FreeStatistics.org

Free Statistics

of Irreproducible Research!

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, 18 Nov 2010 17:38:25 +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/18/t129010183972v5iblpmi1d6wy.htm/, Retrieved Mon, 06 May 2024 18:44:07 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=97482, Retrieved Mon, 06 May 2024 18:44:07 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact162

Tree of Dependent Computations

Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
F       [Two-Way ANOVA] [] [2010-11-18 17:38:25] [95fdfecfb4f2f50e2168e1a971ea5f83] [Current]
Feedback Forum
2010-11-20 14:28:26 [Hans Tierens] [reply] 
Ik vrees dat je een beetje verloren loopt in al de output.
In de Anova Statistics bekijkt men A = invloed van leermiddelen, B = invloed van geslacht en A:B = eventuele interactie-effecten.

Bij de Tukey Honest tabel maak je hier en daar een foutje:

Je kan uit de vergelijking van de leermiddelen apart niet zomaar besluiten dat F het beste leermateriaal is voor zowel mannen als vrouwen! Hier vind ik namelijk nergens een geslachtscomponent terug (bovendien is er geen significant verschil tussen de prestatievergelijking per geslacht)...

Ik geef je even de juiste vergelijkingen, waaruit je wel kan besluiten dat F voor zowel vrouwen als mannen de beste treatment is. Houdt goed in het achterhoofd dat je eigenlijk zoekt naar een treatment die je perfect aan een gemengde groep kunt geven...

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)


Ik geef je ook nog een extra conclusie mee:

als je kijkt naar de grafiek: 'Possible Interactions Between Anova Groups' zie je een groot verschil tussen mannen en vrouwen met treatment E. Wanneer we gaan kijken in de tabel zien we dat E:1-E:0 niet significant verschillen is van nul, het is dus aan het toeval te wijten (natuurlijk kennen we hier de type 2 fout 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 time3 seconds
R Server'George Udny Yule' @ 72.249.76.132

\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' @ 72.249.76.132 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=97482&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' @ 72.249.76.132[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=97482&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97482&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' @ 72.249.76.132






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=97482&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=97482&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97482&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=97482&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=97482&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97482&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=97482&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=97482&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97482&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=97482&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=97482&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=97482&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)
Group55.5040
111


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 4 ; par2 = 3 ; par3 = 5 ; par4 = TRUE ;
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')