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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 computationFri, 27 Nov 2015 10:57:30 +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/2015/Nov/27/t1448621871xzoyflt1ol8m2l8.htm/, Retrieved Wed, 15 May 2024 04:03:23 +0000
Statistical Computations at FreeStatistics.org, Office for Research Development and Education, URL https://freestatistics.org/blog/index.php?pk=284302, Retrieved Wed, 15 May 2024 04:03:23 +0000
QR Codes:

Original text written by user:
IsPrivate?No (this computation is public)
User-defined keywords
Estimated Impact132
Family? (F = Feedback message, R = changed R code, M = changed R Module, P = changed Parameters, D = changed Data)
-       [Two-Way ANOVA] [] [2015-11-27 10:57:30] [11e09077693c238f0a6e6f4d2cf77105] [Current]
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Dataseries X:
NA 501
NA 488
NA 504
NA 578
NA 545
NA 632
NA 728
NA 725
NA 585
NA 542
NA 480
NA 530
NA 518
NA 489
NA 528
NA 599
NA 572
NA 659
NA 739
NA 758
NA 602
NA 587
NA 497
NA 558
NA 555
NA 523
NA 532
NA 623
NA 598
NA 683
NA 774
NA 780
NA 609
NA 604
NA 531
NA 592
76.83 578
77.74 543
80.47 565
79.56 648
82.28 615
100.92 697
113.2 785
90.92 830
86.83 645
82.74 643
83.65 551
80.92 606
83.19 585
83.65 553
83.65 576
83.65 665
86.83 656
100.47 720
91.38 826
101.38 838
95.92 652
88.19 661
88.19 584
80.47 644
80.92 623
79.56 553
80.92 599
88.19 657
91.83 680
96.38 759
97.29 878
102.29 881
99.1 705
92.74 684
87.29 577
85.47 656
91.38 645
92.74 593
89.56 617
88.65 686
93.2 679
99.56 773
109.11 906
124.56 934
115.47 713
96.38 710
92.29 600
86.83 676
87.29 645
85.92 602
85.92 601
88.65 709
91.83 706
112.29 817
101.83 930
125.02 983
102.74 745
95.01 735
91.83 620
86.38 698
87.29 665
88.19 626
89.1 649
89.1 740
103.65 729
127.75 824
125.47 937
125.47 994
109.11 781
100.01 759
95.01 643
85.01 728
86.83 691
86.83 649
86.83 656
86.83 735
100.47 748
111.38 837
105.47 995
102.74 1040
105.01 809
96.38 793
94.1 692
86.83 763
92.74 723
93.2 655
95.47 658
96.38 761
99.56 768
120.47 885
123.2 1067
114.11 1038
120.93 812
102.74 790
101.83 692
95.47 782
100.01 758
100.01 709
98.2 715
100.01 788
103.65 794
114.56 893
134.11 1046
131.84 1075
113.65 812
107.29 822
102.29 714
94.56 802
97.29 748
98.2 731
95.47 748
100.47 827
116.38 788
117.29 937
140.93 1076
120.02 1125
111.38 840
108.65 864
105.92 717
99.1 813
101.83 811
102.74 732
102.74 745
105.47 844
108.65 833
139.57 935
110.47 1110
118.65 1124
120.02 868
109.11 860
108.2 762
101.38 877
106.38 NA
108.65 NA
107.74 NA
105.92 NA
129.56 NA
139.11 NA
125.93 NA
123.65 NA
118.65 NA
110.47 NA
110.02 NA
100.47 NA
104.1 NA
106.6 NA
105.5 NA
107.5 NA
117.9 NA
136.3 NA
156.8 NA
135.8 NA
130 NA
117.5 NA
115.8 NA
105.5 NA
111.6 NA
113.2 NA
113.1 NA
112.5 NA
120 NA
147.6 NA
149.9 NA
131.2 NA
134.6 NA
122.2 NA
117.7 NA
106.8 NA
111.5 NA
111.3 NA
109.5 NA
112.1 NA
127 NA
135.9 NA
150.4 NA
135.6 NA
134.9 NA
124.1 NA
120.8 NA
112.8 NA
117.4 NA
118.6 NA
119.2 NA
119.7 NA
128.6 NA
142.8 NA
170 NA
145.9 NA
140.1 NA
128.7 NA
123.4 NA
114.6 NA
120.2 NA
122 NA
121.3 NA
123.2 NA
141.1 NA
129.7 NA
152.4 NA
141.9 NA
137 NA
129 NA
124.6 NA
117.3 NA
122.7 NA
121 NA
122 NA
122 NA
126.3 NA
158.1 NA
164.9 NA
143.3 NA
151.4 NA
136.8 NA
133.1 NA
124.8 NA
132.6 NA
130.2 NA
129.6 NA
129.7 NA
133.7 NA
148.3 NA
155.1 NA
157.2 NA
147.2 NA
142.7 NA
135.9 NA
123.8 NA
132.3 NA
132.7 NA
130.7 NA
129.9 NA
145.5 NA
156.6 NA
161.7 NA
156 NA
146.1 NA
136.8 NA
132.5 NA
129.5 NA
129.5 NA
134.7 NA
136.6 NA
138.4 NA
149.6 NA
159.5 NA
171.4 NA
162.1 NA
163.1 NA
152.4 NA
145.5 NA
133.9 NA
136.6 NA
139.4 NA
141.2 NA
144.9 NA
181.4 NA
187 NA
211.4 NA
178.1 NA
168 NA
154.4 NA
150.4 NA
139.4 NA
144.7 NA
143 NA
148.3 NA
152.7 NA
173.3 NA
226.3 NA
218.2 NA
184.6 NA
174.9 NA
161.4 NA
161.4 NA
145.8 NA




Summary of computational transaction
Raw Inputview raw input (R code)
Raw Outputview raw output of R engine
Computing time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Engine error message
Error in x[, cat3] : subscript out of bounds
Execution halted

\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 & 1 seconds \tabularnewline
R Server & 'Herman Ole Andreas Wold' @ wold.wessa.net \tabularnewline
R Engine error message & 
Error in x[, cat3] : subscript out of bounds
Execution halted
\tabularnewline \hline \end{tabular} %Source: https://freestatistics.org/blog/index.php?pk=284302&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]1 seconds[/C][/ROW]
[ROW][C]R Server[/C][C]'Herman Ole Andreas Wold' @ wold.wessa.net[/C][/ROW]
[ROW][C]R Engine error message[/C][C]
Error in x[, cat3] : subscript out of bounds
Execution halted
[/C][/ROW] [/TABLE] Source: https://freestatistics.org/blog/index.php?pk=284302&T=0

Globally Unique Identifier (entire table): ba.freestatistics.org/blog/index.php?pk=284302&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 time1 seconds
R Server'Herman Ole Andreas Wold' @ wold.wessa.net
R Engine error message
Error in x[, cat3] : subscript out of bounds
Execution halted



Parameters (Session):
par1 = Default ; par2 = 1 ; par3 = 0 ; par4 = 0 ; par5 = 12 ; par6 = White Noise ; par7 = 0.95 ;
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')