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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 computationMon, 01 Nov 2010 09:42:41 +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/01/t1288604632v4xwa4dnftl00af.htm/, Retrieved Mon, 29 Apr 2024 14:29:05 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=90675, Retrieved Mon, 29 Apr 2024 14:29:05 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact208

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] [Golfballs] [2010-10-25 12:43:22] [b98453cac15ba1066b407e146608df68]
F   PD    [Two-Way ANOVA] [Q6_1] [2010-11-01 09:42:41] [dcc54e7e6e8c80b7c45e040080afe6ab] [Current]
-           [Two-Way ANOVA] [] [2010-11-02 08:32:14] [049b50ae610f671f7417ed8e2d1295c1]
F RMPD      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Question 6] [2010-11-02 13:07:52] [13c73ac943380855a1c72833078e44d2]
-             [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-03 12:51:56] [43239ed98a62e091c70785d80176537f]
F RMPD      [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [Question 6, LT] [2010-11-02 13:14:23] [13c73ac943380855a1c72833078e44d2]
-             [One-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)] [] [2010-11-03 12:55:00] [43239ed98a62e091c70785d80176537f]
Feedback Forum
2010-11-06 09:49:44 [48eb36e2c01435ad7e4ea7854a9d98fe] [reply] 
Gezien de gegevens in de opgave was het gebruik van de one factor ANOVA- test hier misschien meer aangewezen om op een makkelijke manier een conclusie te trekken. Deze kunnen we gebruiken omdat we de gegevens post 1 - pre kunnen invoeren. Een voorbeeld van deze one factor ANOVA is hier te vinden: http://www.freestatistics.org/blog/index.php?v=date/2010/Nov/01/t1288618599zm6alpr6uitaher.htm/

Wanneer we de eerste tabel ANOVA Statistics bekijken, zien we dat de P – waarde zeer klein is, hieruit kunnen we besluiten dat we de nulhypothese –alle treatments zijn aan elkaar gelijk – kunnen verwerpen.

Vervolgens bekijken we de Tukey Honest Significant Difference Comparisons. Bij de vergelijking van de S- treatment en de E – treatment zien we dat de P – waarde zeer klein is, hierdoor kunnen verwerpen dat beiden aan elkaar gelijk zijn, en we zien dat het gemiddelde van “E” groter is dan dat van “S”. De invloed van de E – treatment is dus positiever/ groter dan die van de S – treatment. Bij de vergelijking van de T – treatment en de E – treatment zien we hetzelfde patroon.

We kunnen dus besluiten dat op korte termijn de example treatment de beste resultaten oplevert.

Post a new message

Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
1	1	'T'
1	1	'T'
0	1	'T'
0	0	'T'
1	1	'T'
1	1	'T'
1	1	'T'
0	1	'T'
0	1	'T'
1	1	'T'
0	0	'T'
0	1	'T'
0	1	'T'
0	1	'T'
0	0	'T'
1	1	'T'
1	1	'T'
1	1	'T'
0	1	'T'
0	0	'T'
1	1	'T'
1	1	'T'
0	0	'T'
1	0	'T'
1	1	'T'
1	0	'T'
1	1	'T'
0	0	'T'
0	0	'T'
1	1	'T'
1	0	'T'
1	1	'T'
0	0	'T'
0	0	'T'
0	0	'T'
1	1	'T'
1	1	'T'
0	1	'E'
0	1	'E'
1	1	'E'
1	1	'E'
1	1	'E'
1	1	'E'
1	1	'E'
0	0	'E'
0	1	'E'
0	1	'E'
1	1	'E'
1	1	'E'
0	0	'E'
0	1	'E'
1	1	'E'
0	1	'E'
0	0	'E'
0	1	'E'
0	1	'E'
0	1	'E'
0	1	'E'
0	0	'E'
0	0	'E'
0	1	'E'
1	1	'E'
1	1	'E'
1	0	'E'
0	0	'E'
0	1	'E'
0	1	'E'
0	0	'E'
1	1	'E'
1	1	'E'
0	1	'S'
0	1	'S'
0	1	'S'
0	1	'S'
1	1	'S'
1	0	'S'
0	0	'S'
1	1	'S'
1	0	'S'
1	1	'S'
0	0	'S'
0	0	'S'
0	0	'S'
1	0	'S'
0	0	'S'
0	0	'S'
1	0	'S'
1	1	'S'
0	0	'S'
0	0	'S'
1	1	'S'
1	1	'S'
1	1	'S'
0	1	'S'
1	1	'S'
1	1	'S'
1	1	'S'
1	1	'S'
0	0	'S'
0	0	'S'
1	1	'S'
0	0	'S'
0	0	'S'
0	0	'S'
0	1	'S'

Tables (Output of Computation)





Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time4 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 & 4 seconds \tabularnewline
R Server & 'RServer@AstonUniversity' @ vre.aston.ac.uk \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90675&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]4 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=90675&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90675&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 time4 seconds
R Server'RServer@AstonUniversity' @ vre.aston.ac.uk






ANOVA Model
Response ~ Treatment_A * Treatment_B
means0.1250.3550.110.1060.0760.123

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Response ~ Treatment_A * Treatment_B \tabularnewline
means & 0.125 & 0.355 & 0.11 & 0.106 & 0.076 & 0.123 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90675&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Response ~ Treatment_A * Treatment_B[/C][/ROW]
[ROW][C]means[/C][C]0.125[/C][C]0.355[/C][C]0.11[/C][C]0.106[/C][C]0.076[/C][C]0.123[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90675&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90675&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
means0.1250.3550.110.1060.0760.123






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
1
Treatment_A13.9073.90718.0410
Treatment_B10.7330.3671.6930.189
Treatment_A:Treatment_B10.0530.0270.1230.885
Residuals9921.440.217 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
 & 1 &  &  &  &  \tabularnewline
Treatment_A & 1 & 3.907 & 3.907 & 18.041 & 0 \tabularnewline
Treatment_B & 1 & 0.733 & 0.367 & 1.693 & 0.189 \tabularnewline
Treatment_A:Treatment_B & 1 & 0.053 & 0.027 & 0.123 & 0.885 \tabularnewline
Residuals & 99 & 21.44 & 0.217 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90675&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]1[/C][C][/C][C][/C][C][/C][C][/C][/ROW]
[ROW][C]Treatment_A[/C][C]1[/C][C]3.907[/C][C]3.907[/C][C]18.041[/C][C]0[/C][/ROW]
[ROW][C]Treatment_B[/C][C]1[/C][C]0.733[/C][C]0.367[/C][C]1.693[/C][C]0.189[/C][/ROW]
[ROW][C]Treatment_A:Treatment_B[/C][C]1[/C][C]0.053[/C][C]0.027[/C][C]0.123[/C][C]0.885[/C][/ROW]
[ROW][C]Residuals[/C][C]99[/C][C]21.44[/C][C]0.217[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90675&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90675&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)
1
Treatment_A13.9073.90718.0410
Treatment_B10.7330.3671.6930.189
Treatment_A:Treatment_B10.0530.0270.1230.885
Residuals9921.440.217






Tukey Honest Significant Difference Comparisons
difflwruprp adj
1-00.4010.2140.5890
S-E0.161-0.1080.430.332
T-E0.19-0.0750.4550.207
T-S0.029-0.2320.2910.961
1:E-0:E0.355-0.1940.9040.422
0:S-0:E0.11-0.470.690.994
1:S-0:E0.542-0.0331.1160.077
0:T-0:E0.106-0.5020.7140.996
1:T-0:E0.5830.0311.1350.032
0:S-1:E-0.245-0.670.180.553
1:S-1:E0.187-0.2310.6050.786
0:T-1:E-0.249-0.7120.2130.623
1:T-1:E0.228-0.1580.6150.524
1:S-0:S0.431-0.0260.8890.076
0:T-0:S-0.005-0.5030.4941
1:T-0:S0.4730.0440.9020.022
0:T-1:S-0.436-0.9280.0560.114
1:T-1:S0.042-0.380.4631
1:T-0:T0.4780.0120.9430.041

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
1-0 & 0.401 & 0.214 & 0.589 & 0 \tabularnewline
S-E & 0.161 & -0.108 & 0.43 & 0.332 \tabularnewline
T-E & 0.19 & -0.075 & 0.455 & 0.207 \tabularnewline
T-S & 0.029 & -0.232 & 0.291 & 0.961 \tabularnewline
1:E-0:E & 0.355 & -0.194 & 0.904 & 0.422 \tabularnewline
0:S-0:E & 0.11 & -0.47 & 0.69 & 0.994 \tabularnewline
1:S-0:E & 0.542 & -0.033 & 1.116 & 0.077 \tabularnewline
0:T-0:E & 0.106 & -0.502 & 0.714 & 0.996 \tabularnewline
1:T-0:E & 0.583 & 0.031 & 1.135 & 0.032 \tabularnewline
0:S-1:E & -0.245 & -0.67 & 0.18 & 0.553 \tabularnewline
1:S-1:E & 0.187 & -0.231 & 0.605 & 0.786 \tabularnewline
0:T-1:E & -0.249 & -0.712 & 0.213 & 0.623 \tabularnewline
1:T-1:E & 0.228 & -0.158 & 0.615 & 0.524 \tabularnewline
1:S-0:S & 0.431 & -0.026 & 0.889 & 0.076 \tabularnewline
0:T-0:S & -0.005 & -0.503 & 0.494 & 1 \tabularnewline
1:T-0:S & 0.473 & 0.044 & 0.902 & 0.022 \tabularnewline
0:T-1:S & -0.436 & -0.928 & 0.056 & 0.114 \tabularnewline
1:T-1:S & 0.042 & -0.38 & 0.463 & 1 \tabularnewline
1:T-0:T & 0.478 & 0.012 & 0.943 & 0.041 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90675&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]1-0[/C][C]0.401[/C][C]0.214[/C][C]0.589[/C][C]0[/C][/ROW]
[ROW][C]S-E[/C][C]0.161[/C][C]-0.108[/C][C]0.43[/C][C]0.332[/C][/ROW]
[ROW][C]T-E[/C][C]0.19[/C][C]-0.075[/C][C]0.455[/C][C]0.207[/C][/ROW]
[ROW][C]T-S[/C][C]0.029[/C][C]-0.232[/C][C]0.291[/C][C]0.961[/C][/ROW]
[ROW][C]1:E-0:E[/C][C]0.355[/C][C]-0.194[/C][C]0.904[/C][C]0.422[/C][/ROW]
[ROW][C]0:S-0:E[/C][C]0.11[/C][C]-0.47[/C][C]0.69[/C][C]0.994[/C][/ROW]
[ROW][C]1:S-0:E[/C][C]0.542[/C][C]-0.033[/C][C]1.116[/C][C]0.077[/C][/ROW]
[ROW][C]0:T-0:E[/C][C]0.106[/C][C]-0.502[/C][C]0.714[/C][C]0.996[/C][/ROW]
[ROW][C]1:T-0:E[/C][C]0.583[/C][C]0.031[/C][C]1.135[/C][C]0.032[/C][/ROW]
[ROW][C]0:S-1:E[/C][C]-0.245[/C][C]-0.67[/C][C]0.18[/C][C]0.553[/C][/ROW]
[ROW][C]1:S-1:E[/C][C]0.187[/C][C]-0.231[/C][C]0.605[/C][C]0.786[/C][/ROW]
[ROW][C]0:T-1:E[/C][C]-0.249[/C][C]-0.712[/C][C]0.213[/C][C]0.623[/C][/ROW]
[ROW][C]1:T-1:E[/C][C]0.228[/C][C]-0.158[/C][C]0.615[/C][C]0.524[/C][/ROW]
[ROW][C]1:S-0:S[/C][C]0.431[/C][C]-0.026[/C][C]0.889[/C][C]0.076[/C][/ROW]
[ROW][C]0:T-0:S[/C][C]-0.005[/C][C]-0.503[/C][C]0.494[/C][C]1[/C][/ROW]
[ROW][C]1:T-0:S[/C][C]0.473[/C][C]0.044[/C][C]0.902[/C][C]0.022[/C][/ROW]
[ROW][C]0:T-1:S[/C][C]-0.436[/C][C]-0.928[/C][C]0.056[/C][C]0.114[/C][/ROW]
[ROW][C]1:T-1:S[/C][C]0.042[/C][C]-0.38[/C][C]0.463[/C][C]1[/C][/ROW]
[ROW][C]1:T-0:T[/C][C]0.478[/C][C]0.012[/C][C]0.943[/C][C]0.041[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90675&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90675&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
1-00.4010.2140.5890
S-E0.161-0.1080.430.332
T-E0.19-0.0750.4550.207
T-S0.029-0.2320.2910.961
1:E-0:E0.355-0.1940.9040.422
0:S-0:E0.11-0.470.690.994
1:S-0:E0.542-0.0331.1160.077
0:T-0:E0.106-0.5020.7140.996
1:T-0:E0.5830.0311.1350.032
0:S-1:E-0.245-0.670.180.553
1:S-1:E0.187-0.2310.6050.786
0:T-1:E-0.249-0.7120.2130.623
1:T-1:E0.228-0.1580.6150.524
1:S-0:S0.431-0.0260.8890.076
0:T-0:S-0.005-0.5030.4941
1:T-0:S0.4730.0440.9020.022
0:T-1:S-0.436-0.9280.0560.114
1:T-1:S0.042-0.380.4631
1:T-0:T0.4780.0120.9430.041






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group51.0980.367
99 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 5 & 1.098 & 0.367 \tabularnewline
  & 99 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=90675&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]1.098[/C][C]0.367[/C][/ROW]
[ROW][C] [/C][C]99[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=90675&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=90675&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)
Group51.0980.367
99


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):
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')