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Description of Statistical Computation

Author's title

Author*The author of this computation has been verified*
R Software Modulerwasp_One Factor ANOVA.wasp
Title produced by softwareOne-Way-Between-Groups ANOVA- Free Statistics Software (Calculator)
Date of computationMon, 17 Dec 2018 22:26:11 +0100
Cite this page as followsStatistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?v=date/2018/Dec/17/t1545081991nofl62yuwakc5oz.htm/, Retrieved Thu, 02 May 2024 00:43:03 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=315923, Retrieved Thu, 02 May 2024 00:43:03 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact165

Tree of Dependent Computations

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)] [] [2018-12-17 21:26:11] [63a9f0ea7bb98050796b649e85481845] [Current]
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Dataset

Dataseries X:
Download CSV
Histogram
Boxplots 
0.79	'Middle_East/Central_Asia'
2.21	'Northern/Eastern_Europe'
2.12	'Africa'
0.93	'Africa'
5.38	'Latin_America'
3.14	'Latin_America'
2.23	'Middle_East/Central_Asia'
11.88	'Latin_America'
9.31	'Asia-Pacific'
6.06	'European_Union'
2.31	'Middle_East/Central_Asia'
6.84	'Latin_America'
7.49	'Middle_East/Central_Asia'
0.72	'Asia-Pacific'
4.48	'Latin_America'
5.09	'Northern/Eastern_Europe'
7.44	'European_Union'
1.41	'Africa'
5.77	'North_America'
4.84	'Asia-Pacific'
2.96	'Latin_America'
3.12	'Northern/Eastern_Europe'
3.83	'Africa'
3.11	'Latin_America'
2.86	'Latin_America'
4.06	'Asia-Pacific'
3.32	'European_Union'
1.21	'Africa'
0.8	'Africa'
2.52	'Africa'
1.21	'Asia-Pacific'
1.17	'Africa'
8.17	'North_America'
5.65	'Latin_America'
1.24	'Africa'
1.46	'Africa'
4.36	'Latin_America'
3.38	'Asia-Pacific'
1.87	'Latin_America'
1.03	'Africa'
1.29	'Africa'
0.82	'Africa'
2.84	'Latin_America'
1.27	'Africa'
3.92	'Northern/Eastern_Europe'
1.95	'Latin_America'
4.21	'European_Union'
5.19	'European_Union'
5.51	'European_Union'
2.19	'Africa'
2.57	'Latin_America'
1.53	'Latin_America'
2.17	'Latin_America'
2.15	'Africa'
2.07	'Latin_America'
3.97	'Africa'
0.42	'Africa'
6.86	'European_Union'
1.02	'Africa'
2.9	'Asia-Pacific'
5.87	'European_Union'
5.14	'European_Union'
2.34	'Latin_America'
4.73	'Asia-Pacific'
2.02	'Africa'
1.03	'Africa'
1.58	'Middle_East/Central_Asia'
5.3	'European_Union'
1.97	'Africa'
4.38	'European_Union'
2.98	'Latin_America'
3.23	'Latin_America'
1.89	'Latin_America'
1.41	'Africa'
1.53	'Africa'
3.07	'Latin_America'
0.61	'Latin_America'
1.68	'Latin_America'
2.92	'European_Union'
1.16	'Asia-Pacific'
1.58	'Asia-Pacific'
2.79	'Middle_East/Central_Asia'
1.88	'Middle_East/Central_Asia'
5.57	'European_Union'
6.22	'Middle_East/Central_Asia'
4.61	'European_Union'
1.89	'Latin_America'
5.02	'Asia-Pacific'
2.1	'Middle_East/Central_Asia'
5.55	'Middle_East/Central_Asia'
1.03	'Africa'
1.17	'Asia-Pacific'
5.69	'Asia-Pacific'
8.13	'Middle_East/Central_Asia'
1.91	'Middle_East/Central_Asia'
1.22	'Asia-Pacific'
6.29	'European_Union'
3.84	'Middle_East/Central_Asia'
1.66	'Africa'
1.21	'Africa'
3.69	'Africa'
5.83	'European_Union'
15.82	'European_Union'
3.26	'Northern/Eastern_Europe'
0.99	'Africa'
0.81	'Africa'
3.71	'Asia-Pacific'
1.53	'Africa'
2.08	'Latin_America'
2.54	'Africa'
3.46	'Africa'
2.89	'Latin_America'
1.78	'Northern/Eastern_Europe'
6.08	'Asia-Pacific'
3.78	'Northern/Eastern_Europe'
7.78	'Latin_America'
1.68	'Africa'
0.87	'Africa'
1.43	'Asia-Pacific'
2.48	'Africa'
2.94	'Asia-Pacific'
0.98	'Asia-Pacific'
5.28	'European_Union'
3.58	'Asia-Pacific'
5.6	'Asia-Pacific'
1.39	'Latin_America'
1.56	'Africa'
1.16	'Africa'
4.98	'Northern/Eastern_Europe'
7.52	'Middle_East/Central_Asia'
0.79	'Asia-Pacific'
2.79	'Latin_America'
1.91	'Asia-Pacific'
4.16	'Latin_America'
2.28	'Latin_America'
1.1	'Asia-Pacific'
4.44	'European_Union'
3.88	'European_Union'
10.8	'Middle_East/Central_Asia'
3.65	'Africa'
2.71	'European_Union'
5.69	'Northern/Eastern_Europe'
0.87	'Africa'
4.94	'Latin_America'
2.45	'Latin_America'
3.11	'Latin_America'
2.77	'Asia-Pacific'
1.49	'Africa'
5.61	'Middle_East/Central_Asia'
1.21	'Africa'
2.7	'Northern/Eastern_Europe'
1.24	'Africa'
7.97	'Asia-Pacific'
4.06	'European_Union'
5.81	'European_Union'
1.29	'Asia-Pacific'
1.24	'Africa'
3.31	'Africa'
3.67	'European_Union'
1.32	'Asia-Pacific'
4.25	'Latin_America'
2.01	'Africa'
7.25	'European_Union'
5.79	'Northern/Eastern_Europe'
1.51	'Middle_East/Central_Asia'
0.91	'Middle_East/Central_Asia'
1.32	'Africa'
2.66	'Asia-Pacific'
0.48	'Asia-Pacific'
1.13	'Africa'
2.7	'Asia-Pacific'
7.92	'Latin_America'
2.34	'Africa'
3.33	'Middle_East/Central_Asia'
5.47	'Middle_East/Central_Asia'
1.24	'Africa'
2.84	'Northern/Eastern_Europe'
4.94	'European_Union'
7.93	'Middle_East/Central_Asia'
8.22	'North_America'
2.91	'Latin_America'
2.32	'Middle_East/Central_Asia'
3.57	'Latin_America'
1.65	'Asia-Pacific'
2.07	'Asia-Pacific'
1.03	'Middle_East/Central_Asia'
0.99	'Africa'
1.37	'Africa'

Tables (Output of Computation)





Summary of computational transaction
Raw Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center

\begin{tabular}{lllllllll}
\hline
Summary of computational transaction \tabularnewline
Raw Input view raw input (R code)  \tabularnewline
Raw Outputview raw output of R engine  \tabularnewline
Computing time4 seconds \tabularnewline
R ServerBig Analytics Cloud Computing Center \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315923&T=0

[TABLE]
[ROW]
Summary of computational transaction[/C][/ROW] [ROW]Raw Input[/C] view raw input (R code) [/C][/ROW] [ROW]Raw Output[/C]view raw output of R engine [/C][/ROW] [ROW]Computing time[/C]4 seconds[/C][/ROW] [ROW]R Server[/C]Big Analytics Cloud Computing Center[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=315923&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315923&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 Input view raw input (R code)
Raw Outputview raw output of R engine
Computing time4 seconds
R ServerBig Analytics Cloud Computing Center






ANOVA Model
Total_Ecological_Footprint ~ Region
means1.6711.2993.8041.8132.3835.7162.092

\begin{tabular}{lllllllll}
\hline
ANOVA Model \tabularnewline
Total_Ecological_Footprint  ~  Region \tabularnewline
means & 1.671 & 1.299 & 3.804 & 1.813 & 2.383 & 5.716 & 2.092 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315923&T=1

[TABLE]
[ROW][C]ANOVA Model[/C][/ROW]
[ROW][C]Total_Ecological_Footprint  ~  Region[/C][/ROW]
[ROW][C]means[/C][C]1.671[/C][C]1.299[/C][C]3.804[/C][C]1.813[/C][C]2.383[/C][C]5.716[/C][C]2.092[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315923&T=1

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315923&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
Total_Ecological_Footprint ~ Region
means1.6711.2993.8041.8132.3835.7162.092






ANOVA Statistics
DfSum SqMean SqF valuePr(>F)
Region6331.64955.27513.9040
Residuals181719.5373.975 

\begin{tabular}{lllllllll}
\hline
ANOVA Statistics \tabularnewline
  & Df & Sum Sq & Mean Sq & F value & Pr(>F) \tabularnewline
Region & 6 & 331.649 & 55.275 & 13.904 & 0 \tabularnewline
Residuals & 181 & 719.537 & 3.975 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315923&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]Region[/C][C]6[/C][C]331.649[/C][C]55.275[/C][C]13.904[/C][C]0[/C][/ROW]
[ROW][C]Residuals[/C][C]181[/C][C]719.537[/C][C]3.975[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315923&T=2

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315923&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)
Region6331.64955.27513.9040
Residuals181719.5373.975






Tukey Honest Significant Difference Comparisons
difflwruprp adj
Asia-Pacific-Africa1.299-0.0242.6230.058
European_Union-Africa3.8042.3765.2320
Latin_America-Africa1.8130.5543.0720.001
Middle_East/Central_Asia-Africa2.3830.8953.8720
North_America-Africa5.7162.1869.2460
Northern/Eastern_Europe-Africa2.0920.1883.9960.021
European_Union-Asia-Pacific2.5050.9464.0640
Latin_America-Asia-Pacific0.514-0.8931.920.931
Middle_East/Central_Asia-Asia-Pacific1.084-0.5312.6990.417
North_America-Asia-Pacific4.4160.8318.0010.006
Northern/Eastern_Europe-Asia-Pacific0.793-1.2112.7970.901
Latin_America-European_Union-1.992-3.497-0.4860.002
Middle_East/Central_Asia-European_Union-1.421-3.1230.2810.169
North_America-European_Union1.911-1.7145.5360.7
Northern/Eastern_Europe-European_Union-1.712-3.7870.3630.18
Middle_East/Central_Asia-Latin_America0.571-0.9932.1340.931
North_America-Latin_America3.9030.3417.4650.022
Northern/Eastern_Europe-Latin_America0.279-1.6832.2421
North_America-Middle_East/Central_Asia3.332-0.3176.9820.099
Northern/Eastern_Europe-Middle_East/Central_Asia-0.291-2.4081.8261
Northern/Eastern_Europe-North_America-3.623-7.4610.2140.078

\begin{tabular}{lllllllll}
\hline
Tukey Honest Significant Difference Comparisons \tabularnewline
  & diff & lwr & upr & p adj \tabularnewline
Asia-Pacific-Africa & 1.299 & -0.024 & 2.623 & 0.058 \tabularnewline
European_Union-Africa & 3.804 & 2.376 & 5.232 & 0 \tabularnewline
Latin_America-Africa & 1.813 & 0.554 & 3.072 & 0.001 \tabularnewline
Middle_East/Central_Asia-Africa & 2.383 & 0.895 & 3.872 & 0 \tabularnewline
North_America-Africa & 5.716 & 2.186 & 9.246 & 0 \tabularnewline
Northern/Eastern_Europe-Africa & 2.092 & 0.188 & 3.996 & 0.021 \tabularnewline
European_Union-Asia-Pacific & 2.505 & 0.946 & 4.064 & 0 \tabularnewline
Latin_America-Asia-Pacific & 0.514 & -0.893 & 1.92 & 0.931 \tabularnewline
Middle_East/Central_Asia-Asia-Pacific & 1.084 & -0.531 & 2.699 & 0.417 \tabularnewline
North_America-Asia-Pacific & 4.416 & 0.831 & 8.001 & 0.006 \tabularnewline
Northern/Eastern_Europe-Asia-Pacific & 0.793 & -1.211 & 2.797 & 0.901 \tabularnewline
Latin_America-European_Union & -1.992 & -3.497 & -0.486 & 0.002 \tabularnewline
Middle_East/Central_Asia-European_Union & -1.421 & -3.123 & 0.281 & 0.169 \tabularnewline
North_America-European_Union & 1.911 & -1.714 & 5.536 & 0.7 \tabularnewline
Northern/Eastern_Europe-European_Union & -1.712 & -3.787 & 0.363 & 0.18 \tabularnewline
Middle_East/Central_Asia-Latin_America & 0.571 & -0.993 & 2.134 & 0.931 \tabularnewline
North_America-Latin_America & 3.903 & 0.341 & 7.465 & 0.022 \tabularnewline
Northern/Eastern_Europe-Latin_America & 0.279 & -1.683 & 2.242 & 1 \tabularnewline
North_America-Middle_East/Central_Asia & 3.332 & -0.317 & 6.982 & 0.099 \tabularnewline
Northern/Eastern_Europe-Middle_East/Central_Asia & -0.291 & -2.408 & 1.826 & 1 \tabularnewline
Northern/Eastern_Europe-North_America & -3.623 & -7.461 & 0.214 & 0.078 \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315923&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]Asia-Pacific-Africa[/C][C]1.299[/C][C]-0.024[/C][C]2.623[/C][C]0.058[/C][/ROW]
[ROW][C]European_Union-Africa[/C][C]3.804[/C][C]2.376[/C][C]5.232[/C][C]0[/C][/ROW]
[ROW][C]Latin_America-Africa[/C][C]1.813[/C][C]0.554[/C][C]3.072[/C][C]0.001[/C][/ROW]
[ROW][C]Middle_East/Central_Asia-Africa[/C][C]2.383[/C][C]0.895[/C][C]3.872[/C][C]0[/C][/ROW]
[ROW][C]North_America-Africa[/C][C]5.716[/C][C]2.186[/C][C]9.246[/C][C]0[/C][/ROW]
[ROW][C]Northern/Eastern_Europe-Africa[/C][C]2.092[/C][C]0.188[/C][C]3.996[/C][C]0.021[/C][/ROW]
[ROW][C]European_Union-Asia-Pacific[/C][C]2.505[/C][C]0.946[/C][C]4.064[/C][C]0[/C][/ROW]
[ROW][C]Latin_America-Asia-Pacific[/C][C]0.514[/C][C]-0.893[/C][C]1.92[/C][C]0.931[/C][/ROW]
[ROW][C]Middle_East/Central_Asia-Asia-Pacific[/C][C]1.084[/C][C]-0.531[/C][C]2.699[/C][C]0.417[/C][/ROW]
[ROW][C]North_America-Asia-Pacific[/C][C]4.416[/C][C]0.831[/C][C]8.001[/C][C]0.006[/C][/ROW]
[ROW][C]Northern/Eastern_Europe-Asia-Pacific[/C][C]0.793[/C][C]-1.211[/C][C]2.797[/C][C]0.901[/C][/ROW]
[ROW][C]Latin_America-European_Union[/C][C]-1.992[/C][C]-3.497[/C][C]-0.486[/C][C]0.002[/C][/ROW]
[ROW][C]Middle_East/Central_Asia-European_Union[/C][C]-1.421[/C][C]-3.123[/C][C]0.281[/C][C]0.169[/C][/ROW]
[ROW][C]North_America-European_Union[/C][C]1.911[/C][C]-1.714[/C][C]5.536[/C][C]0.7[/C][/ROW]
[ROW][C]Northern/Eastern_Europe-European_Union[/C][C]-1.712[/C][C]-3.787[/C][C]0.363[/C][C]0.18[/C][/ROW]
[ROW][C]Middle_East/Central_Asia-Latin_America[/C][C]0.571[/C][C]-0.993[/C][C]2.134[/C][C]0.931[/C][/ROW]
[ROW][C]North_America-Latin_America[/C][C]3.903[/C][C]0.341[/C][C]7.465[/C][C]0.022[/C][/ROW]
[ROW][C]Northern/Eastern_Europe-Latin_America[/C][C]0.279[/C][C]-1.683[/C][C]2.242[/C][C]1[/C][/ROW]
[ROW][C]North_America-Middle_East/Central_Asia[/C][C]3.332[/C][C]-0.317[/C][C]6.982[/C][C]0.099[/C][/ROW]
[ROW][C]Northern/Eastern_Europe-Middle_East/Central_Asia[/C][C]-0.291[/C][C]-2.408[/C][C]1.826[/C][C]1[/C][/ROW]
[ROW][C]Northern/Eastern_Europe-North_America[/C][C]-3.623[/C][C]-7.461[/C][C]0.214[/C][C]0.078[/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315923&T=3

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315923&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
Asia-Pacific-Africa1.299-0.0242.6230.058
European_Union-Africa3.8042.3765.2320
Latin_America-Africa1.8130.5543.0720.001
Middle_East/Central_Asia-Africa2.3830.8953.8720
North_America-Africa5.7162.1869.2460
Northern/Eastern_Europe-Africa2.0920.1883.9960.021
European_Union-Asia-Pacific2.5050.9464.0640
Latin_America-Asia-Pacific0.514-0.8931.920.931
Middle_East/Central_Asia-Asia-Pacific1.084-0.5312.6990.417
North_America-Asia-Pacific4.4160.8318.0010.006
Northern/Eastern_Europe-Asia-Pacific0.793-1.2112.7970.901
Latin_America-European_Union-1.992-3.497-0.4860.002
Middle_East/Central_Asia-European_Union-1.421-3.1230.2810.169
North_America-European_Union1.911-1.7145.5360.7
Northern/Eastern_Europe-European_Union-1.712-3.7870.3630.18
Middle_East/Central_Asia-Latin_America0.571-0.9932.1340.931
North_America-Latin_America3.9030.3417.4650.022
Northern/Eastern_Europe-Latin_America0.279-1.6832.2421
North_America-Middle_East/Central_Asia3.332-0.3176.9820.099
Northern/Eastern_Europe-Middle_East/Central_Asia-0.291-2.4081.8261
Northern/Eastern_Europe-North_America-3.623-7.4610.2140.078






Levenes Test for Homogeneity of Variance
DfF valuePr(>F)
Group63.9990.001
181 

\begin{tabular}{lllllllll}
\hline
Levenes Test for Homogeneity of Variance \tabularnewline
  & Df & F value & Pr(>F) \tabularnewline
Group & 6 & 3.999 & 0.001 \tabularnewline
  & 181 &   &   \tabularnewline
\hline
\end{tabular}
%Source: https://freestatistics.org/blog/index.php?pk=315923&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]6[/C][C]3.999[/C][C]0.001[/C][/ROW]
[ROW][C] [/C][C]181[/C][C] [/C][C] [/C][/ROW]
[/TABLE]
Source: https://freestatistics.org/blog/index.php?pk=315923&T=4

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=315923&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)
Group63.9990.001
181


Figures (Output of Computation)

Input Parameters & R Code

Parameters (Session):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
Parameters (R input):
par1 = 1 ; par2 = 2 ; par3 = TRUE ;
R code (references can be found in the software module):
par3 <- 'TRUE'
par2 <- '2'
par1 <- '1'
cat1 <- as.numeric(par1) #
cat2<- as.numeric(par2) #
intercept<-as.logical(par3)
x <- t(x)
x1<-as.numeric(x[,cat1])
f1<-as.character(x[,cat2])
xdf<-data.frame(x1,f1)
(V1<-dimnames(y)[[1]][cat1])
(V2<-dimnames(y)[[1]][cat2])
names(xdf)<-c('Response', 'Treatment')
if(intercept == FALSE) (lmxdf<-lm(Response ~ Treatment - 1, data = xdf) ) else (lmxdf<-lm(Response ~ Treatment, 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$coefficients)+1,TRUE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, paste(V1, ' ~ ', V2), length(lmxdf$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)
a<-table.row.start(a)
a<-table.element(a, V2,,TRUE)
a<-table.element(a, anova.xdf$Df[1],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'F value'[1], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Pr(>F)'[1], digits=3),,FALSE)
a<-table.row.end(a)
a<-table.row.start(a)
a<-table.element(a, 'Residuals',,TRUE)
a<-table.element(a, anova.xdf$Df[2],,FALSE)
a<-table.element(a, round(anova.xdf$'Sum Sq'[2], digits=3),,FALSE)
a<-table.element(a, round(anova.xdf$'Mean Sq'[2], 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, data=xdf, xlab=V2, ylab=V1)
dev.off()
if(intercept==TRUE){
'Tukey Plot'
thsd<-TukeyHSD(aov.xdf)
bitmap(file='TukeyHSDPlot.png')
plot(thsd)
dev.off()
}
if(intercept==TRUE){
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(i in 1:length(rownames(thsd[[1]]))){
a<-table.row.start(a)
a<-table.element(a,rownames(thsd[[1]])[i], 1, TRUE)
for(j in 1:4){
a<-table.element(a,round(thsd[[1]][i,j], digits=3), 1, FALSE)
}
a<-table.row.end(a)
}
a<-table.end(a)
table.save(a,file='mytable2.tab')
}
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<-leveneTest(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')