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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 computationThu, 13 Dec 2012 12:36:03 -0500
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2012/Dec/13/t1355420255p8zkyrj7lggs8k1.htm/, Retrieved Mon, 29 Apr 2024 03:54:39 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=199335, Retrieved Mon, 29 Apr 2024 03:54:39 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact85

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] [fr] [2012-12-13 17:36:03] [e4c351aee2a0bb2c047702ea90f356fa] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
2	41	38	14	12
2	39	32	18	11
2	30	35	11	14
1	31	33	12	12
2	34	37	16	21
2	35	29	18	12
2	39	31	14	22
2	34	36	14	11
2	36	35	15	10
2	37	38	15	13
1	38	31	17	10
2	36	34	19	8
1	38	35	10	15
2	39	38	16	14
2	33	37	18	10
1	32	33	14	14
1	36	32	14	14
2	38	38	17	11
1	39	38	14	10
2	32	32	16	13
1	32	33	18	7
2	31	31	11	14
2	39	38	14	12
2	37	39	12	14
1	39	32	17	11
2	41	32	9	9
1	36	35	16	11
2	33	37	14	15
2	33	33	15	14
1	34	33	11	13
2	31	28	16	9
1	27	32	13	15
2	37	31	17	10
2	34	37	15	11
1	34	30	14	13
1	32	33	16	8
1	29	31	9	20
1	36	33	15	12
2	29	31	17	10
1	35	33	13	10
1	37	32	15	9
2	34	33	16	14
1	38	32	16	8
1	35	33	12	14
2	38	28	12	11
2	37	35	11	13
2	38	39	15	9
2	33	34	15	11
2	36	38	17	15
1	38	32	13	11
2	32	38	16	10
1	32	30	14	14
1	32	33	11	18
2	34	38	12	14
1	32	32	12	11
2	37	32	15	12
2	39	34	16	13
2	29	34	15	9
1	37	36	12	10
2	35	34	12	15
1	30	28	8	20
1	38	34	13	12
2	34	35	11	12
2	31	35	14	14
2	34	31	15	13
1	35	37	10	11
2	36	35	11	17
1	30	27	12	12
2	39	40	15	13
1	35	37	15	14
1	38	36	14	13
2	31	38	16	15
2	34	39	15	13
1	38	41	15	10
1	34	27	13	11
2	39	30	12	19
2	37	37	17	13
2	34	31	13	17
1	28	31	15	13
1	37	27	13	9
1	33	36	15	11
1	37	38	16	10
2	35	37	15	9
1	37	33	16	12
2	32	34	15	12
2	33	31	14	13
1	38	39	15	13
2	33	34	14	12
2	29	32	13	15
2	33	33	7	22
2	31	36	17	13
2	36	32	13	15
2	35	41	15	13
2	32	28	14	15
2	29	30	13	10
2	39	36	16	11
2	37	35	12	16
2	35	31	14	11
1	37	34	17	11
1	32	36	15	10
2	38	36	17	10
1	37	35	12	16
2	36	37	16	12
1	32	28	11	11
2	33	39	15	16
1	40	32	9	19
2	38	35	16	11
1	41	39	15	16
1	36	35	10	15
2	43	42	10	24
2	30	34	15	14
2	31	33	11	15
2	32	41	13	11
1	32	33	14	15
2	37	34	18	12
1	37	32	16	10
2	33	40	14	14
2	34	40	14	13
2	33	35	14	9
2	38	36	14	15
2	33	37	12	15
2	31	27	14	14
2	38	39	15	11
2	37	38	15	8
2	33	31	15	11
2	31	33	13	11
1	39	32	17	8
2	44	39	17	10
2	33	36	19	11
2	35	33	15	13
1	32	33	13	11
1	28	32	9	20
2	40	37	15	10
1	27	30	15	15
1	37	38	15	12
2	32	29	16	14
1	28	22	11	23
1	34	35	14	14
2	30	35	11	16
2	35	34	15	11
1	31	35	13	12
2	32	34	15	10
1	30	34	16	14
2	30	35	14	12
1	31	23	15	12
2	40	31	16	11
2	32	27	16	12
1	36	36	11	13
1	32	31	12	11
1	35	32	9	19
2	38	39	16	12
2	42	37	13	17
1	34	38	16	9
2	35	39	12	12
2	35	34	9	19
2	33	31	13	18
2	36	32	13	15
2	32	37	14	14
2	33	36	19	11
2	34	32	13	9
2	32	35	12	18
2	34	36	13	16

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time120 seconds
R Server'Sir Maurice George Kendall' @ kendall.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 & 120 seconds \tabularnewline
R Server & 'Sir Maurice George Kendall' @ kendall.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=199335&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]120 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Sir Maurice George Kendall' @ kendall.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=199335&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199335&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 time120 seconds
R Server'Sir Maurice George Kendall' @ kendall.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
means1022.2512.75222.75121.75221212-1-2-0.75-1.750-10-2-1-1.25-1-10-1-1-1.75NANANANANANANANANANANANANANANANANANANANA-0.2520.25NA0NANANANANANANANANANANANA-1.50.75-1.5NANANANANANANANANANANANANANANANA0NANANANANANANANANANANANANA-1NANA-2.75NA-2NANANANA-1NANANANANA1-0.5NA1-1.7500-0.75NA0-0.750NANANANANANA-1NANA-2.25NA-1-2.75-0.333-1.667-1.75-1.667-2NANANANANANANA1.667-0.7510.5-0.4171NANANANANANANANANANA-0.75NA-0.750NA-0.751-0.5-0.75NANANANANANANANANA0.75-0.50.25-0.25NA0.75-0.083NANANANANANANANANANA1-1.75-0.3330NA0-1-0.083NANANANANANANANANANA-0.7500-1.4171NANANANANANANANANANANANA-1.75NA-1.5NANA-1.5-0.75-1.333NANANANANANANANANANA0NA-0.75NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 1 & 0 & 2 & 2.25 & 1 & 2.75 & 2 & 2 & 2.75 & 1 & 2 & 1.75 & 2 & 2 & 1 & 2 & 1 & 2 & -1 & -2 & -0.75 & -1.75 & 0 & -1 & 0 & -2 & -1 & -1.25 & -1 & -1 & 0 & -1 & -1 & -1.75 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -0.25 & 2 & 0.25 & NA & 0 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -1.5 & 0.75 & -1.5 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 0 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -1 & NA & NA & -2.75 & NA & -2 & NA & NA & NA & NA & -1 & NA & NA & NA & NA & NA & 1 & -0.5 & NA & 1 & -1.75 & 0 & 0 & -0.75 & NA & 0 & -0.75 & 0 & NA & NA & NA & NA & NA & NA & -1 & NA & NA & -2.25 & NA & -1 & -2.75 & -0.333 & -1.667 & -1.75 & -1.667 & -2 & NA & NA & NA & NA & NA & NA & NA & 1.667 & -0.75 & 1 & 0.5 & -0.417 & 1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -0.75 & NA & -0.75 & 0 & NA & -0.75 & 1 & -0.5 & -0.75 & NA & NA & NA & NA & NA & NA & NA & NA & NA & 0.75 & -0.5 & 0.25 & -0.25 & NA & 0.75 & -0.083 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 1 & -1.75 & -0.333 & 0 & NA & 0 & -1 & -0.083 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -0.75 & 0 & 0 & -1.417 & 1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -1.75 & NA & -1.5 & NA & NA & -1.5 & -0.75 & -1.333 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 0 & NA & -0.75 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199335&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]1[/C][C]0[/C][C]2[/C][C]2.25[/C][C]1[/C][C]2.75[/C][C]2[/C][C]2[/C][C]2.75[/C][C]1[/C][C]2[/C][C]1.75[/C][C]2[/C][C]2[/C][C]1[/C][C]2[/C][C]1[/C][C]2[/C][C]-1[/C][C]-2[/C][C]-0.75[/C][C]-1.75[/C][C]0[/C][C]-1[/C][C]0[/C][C]-2[/C][C]-1[/C][C]-1.25[/C][C]-1[/C][C]-1[/C][C]0[/C][C]-1[/C][C]-1[/C][C]-1.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.25[/C][C]2[/C][C]0.25[/C][C]NA[/C][C]0[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1.5[/C][C]0.75[/C][C]-1.5[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1[/C][C]NA[/C][C]NA[/C][C]-2.75[/C][C]NA[/C][C]-2[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1[/C][C]-0.5[/C][C]NA[/C][C]1[/C][C]-1.75[/C][C]0[/C][C]0[/C][C]-0.75[/C][C]NA[/C][C]0[/C][C]-0.75[/C][C]0[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1[/C][C]NA[/C][C]NA[/C][C]-2.25[/C][C]NA[/C][C]-1[/C][C]-2.75[/C][C]-0.333[/C][C]-1.667[/C][C]-1.75[/C][C]-1.667[/C][C]-2[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1.667[/C][C]-0.75[/C][C]1[/C][C]0.5[/C][C]-0.417[/C][C]1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.75[/C][C]NA[/C][C]-0.75[/C][C]0[/C][C]NA[/C][C]-0.75[/C][C]1[/C][C]-0.5[/C][C]-0.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0.75[/C][C]-0.5[/C][C]0.25[/C][C]-0.25[/C][C]NA[/C][C]0.75[/C][C]-0.083[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]1[/C][C]-1.75[/C][C]-0.333[/C][C]0[/C][C]NA[/C][C]0[/C][C]-1[/C][C]-0.083[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.75[/C][C]0[/C][C]0[/C][C]-1.417[/C][C]1[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1.75[/C][C]NA[/C][C]-1.5[/C][C]NA[/C][C]NA[/C][C]-1.5[/C][C]-0.75[/C][C]-1.333[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]0[/C][C]NA[/C][C]-0.75[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199335&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199335&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
means1022.2512.75222.75121.75221212-1-2-0.75-1.750-10-2-1-1.25-1-10-1-1-1.75NANANANANANANANANANANANANANANANANANANANA-0.2520.25NA0NANANANANANANANANANANANA-1.50.75-1.5NANANANANANANANANANANANANANANANA0NANANANANANANANANANANANANA-1NANA-2.75NA-2NANANANA-1NANANANANA1-0.5NA1-1.7500-0.75NA0-0.750NANANANANANA-1NANA-2.25NA-1-2.75-0.333-1.667-1.75-1.667-2NANANANANANANA1.667-0.7510.5-0.4171NANANANANANANANANANA-0.75NA-0.750NA-0.751-0.5-0.75NANANANANANANANANA0.75-0.50.25-0.25NA0.75-0.083NANANANANANANANANANA1-1.75-0.3330NA0-1-0.083NANANANANANANANANANA-0.7500-1.4171NANANANANANANANANANANANA-1.75NA-1.5NANA-1.5-0.75-1.333NANANANANANANANANANA0NA-0.75NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
17
Treatment_A175.4840.3231.320.213
Treatment_B175.1660.3231.3210.215
Treatment_A:Treatment_B1712.9650.1880.7690.854
Residuals5914.4170.244 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 17 &  &  &  &  \tabularnewline
Treatment_A & 17 & 5.484 & 0.323 & 1.32 & 0.213 \tabularnewline
Treatment_B & 17 & 5.166 & 0.323 & 1.321 & 0.215 \tabularnewline
Treatment_A:Treatment_B & 17 & 12.965 & 0.188 & 0.769 & 0.854 \tabularnewline
Residuals & 59 & 14.417 & 0.244 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=199335&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]17[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]17[/C][C]5.484[/C][C]0.323[/C][C]1.32[/C][C]0.213[/C][/ROW]
[ROW][C]Treatment_B[/C][C]17[/C][C]5.166[/C][C]0.323[/C][C]1.321[/C][C]0.215[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]17[/C][C]12.965[/C][C]0.188[/C][C]0.769[/C][C]0.854[/C][/ROW]
[ROW][C]Residuals[/C][C]59[/C][C]14.417[/C][C]0.244[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=199335&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=199335&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)
17
Treatment_A175.4840.3231.320.213
Treatment_B175.1660.3231.3210.215
Treatment_A:Treatment_B1712.9650.1880.7690.854
Residuals5914.4170.244


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = TRUE ;
R code (references can be found in the software module):
par4 <- 'TRUE'
par3 <- '3'
par2 <- '2'
par1 <- '1'
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')