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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 computationSat, 22 Dec 2012 11:01:36 -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/22/t1356192148muijhydn0i8399o.htm/, Retrieved Fri, 29 Mar 2024 15:55:22 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=204547, Retrieved Fri, 29 Mar 2024 15:55:22 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact142
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]
- R P   [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Short term effect...] [2012-12-20 00:31:37] [ce03f21eb3e54b507ec8f2385bd7917e]
-         [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [One-way ANOVA kor...] [2012-12-22 14:57:57] [74be16979710d4c4e7c6647856088456]
- RMPD        [Two-Way ANOVA] [two way ANOVA int...] [2012-12-22 16:01:36] [04b4f93d77ad48b80a96306d43249fd5] [Current]
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Dataseries X:
1	26	23
1	20	24
1	19	22
0	19	20
1	20	24
1	25	27
0	25	28
1	22	27
1	26	24
1	22	23
0	17	24
0	22	27
1	19	27
1	24	28
1	26	27
0	21	23
1	13	24
0	26	28
0	20	27
1	22	25
0	14	19
1	21	24
1	7	20
0	23	28
1	17	26
1	25	23
1	25	23
1	19	20
0	20	11
1	23	24
1	22	25
1	22	23
1	21	18
0	15	20
0	20	20
0	22	24
1	18	23
0	20	25
0	28	28
1	22	26
1	18	26
1	23	23
1	20	22
0	25	24
0	26	21
1	15	20
0	17	22
0	23	20
1	21	25
0	13	20
1	18	22
1	19	23
1	22	25
1	16	23
0	24	23
1	18	22
1	20	24
1	24	25
0	14	21
0	22	12
1	24	17
1	18	20
1	21	23
0	23	23
1	17	20
0	22	28
0	24	24
0	21	24
1	22	24
1	16	24
1	21	28
0	23	25
0	22	21
1	24	25
1	24	25
1	16	18
1	16	17
0	21	26
0	26	28
0	15	21
0	25	27
1	18	22
1	23	21
1	20	25
0	17	22
0	25	23
1	24	26
1	17	19
1	19	25
1	20	21
1	15	13
0	27	24
1	22	25
1	23	26
1	16	25
1	19	25
0	25	22
1	19	21
0	19	23
0	26	25
1	21	24
0	20	21
1	24	21
1	22	25
0	20	22
1	18	20
0	18	20
1	24	23
1	24	28
1	22	23
1	23	28
1	22	24
1	20	18
1	18	20
1	25	28
0	18	21
1	16	21
1	20	25
0	19	19
1	15	18
1	19	21
1	19	22
1	16	24
1	17	15
1	28	28
0	23	26
1	25	23
1	20	26
0	17	20
0	23	22
1	16	20
0	23	23
0	11	22
0	18	24
0	24	23
1	23	22
1	21	26
0	16	23
0	24	27
1	23	23
1	18	21
1	20	26
1	9	23
0	24	21
1	25	27
1	20	19
0	21	23
0	25	25
0	22	23
0	21	22
1	21	22
1	22	25
1	27	25
0	24	28
0	24	28
0	21	20
1	18	25
1	16	19
1	22	25
1	20	22
0	18	18
1	20	20




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=204547&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=204547&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204547&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 time3 seconds
R Server'George Udny Yule' @ yule.wessa.net







ANOVA Model
Response ~ Treatment_A * Treatment_B - 1
means1.531121.51.510211.51.51.5121.52.51-10-0.5-0.5-11-1.5-1-1.50-2-1-0.50-1NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA-0.5NANANANANANANANANANANANANANANANA10NA-0.5NA2NANANANANANANANANANANA-2NA-2-1.5NA-2NANANANANANANANANANANA-1.5NA10.50.50.7512-0.5NANANANANANANANANANANANA0NA011.5NA00.50NANANANANANANANANANA011.52.1670NA0.5NANANANANANANANANANA-1.5NA-0.5-0.5NA-1.667-0.25-1-1.167-0.75NANANANANANANANA10.50.5NA30.6671.6671.50.50.52NANANANANANANANANA0.511.66701-0.50.5-0.5NANANANANANANANANANANANA1.5-10.5-0.5NANANANANANANANANANANANANANANANA-0.5NA-1.5-0.833NANANANANANANANANANANANANANANANANANANANANANANA

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B - 1 \tabularnewline
means & 1.5 & 3 & 1 & 1 & 2 & 1.5 & 1.5 & 1 & 0 & 2 & 1 & 1.5 & 1.5 & 1.5 & 1 & 2 & 1.5 & 2.5 & 1 & -1 & 0 & -0.5 & -0.5 & -1 & 1 & -1.5 & -1 & -1.5 & 0 & -2 & -1 & -0.5 & 0 & -1 & 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 & -0.5 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 1 & 0 & NA & -0.5 & NA & 2 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -2 & NA & -2 & -1.5 & NA & -2 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -1.5 & NA & 1 & 0.5 & 0.5 & 0.75 & 1 & 2 & -0.5 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 0 & NA & 0 & 1 & 1.5 & NA & 0 & 0.5 & 0 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 0 & 1 & 1.5 & 2.167 & 0 & NA & 0.5 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -1.5 & NA & -0.5 & -0.5 & NA & -1.667 & -0.25 & -1 & -1.167 & -0.75 & NA & NA & NA & NA & NA & NA & NA & NA & 1 & 0.5 & 0.5 & NA & 3 & 0.667 & 1.667 & 1.5 & 0.5 & 0.5 & 2 & NA & NA & NA & NA & NA & NA & NA & NA & NA & 0.5 & 1 & 1.667 & 0 & 1 & -0.5 & 0.5 & -0.5 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & 1.5 & -1 & 0.5 & -0.5 & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & NA & -0.5 & NA & -1.5 & -0.833 & 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=204547&T=1

[TABLE]
[ROW]ANOVA Model[/C][/ROW]
[ROW]Response ~ Treatment_A * Treatment_B - 1[/C][/ROW]
[ROW][C]means[/C][C]1.5[/C][C]3[/C][C]1[/C][C]1[/C][C]2[/C][C]1.5[/C][C]1.5[/C][C]1[/C][C]0[/C][C]2[/C][C]1[/C][C]1.5[/C][C]1.5[/C][C]1.5[/C][C]1[/C][C]2[/C][C]1.5[/C][C]2.5[/C][C]1[/C][C]-1[/C][C]0[/C][C]-0.5[/C][C]-0.5[/C][C]-1[/C][C]1[/C][C]-1.5[/C][C]-1[/C][C]-1.5[/C][C]0[/C][C]-2[/C][C]-1[/C][C]-0.5[/C][C]0[/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]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]-0.5[/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]1[/C][C]0[/C][C]NA[/C][C]-0.5[/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]-2[/C][C]NA[/C][C]-2[/C][C]-1.5[/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]-1.5[/C][C]NA[/C][C]1[/C][C]0.5[/C][C]0.5[/C][C]0.75[/C][C]1[/C][C]2[/C][C]-0.5[/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]0[/C][C]NA[/C][C]0[/C][C]1[/C][C]1.5[/C][C]NA[/C][C]0[/C][C]0.5[/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]0[/C][C]1[/C][C]1.5[/C][C]2.167[/C][C]0[/C][C]NA[/C][C]0.5[/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]-1.5[/C][C]NA[/C][C]-0.5[/C][C]-0.5[/C][C]NA[/C][C]-1.667[/C][C]-0.25[/C][C]-1[/C][C]-1.167[/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]1[/C][C]0.5[/C][C]0.5[/C][C]NA[/C][C]3[/C][C]0.667[/C][C]1.667[/C][C]1.5[/C][C]0.5[/C][C]0.5[/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]0.5[/C][C]1[/C][C]1.667[/C][C]0[/C][C]1[/C][C]-0.5[/C][C]0.5[/C][C]-0.5[/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]1.5[/C][C]-1[/C][C]0.5[/C][C]-0.5[/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]-0.5[/C][C]NA[/C][C]-1.5[/C][C]-0.833[/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=204547&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204547&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 - 1
means1.531121.51.510211.51.51.5121.52.51-10-0.5-0.5-11-1.5-1-1.50-2-1-0.50-1NANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANANA-0.5NANANANANANANANANANANANANANANANA10NA-0.5NA2NANANANANANANANANANANA-2NA-2-1.5NA-2NANANANANANANANANANANA-1.5NA10.50.50.7512-0.5NANANANANANANANANANANANA0NA011.5NA00.50NANANANANANANANANANA011.52.1670NA0.5NANANANANANANANANANA-1.5NA-0.5-0.5NA-1.667-0.25-1-1.167-0.75NANANANANANANANA10.50.5NA30.6671.6671.50.50.52NANANANANANANANANA0.511.66701-0.50.5-0.5NANANANANANANANANANANANA1.5-10.5-0.5NANANANANANANANANANANANANANANANA-0.5NA-1.5-0.833NANANANANANANANANANANANANANANANANANANANANANANA







ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
19
Treatment_A1965.4073.44212.8480
Treatment_B193.4630.2310.8620.608
Treatment_A:Treatment_B1913.7140.2180.8120.795
Residuals6517.4170.268

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 19 &  &  &  &  \tabularnewline
Treatment_A & 19 & 65.407 & 3.442 & 12.848 & 0 \tabularnewline
Treatment_B & 19 & 3.463 & 0.231 & 0.862 & 0.608 \tabularnewline
Treatment_A:Treatment_B & 19 & 13.714 & 0.218 & 0.812 & 0.795 \tabularnewline
Residuals & 65 & 17.417 & 0.268 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204547&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]19[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]19[/C][C]65.407[/C][C]3.442[/C][C]12.848[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]19[/C][C]3.463[/C][C]0.231[/C][C]0.862[/C][C]0.608[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]19[/C][C]13.714[/C][C]0.218[/C][C]0.812[/C][C]0.795[/C][/ROW]
[ROW][C]Residuals[/C][C]65[/C][C]17.417[/C][C]0.268[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204547&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204547&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)
19
Treatment_A1965.4073.44212.8480
Treatment_B193.4630.2310.8620.608
Treatment_A:Treatment_B1913.7140.2180.8120.795
Residuals6517.4170.268







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=204547&T=3

[TABLE]
[ROW][C]Must Include Intercept to use Tukey Test [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204547&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204547&T=3

As an alternative you can also use a 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)
Group960.7490.902
65

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 96 & 0.749 & 0.902 \tabularnewline
  & 65 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=204547&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]96[/C][C]0.749[/C][C]0.902[/C][/ROW]
[ROW][C] [/C][C]65[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=204547&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=204547&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)
Group960.7490.902
65



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