conjfat2dbc.Rd
Function of the AgroR package for joint analysis of experiments conducted in a randomized factorial double in block design with balanced data. The function generates the joint analysis through two models. Model 1: F-test of the effects of Factor 1, Factor 2 and F1 x F2 interaction are used in reference to the mean square of the interaction with the year. Model 2: F-test of the Factor 1, Factor 2 and F1 x F2 interaction effects are used in reference to the mean square of the residual.
conjfat2dbc(
f1,
f2,
block,
experiment,
response,
transf = 1,
constant = 0,
model = 1,
norm = "sw",
homog = "bt",
homog.value = 7,
alpha.f = 0.05,
alpha.t = 0.05
)
Numeric or complex vector with factor 1 levels
Numeric or complex vector with factor 2 levels
Numerical or complex vector with blocks
Numeric or complex vector with locations or times
Numerical vector containing the response of the experiment.
Applies data transformation (default is 1; for log consider 0)
Add a constant for transformation (enter value)
Define model of the analysis of variance
Error normality test (default is Shapiro-Wilk)
Homogeneity test of variances (default is Bartlett)
Reference value for homogeneity of experiments. By default, this ratio should not be greater than 7
Level of significance of the F test (default is 0.05)
Significance level of the multiple comparison test (default is 0.05)
Returns the assumptions of the analysis of variance, the assumption of the joint analysis by means of a QMres ratio matrix and analysis of variance
The function is still limited to analysis of variance and assumptions only.
Ferreira, P. V. Estatistica experimental aplicada a agronomia. Edufal, 2018.
Principles and procedures of statistics a biometrical approach Steel, Torry and Dickey. Third Edition 1997
Multiple comparisons theory and methods. Departament of statistics the Ohio State University. USA, 1996. Jason C. Hsu. Chapman Hall/CRC.
Practical Nonparametrics Statistics. W.J. Conover, 1999
Ramalho M.A.P., Ferreira D.F., Oliveira A.C. 2000. Experimentacao em Genetica e Melhoramento de Plantas. Editora UFLA.
library(AgroR)
ano=factor(rep(c(2018,2019,2020),e=48))
f1=rep(rep(c("A","B","C"),e=16),3)
f2=rep(rep(rep(c("a1","a2","a3","a4"),e=4),3),3)
resp=rnorm(48*3,10,1)
bloco=rep(c("b1","b2","b3","b4"),36)
dados=data.frame(ano,f1,f2,resp,bloco)
with(dados,conjfat2dbc(f1,f2,bloco,ano,resp, model=1))
#>
#> -----------------------------------------------------------------
#> Normality of errors
#> -----------------------------------------------------------------
#> Method Statistic p.value
#> Shapiro-Wilk normality test(W) 0.9936686 0.7794519
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered normal
#> -----------------------------------------------------------------
#> Homogeneity of Variances
#> -----------------------------------------------------------------
#> Method Statistic p.value
#> Bartlett test(Bartlett's K-squared) 23.44848 0.0152697
#>
#> As the calculated p-value is less than the 5% significance level, H0 is rejected. Therefore, the variances are not homogeneous
#>
#> -----------------------------------------------------------------
#> Independence from errors
#> -----------------------------------------------------------------
#> Method Statistic p.value
#> Durbin-Watson test(DW) 2.568423 0.8143241
#>
#> As the calculated p-value is greater than the 5% significance level, hypothesis H0 is not rejected. Therefore, errors can be considered independent
#> -----------------------------------------------------------------
#> Test Homogeneity of experiments
#> -----------------------------------------------------------------
#> [1] 1.779032
#>
#> Based on the analysis of variance and homogeneity of experiments, it can be concluded that:
#> The experiments can be analyzed together
#>
#>
#>
#> -----------------------------------------------------------------
#> Analysis of variance
#> -----------------------------------------------------------------
#> Df Sum Sq Mean Sq F value Pr(>F)
#> f1 2 1.588736 0.7943681 0.6529516 0.5303031
#> f2 3 8.457173 2.8190575 2.3171980 0.1035156
#> f1:f2 6 8.344925 1.3908208 1.1432215 0.3710006
#> local:bloco 11 12.026048 1.0932771 0.8986477 0.5563127
#> f1:f2:local 22 26.764767 1.2165803 1.2587297 0.1270165
#> Residuals 99 95.684923 0.9665144
#>
#> -----------------------------------------------------------------
#>