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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 computationSun, 04 Nov 2012 08:43:01 -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/Nov/04/t1352036655cf95wj4q4c2ryd1.htm/, Retrieved Thu, 02 May 2024 19:14:36 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=185817, Retrieved Thu, 02 May 2024 19:14:36 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact117

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] [] [2012-11-04 13:43:01] [cd784a0623f47f402dddaa62da6ddd9f] [Current]
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Dataset

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

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time3 seconds
R Server'George Udny Yule' @ yule.wessa.net

\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 & 3 seconds \tabularnewline
R Server & 'George Udny Yule' @ yule.wessa.net \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185817&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]3 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'George Udny Yule' @ yule.wessa.net[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185817&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185817&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 time3 seconds
R Server'George Udny Yule' @ yule.wessa.net






ANOVA Model
Response ~ Treatment_A * Treatment_B - 1
means15132015.9172115.75222214.75232016.75202020191221-5-4-2.75-1.75-3-10-3-5-4-0.25-4-4-7-4-81.25NANANANANANANANANANANANANANANANANANANANA6.083-16.25NA-2NANANANANANANANANANANANA-1.167-3.252NANANANANANANANANANANANANANANANA1NANANANANANANANANANANANANA-4NANA1.25NA-5NANANANA0NANANANANA103NA510.251.667411.25NA49.259NANANANANANA-1NANA-0.25NA-31.25-3.667-13.25-1-4NANANANANANANA-0.3332.417-1.5-34.25-4NANANANANANANANANANA3.583NA3.75-2.5NA3.25-3-23.25NANANANANANANANANA-9.75-3.5-8.75-6.25NA-6.75-5.417NANANANANANANANANANA-52.25-2.667-4NA-6-11.25NANANANANANANANANANA1.25-3-35.5831NANANANANANANANANANANANA7.25NA1NANA3.2510.254.667NANANANANANANANANANA-2NA5.25NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B - 1 \tabularnewline
means & 15 & 13 & 20 & 15.917 & 21 & 15.75 & 22 & 22 & 14.75 & 23 & 20 & 16.75 & 20 & 20 & 20 & 19 & 12 & 21 & -5 & -4 & -2.75 & -1.75 & -3 & -10 & -3 & -5 & -4 & -0.25 & -4 & -4 & -7 & -4 & -8 & 1.25 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 6.083 & -1 & 6.25 & NA & -2 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -1.167 & -3.25 & 2 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -4 & NA & NA & 1.25 & NA & -5 & NA & NA & NA & NA & 0 & NA & NA & NA & NA & NA & 10 & 3 & NA & 5 & 10.25 & 1.667 & 4 & 11.25 & NA & 4 & 9.25 & 9 & NA & NA & NA & NA & NA & NA & -1 & NA & NA & -0.25 & NA & -3 & 1.25 & -3.667 & -1 & 3.25 & -1 & -4 & NA & NA & NA & NA & NA & NA & NA & -0.333 & 2.417 & -1.5 & -3 & 4.25 & -4 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 3.583 & NA & 3.75 & -2.5 & NA & 3.25 & -3 & -2 & 3.25 & NA & NA & NA & NA & NA & NA & NA & NA & NA & -9.75 & -3.5 & -8.75 & -6.25 & NA & -6.75 & -5.417 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -5 & 2.25 & -2.667 & -4 & NA & -6 & -1 & 1.25 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 1.25 & -3 & -3 & 5.583 & 1 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 7.25 & NA & 1 & NA & NA & 3.25 & 10.25 & 4.667 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -2 & NA & 5.25 & 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=185817&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]Response ~ Treatment_A * Treatment_B - 1[/C][/ROW]
[ROW][C]means[/C][C]15[/C][C]13[/C][C]20[/C][C]15.917[/C][C]21[/C][C]15.75[/C][C]22[/C][C]22[/C][C]14.75[/C][C]23[/C][C]20[/C][C]16.75[/C][C]20[/C][C]20[/C][C]20[/C][C]19[/C][C]12[/C][C]21[/C][C]-5[/C][C]-4[/C][C]-2.75[/C][C]-1.75[/C][C]-3[/C][C]-10[/C][C]-3[/C][C]-5[/C][C]-4[/C][C]-0.25[/C][C]-4[/C][C]-4[/C][C]-7[/C][C]-4[/C][C]-8[/C][C]1.25[/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]6.083[/C][C]-1[/C][C]6.25[/C][C]NA[/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]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-1.167[/C][C]-3.25[/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]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]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]-4[/C][C]NA[/C][C]NA[/C][C]1.25[/C][C]NA[/C][C]-5[/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]10[/C][C]3[/C][C]NA[/C][C]5[/C][C]10.25[/C][C]1.667[/C][C]4[/C][C]11.25[/C][C]NA[/C][C]4[/C][C]9.25[/C][C]9[/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]-0.25[/C][C]NA[/C][C]-3[/C][C]1.25[/C][C]-3.667[/C][C]-1[/C][C]3.25[/C][C]-1[/C][C]-4[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]NA[/C][C]-0.333[/C][C]2.417[/C][C]-1.5[/C][C]-3[/C][C]4.25[/C][C]-4[/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]3.583[/C][C]NA[/C][C]3.75[/C][C]-2.5[/C][C]NA[/C][C]3.25[/C][C]-3[/C][C]-2[/C][C]3.25[/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]-9.75[/C][C]-3.5[/C][C]-8.75[/C][C]-6.25[/C][C]NA[/C][C]-6.75[/C][C]-5.417[/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]-5[/C][C]2.25[/C][C]-2.667[/C][C]-4[/C][C]NA[/C][C]-6[/C][C]-1[/C][C]1.25[/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]1.25[/C][C]-3[/C][C]-3[/C][C]5.583[/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]7.25[/C][C]NA[/C][C]1[/C][C]NA[/C][C]NA[/C][C]3.25[/C][C]10.25[/C][C]4.667[/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]-2[/C][C]NA[/C][C]5.25[/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=185817&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185817&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 - 1
means15132015.9172115.75222214.75232016.75202020191221-5-4-2.75-1.75-3-10-3-5-4-0.25-4-4-7-4-81.25NANANANANANANANANANANANANANANANANANANANA6.083-16.25NA-2NANANANANANANANANANANANA-1.167-3.252NANANANANANANANANANANANANANANANA1NANANANANANANANANANANANANA-4NANA1.25NA-5NANANANA0NANANANANA103NA510.251.667411.25NA49.259NANANANANANA-1NANA-0.25NA-31.25-3.667-13.25-1-4NANANANANANANA-0.3332.417-1.5-34.25-4NANANANANANANANANANA3.583NA3.75-2.5NA3.25-3-23.25NANANANANANANANANA-9.75-3.5-8.75-6.25NA-6.75-5.417NANANANANANANANANANA-52.25-2.667-4NA-6-11.25NANANANANANANANANANA1.25-3-35.5831NANANANANANANANANANANANA7.25NA1NANA3.2510.254.667NANANANANANANANANANA-2NA5.25NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
18
Treatment_A1836340.4072018.912359.6850
Treatment_B1899.766.2351.1110.367
Treatment_A:Treatment_B18258.6663.7490.6680.947
Residuals59331.1675.613 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 18 &  &  &  &  \tabularnewline
Treatment_A & 18 & 36340.407 & 2018.912 & 359.685 & 0 \tabularnewline
Treatment_B & 18 & 99.76 & 6.235 & 1.111 & 0.367 \tabularnewline
Treatment_A:Treatment_B & 18 & 258.666 & 3.749 & 0.668 & 0.947 \tabularnewline
Residuals & 59 & 331.167 & 5.613 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185817&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]18[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]18[/C][C]36340.407[/C][C]2018.912[/C][C]359.685[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]18[/C][C]99.76[/C][C]6.235[/C][C]1.111[/C][C]0.367[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]18[/C][C]258.666[/C][C]3.749[/C][C]0.668[/C][C]0.947[/C][/ROW]
[ROW][C]Residuals[/C][C]59[/C][C]331.167[/C][C]5.613[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185817&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185817&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)
18
Treatment_A1836340.4072018.912359.6850
Treatment_B1899.766.2351.1110.367
Treatment_A:Treatment_B18258.6663.7490.6680.947
Residuals59331.1675.613






Must Include Intercept to use Tukey Test 

\begin{tabular}{lllllllll}
\hline
Must Include Intercept to use Tukey Test  \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185817&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185817&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185817&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:

Must Include Intercept to use Tukey Test






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group1020.6050.987
59 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 102 & 0.605 & 0.987 \tabularnewline
  & 59 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=185817&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]102[/C][C]0.605[/C][C]0.987[/C][/ROW]
[ROW][C] [/C][C]59[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=185817&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=185817&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)
Group1020.6050.987
59


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = FALSE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = 3 ; par4 = FALSE ;
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')