| Title: | Analysis of Variance and Other Important Complementary Analyses |
|---|---|
| Description: | Perform analysis of variance and other important complementary analyses. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data. |
| Authors: | Emmanuel Arnhold [aut, cre] |
| Maintainer: | Emmanuel Arnhold <[email protected]> |
| License: | GPL-2 |
| Version: | 11.0 |
| Built: | 2024-12-16 06:58:09 UTC |
| Source: | CRAN |
Perform analysis of variance and other important complementary analyzes. The functions are easy to use. Performs analysis in various designs, with balanced and unbalanced data.
| Package: | easyanova |
| Type: | Package |
| Version: | 11.0 |
| Date: | 2024-09-14 |
| License: | GPL-2 |
Emmanuel Arnhold <[email protected]>
CRUZ, C.D. and CARNEIRO, P.C.S. Modelos biometricos aplicados ao melhoramento genetico. 2nd Edition. Vicosa, UFV, v.2, 2006. 585p.
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
SANDERS W.L. and GAYNOR, P.J. Analysis of switchback data using Statistical Analysis System, Inc. Software. Journal of Dairy Science, 70.2186-2191. 1987.
PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.
RAMALHO, M. A. P.; FERREIRA, D. F. and OLIVEIRA, A. C. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA, 2005, 322p.
ea1, ea2, ec
# Kaps and Lamberson(2009) data(data1) data(data2) data(data3) data(data4) # analysis in completely randomized design r1<-ea1(data1, design=1) names(r1) r1 # analysis in randomized block design r2<-ea1(data2, design=2) # analysis in latin square design r3<-ea1(data3, design=3) # analysis in several latin squares design r4<-ea1(data4, design=4) r1[1] r2[1] r3[1] r4[1] # analysis in unbalanced randomized block design response<-ifelse(data2$Gain>850, NA, data2$Gain) ndata<-data.frame(data2[-3],response) ndata r5<-ea1(ndata, design=2 ) r5 # multivariable response (list argument = TRUE) t<-c('a','a','a','b','b','b','c','c','c') r1<-c(10,12,12.8,4,6,8,14,15,16) r2<-c(102,105,106,125,123,124,99,95,96) r3<-c(560,589,590,658,678,629,369,389,378) d<-data.frame(t,r1,r2,r3) results=ea1(d, design=1, list=TRUE) names(results) results results[1][[1]] names(results[1][[1]])# Kaps and Lamberson(2009) data(data1) data(data2) data(data3) data(data4) # analysis in completely randomized design r1<-ea1(data1, design=1) names(r1) r1 # analysis in randomized block design r2<-ea1(data2, design=2) # analysis in latin square design r3<-ea1(data3, design=3) # analysis in several latin squares design r4<-ea1(data4, design=4) r1[1] r2[1] r3[1] r4[1] # analysis in unbalanced randomized block design response<-ifelse(data2$Gain>850, NA, data2$Gain) ndata<-data.frame(data2[-3],response) ndata r5<-ea1(ndata, design=2 ) r5 # multivariable response (list argument = TRUE) t<-c('a','a','a','b','b','b','c','c','c') r1<-c(10,12,12.8,4,6,8,14,15,16) r2<-c(102,105,106,125,123,124,99,95,96) r3<-c(560,589,590,658,678,629,369,389,378) d<-data.frame(t,r1,r2,r3) results=ea1(d, design=1, list=TRUE) names(results) results results[1][[1]] names(results[1][[1]])
Plot quartis
box.plot(data,test=1, xlab=NULL, ylab=NULL,legend=TRUE, letters=TRUE, family="Times", bg="white", cex.axis=0.7,...)box.plot(data,test=1, xlab=NULL, ylab=NULL,legend=TRUE, letters=TRUE, family="Times", bg="white", cex.axis=0.7,...)
data |
data.frame with data (see examples) |
test |
type of test 1 = Kruskall-Wallis 2 = Friedman |
xlab |
name of x-axis |
ylab |
name of y-axis |
legend |
TRUE = plot p-value of test FALSE = not plot p-value |
letters |
TRUE = plot letters FALSE = not plot letters |
family |
font of plot |
bg |
background color |
cex.axis |
font size in the axis |
... |
more plot parameters |
Returns box plots and test of Kruskall-Wallis and Friedman
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2, means.plot
#data3: Kaps and Lamberson (2009) #Description: ##The experiment compared three diets for pigs in a completely randomized design data(data1) # Kruskal-Wallis test box.plot(data1, test=1) #Description #Complete randomized block design to determine the average daily gain of steers data(data2) box.plot(data2, test=2) #More plot parameters box.plot(data2, test=2, col=c(2,7,3), col.axis="red",las=1, legend=FALSE, bg="cornsilk", sub="Treatments", cex=1.2);grid(10, lwd=1.5)#data3: Kaps and Lamberson (2009) #Description: ##The experiment compared three diets for pigs in a completely randomized design data(data1) # Kruskal-Wallis test box.plot(data1, test=1) #Description #Complete randomized block design to determine the average daily gain of steers data(data2) box.plot(data2, test=2) #More plot parameters box.plot(data2, test=2, col=c(2,7,3), col.axis="red",las=1, legend=FALSE, bg="cornsilk", sub="Treatments", cex=1.2);grid(10, lwd=1.5)
The experiment compared three diets for pigs in a completely randomized design
data(data1)data(data1)
A data frame with 15 observations on the following 2 variables.
Dieta factor with levels d1 d2 d3
Gaina numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data1) summary(data1)data(data1) summary(data1)
Completely randomized design with a covariate.The effect of three diets on daily gain of steers was investigated. The design was a completely randomized design. Weight at the beginning of the experiment (initial weight) was recorded, but not used in the assignment of animals to diet.
data(data10)data(data10)
A data frame with 15 observations on the following 4 variables.
Dietsa factor with levels A B C
Initial_weighta numeric vector
Repetitionsa numeric vector
Gaina numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data10) summary(data10)data(data10) summary(data10)
Incomplete block design
data(data11)data(data11)
A data frame with 56 observations on the following 4 variables.
treatmentsa numeric vector
repa numeric vector
blocksa numeric vector
yielda numeric vector
PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.
data(data11) summary(data11)data(data11) summary(data11)
Incomplete block design
data(data12)data(data12)
A data frame with 42 observations on the following 4 variables.
treatmentsa numeric vector
repa numeric vector
blocksa numeric vector
yielda numeric vector
PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.
data(data12) summary(data12)data(data12) summary(data12)
Incomplete block design
data(data13)data(data13)
A data frame with 23 observations on the following 3 variables.
genotypesa factor with levels f1 f10 f11 f12 f13 f14 f2 f3 f4 f5 f6 f7 f8 f9 test1 test2 test3
blocksa factor with levels b1 b2 b3
yielda numeric vector
CRUZ, C.D. and CARNEIRO, P.C.S. Modelos biometricos aplicados ao melhoramento genetico. 2nd Edition. Vicosa, UFV, v.2, 2006. 585p.
data(data13) summary(data13)data(data13) summary(data13)
Incomplete block design in animals
data(data14)data(data14)
A data frame with 28 observations on the following 4 variables.
treatmenta factor with levels A B C D E F G
animala factor with levels A1 A2 A3 A4 A5 A6 A7
perioda factor with levels P1 P2 P3 P4
responsea numeric vector
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
data(data14) summary(data14)data(data14) summary(data14)
Lattice design
data(data15)data(data15)
A data frame with 48 observations on the following 4 variables.
treatmentsa numeric vector
repa numeric vector
blocksa numeric vector
yielda numeric vector
PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.
data(data15) summary(data15)data(data15) summary(data15)
Switchback design
data(data16)data(data16)
A data frame with 36 observations on the following 4 variables.
treatmenta factor with levels A B C
perioda numeric vector
animala numeric vector
gaina numeric vector
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
data(data16) summary(data16)data(data16) summary(data16)
Switchback design
data(data17)data(data17)
A data frame with 36 observations on the following 5 variables.
treatmentsa numeric vector
blocksa factor with levels b1 b2 b3
perioda numeric vector
animala numeric vector
gaina numeric vector
SANDERS W.L. and GAYNOR, P.J. Analysis of switchback data using Statistical Analysis System, Inc. Software. Journal of Dairy Science, 70.2186-2191. 1987.
data(data17) summary(data17)data(data17) summary(data17)
Repetition of experiments in block design
data(data18)data(data18)
A data frame with 60 observations on the following 4 variables.
treatmentsa numeric vector
experimentsa numeric vector
blocksa numeric vector
responsea numeric vector
RAMALHO, M. A. P.; FERREIRA, D. F. and OLIVEIRA, A. C. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA, 2005, 322p.
data(data18) summary(data18)data(data18) summary(data18)
Repetition of latin square design
data(data19)data(data19)
A data frame with 32 observations on the following 5 variables.
treatmentsa factor with levels A B C D
squaresa factor with levels 1 2
rowsa factor with levels 1 2 3 4
columnsa factor with levels 1 2 3 4
responsea numeric vector
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
data(data19) summary(data19)data(data19) summary(data19)
Complete randomized block design to determine the average daily gain of steers
data(data2)data(data2)
A data frame with 12 observations on the following 3 variables.
Treatmentsa factor with levels t1 t2 t3
Blocksa factor with levels b1 b2 b3 b4
Gaina numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data2) summary(data2)data(data2) summary(data2)
Latin square design for test four different treatments on hay intake of fattening steers
data(data3)data(data3)
A data frame with 16 observations on the following 4 variables.
treatmenta factor with levels A B C D
perioda factor with levels p1 p2 p3 p4
steera factor with levels a1 a2 a3 a4
responsea numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data3) summary(data3)data(data3) summary(data3)
Two latin squares design for test four different treatments on hay intake of fattening steers
data(data4)data(data4)
A data frame with 32 observations on the following 5 variables.
dieta factor with levels A B C D
squarea numeric vector
steera numeric vector
perioda numeric vector
responsea numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data4) summary(data4)data(data4) summary(data4)
Factorial in randomized design for testing two vitamins in feed of pigs
data(data5)data(data5)
A data frame with 20 observations on the following 3 variables.
Vitamin_1a numeric vector
Vitamin_2a numeric vector
Gainsa numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data5) summary(data5)data(data5) summary(data5)
Factorial in randomized block design
data(data6)data(data6)
A data frame with 16 observations on the following 4 variables.
factor1a numeric vector
factor2a numeric vector
blocka numeric vector
yielda numeric vector
PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.
data(data6) summary(data6)data(data6) summary(data6)
The aim of this experiment was to test the difference between two treatments on gain of kids. A sample of 18 kids was chosen, nine for each treatment. One kid in treatment 1 was removed from the experiment due to illness. The experiment began at the age of 8 weeks. Weekly gain was measured at ages 9, 10, 11 and 12 weeks.
data(data7)data(data7)
A data frame with 68 observations on the following 4 variables.
treatmenta character vector
repa numeric vector
weeka character vector
gaina numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data7) summary(data7)data(data7) summary(data7)
Split-plot Design. Main Plots in Randomized Blocks. An experiment was conducted in order to investigate four different treatments of pasture and two mineral supplements on milk yield. The total number of cows available was 24. The experiment was designed as a split-plot, with pasture treatments (factor A) assigned to the main plots and mineral supplements (factor B) assigned to split-plots. The experiment was replicated in three blocks.
data(data8)data(data8)
A data frame with 24 observations on the following 4 variables.
pasturea factor with levels p1 p2 p3 p4
blocka numeric vector
minerala factor with levels m1 m2
milka numeric vector
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
data(data8) summary(data8)data(data8) summary(data8)
Factorial design to evaluate egg quality according to the lineage of chicken, packaging and storage time.
data(data9)data(data9)
A data frame with 120 observations on the following 5 variables.
lineagea factor with levels A B
packinga factor with levels Ce Co S
timea numeric vector
repetitionsa numeric vector
responsea numeric vector
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
data(data9) summary(data9)data(data9) summary(data9)
Perform analysis of variance and other important complementary analyzes. The function are easy to use. Performs analysis in various simples designs, with balanced and unbalanced data. Too performs analysis the kruskal-Wallis and Friedman (designs 14 and 15).
ea1(data, design = 1, alpha = 0.05, list = FALSE, p.adjust=1, plot=2)ea1(data, design = 1, alpha = 0.05, list = FALSE, p.adjust=1, plot=2)
data |
data is a data.frame see how the input data in the examples |
design |
1 = completely randomized design 2 = randomized block design 3 = latin square design 4 = several latin squares 5 = analysis with a covariate (completely randomized design) 6 = analysis with a covariate (randomized block design) 7 = incomplete blocks type I and II 8 = incomplete blocks type III or augmented blocks 9 = incomplete blocks type III in animal experiments 10 = lattice (intra-block analysis) 11 = lattice (inter-block analysis) 12 = switchback design 13 = switchback design in blocks 14 = Kruskal-Wallis rank sum test 15 = Friedman rank sum test |
alpha |
significance level for multiple comparisons |
list |
FALSE = a single response variable TRUE = multivariable response |
p.adjust |
1="none"; 2="holm"; 3="hochberg"; 4="hommel"; 5="bonferroni"; 6="BH", 7="BY"; 8="fdr"; for more details see function "p.adjust" |
plot |
1 = box plot for residuals; 2 = standardized residuals vs sequence data; 3 = standardized residuals vs theoretical quantiles |
The response variable must be numeric. Other variables can be numeric or factors.
Returns analysis of variance, means (adjusted means), multiple comparison test (tukey, snk, duncan, t and scott knott) and residual analysis. Too returns analysis the kruskal-Wallis and Friedman (designs 14 and 15).
Emmanuel Arnhold <[email protected]>
CRUZ, C.D. and CARNEIRO, P.C.S. Modelos biometricos aplicados ao melhoramento genetico. 2nd Edition. Vicosa, UFV, v.2, 2006. 585p.
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
SANDERS W.L. and GAYNOR, P.J. Analysis of switchback data using Statistical Analysis System, Inc. Software. Journal of Dairy Science, 70.2186-2191. 1987.
PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.
ea2, ec
# Kaps and Lamberson(2009) data(data1) data(data2) data(data3) data(data4) # analysis in completely randomized design r1<-ea1(data1, design=1) names(r1) r1 # analysis in randomized block design r2<-ea1(data2, design=2) # analysis in latin square design r3<-ea1(data3, design=3) # analysis in several latin squares design r4<-ea1(data4, design=4) r1[1] r2[1] r3[1] r4[1] # analysis in unbalanced randomized block design response<-ifelse(data2$Gain>850, NA, data2$Gain) ndata<-data.frame(data2[-3],response) ndata r5<-ea1(ndata, design=2 ) r5 # multivariable response (list argument = TRUE) t<-c('a','a','a','b','b','b','c','c','c') r1<-c(10,12,12.8,4,6,8,14,15,16) r2<-c(102,105,106,125,123,124,99,95,96) r3<-c(560,589,590,658,678,629,369,389,378) d<-data.frame(t,r1,r2,r3) results=ea1(d, design=1, list=TRUE) names(results) results results[1][[1]] names(results[1][[1]]) # analysis with a covariate # Kaps and Lamberson (2009) data(data10) # analysis in completely randomized design r6<-ea1(data10[-3], design=5) r6 # incomplete blocks type I and II # Pimentel Gomes and Garcia (2002) data(data11) data(data12) r7<-ea1(data11,design=7) r8<-ea1(data12,design=7) r7;r8 # incomplete blocks type III or augmented blocks # Cruz and Carneiro (2006) data(data13) r9<-ea1(data13, design=8) r9 # incomplete blocks type III in animal experiments # Sampaio (2010) data(data14) r10<-ea1(data14, design=9) r10 # lattice # Pimentel Gomes and Garcia (2002) data(data15) r11<-ea1(data15, design=10) # intra-block analysis r12<-ea1(data15, design=11) # inter-block analysis r11 r12 # switchback design # Sampaio (2010) data(data16) r13<-ea1(data16, design=12) r13 # switchback design in blocks # Sanders and Gaynor (1987) data(data17) r14<-ea1(data17, design=13) r14 #Kruskal-Wallis Rank Sum Test r15<-ea1(data1, design=14) r15 #Friedman Rank Sum Test r16<-ea1(data2, design=15) r16 # Graeco-Latin Square #latin letters treatment=c("A","B","C","D","E","B","C","D","E","A","C", "D","E","A","B","D","E","A","B","C","E","A","B","C","D") ##blocked factors #greek letters block=c(1,2,3,4,5,3,4,5,1,2,5,1,2,3,4,2,3,4,5,1,4,5,1,2,3) # rowns rows=rep(1:5,5) #coluns columns=rep(1:5, each=5) #variable response=c(-1,-8,-7,1,-3,-5,-1,13,6,5,-6,5,1,1,-5,-1,2,2,-2,4,-1,11,-4,-3,6) # table data=data.frame(treatment, block, rows, columns, response) r16=ea1(data, design=16) r16 ### Repetitions of Graeco-Latin Square #latin letters treatment=c("A","B","C","D","E","B","C","D","E", "A","C","D","E","A","B","D","E","A","B","C","E","A","B","C","D", "A","B","C","D","E","B","C","D","E","A","C","D", "E","A","B","D","E","A","B","C","E","A","B","C","D") #squares squares=rep(1:2,25) ##blocked factors #greek letters block=c(1,2,3,4,5,3,4,5,1,2,5,1,2,3,4,2,3,4,5,1,4,5,1,2,3, 1,2,3,4,5,3,4,5,1,2,5,1,2,3,4,2,3,4,5,1,4,5,1,2,3) # rowns rows=c(rep(1:5,5),rep(1:5,5)) #coluns columns=c(rep(1:5, each=5),rep(1:5, each=5)) #variable response=c(-1,-8,-7,1,-3,-5,-1,13,6,5,-6,5,1,1,-5,-1,2,2,-2,4,-1,11,-4,-3,6, -2,-9,-8,1,-2,-5,-1,9,6,5,-5,2,3,1,-7,-1,2,4,-1,2,-2,15,-5,-1,7) # table data=data.frame(treatment, squares, block, rows, columns, response) r17=ea1(data, design=17) r17# Kaps and Lamberson(2009) data(data1) data(data2) data(data3) data(data4) # analysis in completely randomized design r1<-ea1(data1, design=1) names(r1) r1 # analysis in randomized block design r2<-ea1(data2, design=2) # analysis in latin square design r3<-ea1(data3, design=3) # analysis in several latin squares design r4<-ea1(data4, design=4) r1[1] r2[1] r3[1] r4[1] # analysis in unbalanced randomized block design response<-ifelse(data2$Gain>850, NA, data2$Gain) ndata<-data.frame(data2[-3],response) ndata r5<-ea1(ndata, design=2 ) r5 # multivariable response (list argument = TRUE) t<-c('a','a','a','b','b','b','c','c','c') r1<-c(10,12,12.8,4,6,8,14,15,16) r2<-c(102,105,106,125,123,124,99,95,96) r3<-c(560,589,590,658,678,629,369,389,378) d<-data.frame(t,r1,r2,r3) results=ea1(d, design=1, list=TRUE) names(results) results results[1][[1]] names(results[1][[1]]) # analysis with a covariate # Kaps and Lamberson (2009) data(data10) # analysis in completely randomized design r6<-ea1(data10[-3], design=5) r6 # incomplete blocks type I and II # Pimentel Gomes and Garcia (2002) data(data11) data(data12) r7<-ea1(data11,design=7) r8<-ea1(data12,design=7) r7;r8 # incomplete blocks type III or augmented blocks # Cruz and Carneiro (2006) data(data13) r9<-ea1(data13, design=8) r9 # incomplete blocks type III in animal experiments # Sampaio (2010) data(data14) r10<-ea1(data14, design=9) r10 # lattice # Pimentel Gomes and Garcia (2002) data(data15) r11<-ea1(data15, design=10) # intra-block analysis r12<-ea1(data15, design=11) # inter-block analysis r11 r12 # switchback design # Sampaio (2010) data(data16) r13<-ea1(data16, design=12) r13 # switchback design in blocks # Sanders and Gaynor (1987) data(data17) r14<-ea1(data17, design=13) r14 #Kruskal-Wallis Rank Sum Test r15<-ea1(data1, design=14) r15 #Friedman Rank Sum Test r16<-ea1(data2, design=15) r16 # Graeco-Latin Square #latin letters treatment=c("A","B","C","D","E","B","C","D","E","A","C", "D","E","A","B","D","E","A","B","C","E","A","B","C","D") ##blocked factors #greek letters block=c(1,2,3,4,5,3,4,5,1,2,5,1,2,3,4,2,3,4,5,1,4,5,1,2,3) # rowns rows=rep(1:5,5) #coluns columns=rep(1:5, each=5) #variable response=c(-1,-8,-7,1,-3,-5,-1,13,6,5,-6,5,1,1,-5,-1,2,2,-2,4,-1,11,-4,-3,6) # table data=data.frame(treatment, block, rows, columns, response) r16=ea1(data, design=16) r16 ### Repetitions of Graeco-Latin Square #latin letters treatment=c("A","B","C","D","E","B","C","D","E", "A","C","D","E","A","B","D","E","A","B","C","E","A","B","C","D", "A","B","C","D","E","B","C","D","E","A","C","D", "E","A","B","D","E","A","B","C","E","A","B","C","D") #squares squares=rep(1:2,25) ##blocked factors #greek letters block=c(1,2,3,4,5,3,4,5,1,2,5,1,2,3,4,2,3,4,5,1,4,5,1,2,3, 1,2,3,4,5,3,4,5,1,2,5,1,2,3,4,2,3,4,5,1,4,5,1,2,3) # rowns rows=c(rep(1:5,5),rep(1:5,5)) #coluns columns=c(rep(1:5, each=5),rep(1:5, each=5)) #variable response=c(-1,-8,-7,1,-3,-5,-1,13,6,5,-6,5,1,1,-5,-1,2,2,-2,4,-1,11,-4,-3,6, -2,-9,-8,1,-2,-5,-1,9,6,5,-5,2,3,1,-7,-1,2,4,-1,2,-2,15,-5,-1,7) # table data=data.frame(treatment, squares, block, rows, columns, response) r17=ea1(data, design=17) r17
Perform analysis of variance and other important complementary analyzes in factorial and split plot scheme, with balanced and unbalanced data.
ea2(data, design = 1, alpha = 0.05, cov = 4, list = FALSE, p.adjust=1, plot=2)ea2(data, design = 1, alpha = 0.05, cov = 4, list = FALSE, p.adjust=1, plot=2)
data |
data is a data.frame see how the input data in the examples |
design |
1 = double factorial in completely randomized design 2 = double factorial in randomized block design 3 = double factorial in latin square design 4 = split plot in completely randomized design 5 = split plot in randomized block design 6 = split plot in latin square design 7 = triple factorial in completely randomized design 8 = triple factorial in randomized block design 9 = double factorial in split plot (completely randomized) 10 = double factorial in split plot (randomized in block) 11 = joint analysis of experiments with hierarchical blocks 12 = joint analysis of repetitions of latin squares (hierarchical rows) 13 = joint analysis of repetitions of latin squares (hierarchical rows and columns) |
alpha |
significance level for multiple comparisons |
cov |
for split plot designs 1 = Autoregressive 2 = Heterogenius Autoregressive 3 = Continuous Autoregressive Process 4 = Compound Symetry 5 = Unstructured |
list |
FALSE = a single response variable TRUE = multivariable response |
p.adjust |
1="none"; 2="holm"; 3="hochberg"; 4="hommel"; 5="bonferroni"; 6="BH", 7="BY"; 8="fdr"; for more details see function "p.adjust" |
plot |
1 = box plot for residuals; 2 = standardized residuals vs sequence data; 3 = standardized residuals vs theoretical quantiles |
The response variable must be numeric. Other variables can be numeric or factors.
Returns analysis of variance, means (adjusted means), multiple comparison test (tukey, snk, duncan, t and scott knott) and residual analysis.
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
SAMPAIO, I. B. M. Estatistica aplicada a experimentacao animal. 3nd Edition. Belo Horizonte: Editora FEPMVZ, Fundacao de Ensino e Pesquisa em Medicina Veterinaria e Zootecnia, 2010. 264p.
PIMENTEL-GOMES, F. and GARCIA C.H. Estatistica aplicada a experimentos agronomicos e florestais: exposicao com exemplos e orientacoes para uso de aplicativos. Editora Fealq, v.11, 2002. 309p.
RAMALHO, M. A. P.; FERREIRA, D. F. and OLIVEIRA, A. C. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA, 2005, 322p.
ea1, ec
# double factorial # completely randomized design data(data5) r1=ea2(data5, design=1) r1 # randomized block design # data(data6) # r2=ea2(data6, design=2) # r2 # names(r1) # names(r2) # triple factorial # completely randomized design # data(data9) # r3=ea2(data9[,-4], design=7) # r3[1] # split plot # completely randomized design # data(data7) # r4=ea2(data7, design=4) # r4 # randomized block design # data(data8) # r5=ea2(data8, design=5) # r5 # hierarchical blocks # Ramalho et al. (2005) # data(data18) # data18 # r6=ea2(data18, design=11) # r6 # hierarchical latin squares # Sampaio (2010) # data(data19) # data19 # r7=ea2(data19, design=12) # r8=ea2(data19, design=13) # hierarchical rows # r7 # hierarchical rows and columns # r8 #split.plot in latin square #data(data3) #d=rbind(data3,data3) #d=data3[,-4];d=data.frame(d,time=rep(1:2,each=16),response=rnorm(32,45,4)) # r9=ea2(d,design=6) # r9# double factorial # completely randomized design data(data5) r1=ea2(data5, design=1) r1 # randomized block design # data(data6) # r2=ea2(data6, design=2) # r2 # names(r1) # names(r2) # triple factorial # completely randomized design # data(data9) # r3=ea2(data9[,-4], design=7) # r3[1] # split plot # completely randomized design # data(data7) # r4=ea2(data7, design=4) # r4 # randomized block design # data(data8) # r5=ea2(data8, design=5) # r5 # hierarchical blocks # Ramalho et al. (2005) # data(data18) # data18 # r6=ea2(data18, design=11) # r6 # hierarchical latin squares # Sampaio (2010) # data(data19) # data19 # r7=ea2(data19, design=12) # r8=ea2(data19, design=13) # hierarchical rows # r7 # hierarchical rows and columns # r8 #split.plot in latin square #data(data3) #d=rbind(data3,data3) #d=data3[,-4];d=data.frame(d,time=rep(1:2,each=16),response=rnorm(32,45,4)) # r9=ea2(d,design=6) # r9
Performs contrasts of means
ec(mg1, mg2, sdg1, sdg2, df)ec(mg1, mg2, sdg1, sdg2, df)
mg1 |
Means of the group 1 |
mg2 |
Means of the group 2 |
sdg1 |
Standard error of the group 1 |
sdg2 |
Standard error of the group 2 |
df |
Degree of freedom from error |
Returns t test for contrast
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2
# Kaps and Lamberson(2009, pg 254) data(data1) r<-ea1(data1, design=1) r[2] # first contrast mg1=312;mg2=c(278,280); sdg1=7.7028;sdg2=c(7.7028,7.7028); df=12 ec(mg1,mg2,sdg1,sdg2,df) # second contrast mg1=280;mg2=278; sdg1=7.7028;sdg2=7.7028; df=12 ec(mg1,mg2,sdg1,sdg2,df)# Kaps and Lamberson(2009, pg 254) data(data1) r<-ea1(data1, design=1) r[2] # first contrast mg1=312;mg2=c(278,280); sdg1=7.7028;sdg2=c(7.7028,7.7028); df=12 ec(mg1,mg2,sdg1,sdg2,df) # second contrast mg1=280;mg2=278; sdg1=7.7028;sdg2=7.7028; df=12 ec(mg1,mg2,sdg1,sdg2,df)
Estimate of confidence intervals of the contrasts
ic(data, test=1, df=10, alpha=0.05)ic(data, test=1, df=10, alpha=0.05)
data |
output object of ea1 or ea2 function (see examples) |
test |
Letters of the post-hoc test 1=Tukey 2=SNK 3=Duncan 4=t 5=Scott-Knott |
df |
degree of freedom of residuals in anova |
alpha |
significance level |
Returns confidence intervals of the contrasts
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2, box.plot, means.plot, means.plotfat, ic.plot, p.plot
#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot #means means=r[[2]] means ic(means, test=1, df=6) # tukey # alpha = 0.10 ic(r[[2]], test=1, df=6, alpha=0.10) # split plot data('data7') r<-ea2(data7,4) #plot ic(r[2], df=15) #split.plot ic(r[4], df=45)#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot #means means=r[[2]] means ic(means, test=1, df=6) # tukey # alpha = 0.10 ic(r[[2]], test=1, df=6, alpha=0.10) # split plot data('data7') r<-ea2(data7,4) #plot ic(r[2], df=15) #split.plot ic(r[4], df=45)
Plot confidence intervals of contrasts
ic.plot(data,col="dark green", cex=0.5, xlab="constrats", pch=19,family="Times", bg="white",...)ic.plot(data,col="dark green", cex=0.5, xlab="constrats", pch=19,family="Times", bg="white",...)
data |
output object of ic (see examples) |
col |
colours of lines |
cex |
size of points |
xlab |
title of x-axis |
pch |
type of points |
family |
font of plot |
bg |
background color |
... |
more plot parameters |
Plot confidence intervals of contrasts
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2, box.plot, means.plot, means.plotfat, ic.plot, p.plot
#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot #means means=r[[2]] means ic(means, test=1, df=6) # tukey #intervals conf=ic(means, test=1, df=6) #plot intervals ic.plot(conf) #more plot parameters ic.plot(conf, las=2, bg="cornsilk");grid(10)#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot #means means=r[[2]] means ic(means, test=1, df=6) # tukey #intervals conf=ic(means, test=1, df=6) #plot intervals ic.plot(conf) #more plot parameters ic.plot(conf, las=2, bg="cornsilk");grid(10)
Plot contrasts of means
m.plot(data, s="sd",test="tukey", family="Times", bg="white", cex.text=0.7, cex=0.5,bar.order=2, decreasing=TRUE, xlab="treatments", ylab="",pch=19, ...)m.plot(data, s="sd",test="tukey", family="Times", bg="white", cex.text=0.7, cex=0.5,bar.order=2, decreasing=TRUE, xlab="treatments", ylab="",pch=19, ...)
data |
output object of ea1 or ea2 function (see examples) |
s |
s="sd" (defalt) plot standard deviation s="sem" plot standard error of mean |
test |
Letters of the post-hoc test test="tukey" (default) test="snk" test="duncan" test="t" test="scott_knott" |
family |
font of plot |
bg |
background color |
cex.text |
font size in letters and means |
cex |
font size in points |
bar.order |
order of bar or means 1 = order of treatments names 2 = order of the means (default) |
decreasing |
decreasing bar order (TRUE or FALSE) |
xlab |
title of x-axis |
ylab |
title of y-axis |
pch |
type of points |
... |
more plot parameters |
Returns plots of means
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2, box.plot, means.plot, means.plotfat, ic, ic.plot, p.plot
#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot #means means=r[[2]] means m.plot(means, col=gray.colors(4)) #direct m.plot(r[[2]], col=gray.colors(4)) # more graphical parameters m.plot(means, col=c(2,7,3,5), bg="white", las=1, cex.text=1,main="Tukey (0.05)", family="sans", bar.order=2, decreasing=FALSE);grid(10) data('data7') r<-ea2(data7,4) m.plot(r[[4]], col=c(2,7,3,5), las=1, bg="cornsilk");grid(10) par(mfrow=c(1,2)) m.plot(r[[8]][1], test="scott_knott",xlab="treatment 1",col=c(2,7,3,5), las=2, bg="cornsilk",bar.order=2, decreasing=FALSE);grid(10) m.plot(r[[8]][2], test="scott_knott",xlab="treatment 2", col=c(2,7,3,5), las=2, bg="cornsilk", bar.order=2, decreasing=FALSE);grid(10)#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot #means means=r[[2]] means m.plot(means, col=gray.colors(4)) #direct m.plot(r[[2]], col=gray.colors(4)) # more graphical parameters m.plot(means, col=c(2,7,3,5), bg="white", las=1, cex.text=1,main="Tukey (0.05)", family="sans", bar.order=2, decreasing=FALSE);grid(10) data('data7') r<-ea2(data7,4) m.plot(r[[4]], col=c(2,7,3,5), las=1, bg="cornsilk");grid(10) par(mfrow=c(1,2)) m.plot(r[[8]][1], test="scott_knott",xlab="treatment 1",col=c(2,7,3,5), las=2, bg="cornsilk",bar.order=2, decreasing=FALSE);grid(10) m.plot(r[[8]][2], test="scott_knott",xlab="treatment 2", col=c(2,7,3,5), las=2, bg="cornsilk", bar.order=2, decreasing=FALSE);grid(10)
Plot contrasts of means
means.plot(data, plot=1, s=1,test=1, legend=TRUE, letters=TRUE, family="Times", bg="white",cex.names=0.8, cex.text=0.7, cex.legend=1, bar.order=2, decreasing=TRUE, alpha=0.05,cex=0.5, pch=19, ...)means.plot(data, plot=1, s=1,test=1, legend=TRUE, letters=TRUE, family="Times", bg="white",cex.names=0.8, cex.text=0.7, cex.legend=1, bar.order=2, decreasing=TRUE, alpha=0.05,cex=0.5, pch=19, ...)
data |
output object of ea1 function (see examples) |
plot |
type of plot 1 = bar plot (default) 2 = means plot 3 = confidence interval of the contrasts |
s |
s=1 (defalt) plot standard deviation s=2 plot standard error of mean |
test |
Letters of the post-hoc test 1=Tukey 2=SNK 3=Duncan 4=t 5=Scott-Knott |
legend |
TRUE = plot p-value of F test FALSE = not plot p-value |
letters |
TRUE = plot letters FALSE = not plot letters |
family |
font of plot |
bg |
background color |
cex.names |
font size in names of treatments (x-axis) |
cex.text |
font size in letters and means |
cex.legend |
font size in legend |
bar.order |
order of bar or means 1 = order of treatments names 2 = order of the means (default) |
decreasing |
decreasing bar order (TRUE or FALSE) |
alpha |
0.05 (default) is the alpha of confidence intervals |
cex |
size of points |
pch |
type of points |
... |
more plot parameters |
Returns plots and confidence intervals
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2
#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot means.plot(r, col=gray.colors(4)) # more graphical parameters means.plot(r, col=c(2,7,3,5), bg="cornsilk", las=1, cex.names=2, sub="treatments", family="sans");grid(10) # plot = 2 means.plot(r, plot=2, col="dark green", bg="gray", las=1, cex.names=2, sub="Treatments", family="Times", ylab="Hay intake") # plot = 2 decreasing =FALSE means.plot(r, plot=2, las=1, cex.names=2, col="red",lty=2,pch=20,cex=1.1, sub="Treatments", family="Times", ylab="Hay intake", decreasing=FALSE, legend=FALSE);grid(10) # plot=3 means.plot(r, plot=3, las=1, cex.names=2, sub="Contrasts (Tukey 0.05)", family="Times", ylab="") # plot=3 alpha=0.10 means.plot(ea1(data3, design=3), plot=3, las=2, cex.names=2, sub="Contrasts (Tukey 0.10)", family="Times", ylab="", alpha=0.10, bg="cornsilk");grid(10)#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot means.plot(r, col=gray.colors(4)) # more graphical parameters means.plot(r, col=c(2,7,3,5), bg="cornsilk", las=1, cex.names=2, sub="treatments", family="sans");grid(10) # plot = 2 means.plot(r, plot=2, col="dark green", bg="gray", las=1, cex.names=2, sub="Treatments", family="Times", ylab="Hay intake") # plot = 2 decreasing =FALSE means.plot(r, plot=2, las=1, cex.names=2, col="red",lty=2,pch=20,cex=1.1, sub="Treatments", family="Times", ylab="Hay intake", decreasing=FALSE, legend=FALSE);grid(10) # plot=3 means.plot(r, plot=3, las=1, cex.names=2, sub="Contrasts (Tukey 0.05)", family="Times", ylab="") # plot=3 alpha=0.10 means.plot(ea1(data3, design=3), plot=3, las=2, cex.names=2, sub="Contrasts (Tukey 0.10)", family="Times", ylab="", alpha=0.10, bg="cornsilk");grid(10)
Plot contrasts of means
means.plotfat(data, plot=1, s=1,test=1, legend=TRUE, letters=TRUE, family="Times", bg="white", cex.names=0.8, cex.text=0.7, cex.legend=1, bar.order=1,decreasing=TRUE, ...)means.plotfat(data, plot=1, s=1,test=1, legend=TRUE, letters=TRUE, family="Times", bg="white", cex.names=0.8, cex.text=0.7, cex.legend=1, bar.order=1,decreasing=TRUE, ...)
data |
output object of ea2 function (see examples) |
plot |
type of plot 1 = bar plot factor 1(default) 2 = bar plot factor 2 3 = bar plot interactions (option 1) 4 = bar plot interactions (option 2) 5 = bar plot interactions (option 3) 6 = bar plot interactions (option 4) |
s |
s=1 (defalt) plot standard deviation s=2 plot standard error of mean |
test |
Letters of the post-hoc test 1=Tukey 2=SNK 3=Duncan 4=t 5=Scott-Knott |
legend |
TRUE = plot p-value of F test FALSE = not plot p-value |
letters |
TRUE = plot letters FALSE = not plot letters |
family |
font of plot |
bg |
background color |
cex.names |
font size in names of treatments (x-axis) |
cex.text |
font size in letters and means |
cex.legend |
font size in legend |
bar.order |
order of bar or means 1 = order of treatments names 2 = order of the means (default) |
decreasing |
decreasing bar order (TRUE or FALSE) |
... |
more plot parameters |
Returns bar plots
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2, means.plot, box.plot
#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot means.plot(r, col=gray.colors(4)) # more graphical parameters means.plot(r, col=c(2,7,3,5), bg="cornsilk", las=1, cex.names=2, sub="treatments", family="sans");grid(10) # plot = 2 means.plot(r, plot=2, col="dark green", bg="gray", las=1, cex.names=2, sub="Treatments", family="Times", ylab="Hay intake") # plot = 2 decreasing =FALSE means.plot(r, plot=2, las=1, cex.names=2, col="red",lty=2,pch=20,cex=1.1, sub="Treatments", family="Times", ylab="Hay intake", decreasing=FALSE, legend=FALSE);grid(10) # plot=3 means.plot(r, plot=3, las=1, cex.names=2, sub="Contrasts (Tukey 0.05)", family="Times", ylab="") # plot=3 alpha=0.10 means.plot(ea1(data3, design=3), plot=3, las=2, cex.names=2, sub="Contrasts (Tukey 0.10)", family="Times", ylab="", alpha=0.10, bg="cornsilk");grid(10)#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot means.plot(r, col=gray.colors(4)) # more graphical parameters means.plot(r, col=c(2,7,3,5), bg="cornsilk", las=1, cex.names=2, sub="treatments", family="sans");grid(10) # plot = 2 means.plot(r, plot=2, col="dark green", bg="gray", las=1, cex.names=2, sub="Treatments", family="Times", ylab="Hay intake") # plot = 2 decreasing =FALSE means.plot(r, plot=2, las=1, cex.names=2, col="red",lty=2,pch=20,cex=1.1, sub="Treatments", family="Times", ylab="Hay intake", decreasing=FALSE, legend=FALSE);grid(10) # plot=3 means.plot(r, plot=3, las=1, cex.names=2, sub="Contrasts (Tukey 0.05)", family="Times", ylab="") # plot=3 alpha=0.10 means.plot(ea1(data3, design=3), plot=3, las=2, cex.names=2, sub="Contrasts (Tukey 0.10)", family="Times", ylab="", alpha=0.10, bg="cornsilk");grid(10)
Plot p values of the contrasts
p.plot(data, ylab="", xlab="", col.lines="red", cex.axis=0.7, cex=0.9 , col.text="dark green",family="Times", bg="white",...)p.plot(data, ylab="", xlab="", col.lines="red", cex.axis=0.7, cex=0.9 , col.text="dark green",family="Times", bg="white",...)
data |
output object of ea1 or ea2 function (see examples) |
ylab |
title of y-axis |
xlab |
title of x-axis |
col.lines |
colours of the lines |
cex.axis |
font size in axis |
cex |
size of points |
col.text |
colours in letters and means |
family |
font of plot |
bg |
background color |
... |
more plot parameters |
Plot p values of the contrasts
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2, box.plot, means.plot, means.plotfat, ic, ic.plot, m.plot
#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot contrasts=r[[3]] contrasts p.plot(contrasts) #direct p.plot(r[3]) # more graphical parameters p.plot(contrasts, bg="cornsilk", cex=1.5,cex.axis=1.5, main="P-values of the tukey contrasts",family="sans");grid(10) data('data7') r<-ea2(data7,4) p.plot(r[[5]], bg="cornsilk");grid(10) par(mfrow=c(1,2)) p.plot(r[[9]][1], xlab="treatment 1", cex=0.5, bg="cornsilk");grid(10) p.plot(r[[9]][2], xlab="treatment 2", cex=0.5, bg="cornsilk");grid(10)#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) #plot contrasts=r[[3]] contrasts p.plot(contrasts) #direct p.plot(r[3]) # more graphical parameters p.plot(contrasts, bg="cornsilk", cex=1.5,cex.axis=1.5, main="P-values of the tukey contrasts",family="sans");grid(10) data('data7') r<-ea2(data7,4) p.plot(r[[5]], bg="cornsilk");grid(10) par(mfrow=c(1,2)) p.plot(r[[9]][1], xlab="treatment 1", cex=0.5, bg="cornsilk");grid(10) p.plot(r[[9]][2], xlab="treatment 2", cex=0.5, bg="cornsilk");grid(10)
Summary of results in ea1 function
tab(data, test=1)tab(data, test=1)
data |
output object of ea1 function (see examples) |
test |
Letters of the post-hoc test 1=Tukey 2=SNK 3=Duncan 4=t 5=Scott-Knott |
Summary of results in ea1 function
Emmanuel Arnhold <[email protected]>
KAPS, M. and LAMBERSON, W. R. Biostatistics for Animal Science: an introductory text. 2nd Edition. CABI Publishing, Wallingford, Oxfordshire, UK, 2009. 504p.
ea1,ea2, box.plot, means.plot, means.plotfat, ic.plot, p.plot
#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) tab(r) ### multiple variables t<-c('a','a','a','b','b','b','c','c','c') r1<-c(10,12,12.8,4,6,8,14,15,16) r2<-c(102,105,106,125,123,124,99,95,96) r3<-c(560,589,590,658,678,629,369,389,378) d<-data.frame(t,r1,r2,r3) results=ea1(d, design=1, list=TRUE) # scottknott test tab(results,test=5)#data3: Kaps and Lamberson (2009): page 347 #Description: ##Latin square design for test four different treatments on hay intake of fattening steers data(data3) r<-ea1(data3, design=3) tab(r) ### multiple variables t<-c('a','a','a','b','b','b','c','c','c') r1<-c(10,12,12.8,4,6,8,14,15,16) r2<-c(102,105,106,125,123,124,99,95,96) r3<-c(560,589,590,658,678,629,369,389,378) d<-data.frame(t,r1,r2,r3) results=ea1(d, design=1, list=TRUE) # scottknott test tab(results,test=5)