643-2.1610.8750.92
9-0-0.47-1.9971.0580.988
3-10-0.9-2.0490.2490.257
4-10-0.729-1.7620.3050.399
5-10-0.845-1.7620.0710.096
6-10-0.458-1.2770.3620.709
7-10-0.461-1.2240.3020.613
8-10-0.443-1.1810.2950.623
9-10-0.27-1.0270.4870.97
4-30.171-1.0571.41
5-30.055-1.0771.1861
6-30.442-0.6121.4960.924
7-30.439-0.5721.450.909
8-30.457-0.5351.450.876
9-30.63-0.3761.6370.566
5-4-0.117-1.1310.8971
6-40.271-0.6571.1980.992
7-40.267-0.6111.1450.989
8-40.286-0.5711.1420.98
9-40.459-0.4141.3320.773
6-50.388-0.4071.1820.837
7-50.384-0.3521.120.78
8-50.403-0.3081.1130.693
9-50.576-0.1551.3060.25
7-6-0.003-0.6150.6081
8-60.015-0.5650.5951
9-60.188-0.4160.7920.987
8-70.018-0.4780.5151
9-70.192-0.3330.7160.965
9-80.173-0.3150.6610.971






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group80.6840.705
151 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 8 & 0.684 & 0.705 \tabularnewline
  & 151 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=103210&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]8[/C][C]0.684[/C][C]0.705[/C][/ROW]
[ROW][C] [/C][C]151[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=103210&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=103210&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)
Group80.6840.705
151


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
intercept<-as.logical(par3)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
xdf<-data.frame(x1,f1)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
names(xdf)<-c('Response', 'Treatment')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, 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$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$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)
a<-table.row.start(a)
a<-table.element(a, V2,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$Df[2],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], 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, data=xdf, xlab=V2, ylab=V1)
dev.off()
if(intercept==TRUE){
thsd<-TukeyHSD(aov.xdf)
bitmap(file='TukeyHSDPlot.png')
plot(thsd)
dev.off()
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(i in 1:length(rownames(thsd[[1]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
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')