#===============================================================================
#. Name   : tcmm_sk_fe
#. Author : J.C.Faria
# Date   : 05/08/2021 19:09:51
# Aim    : Simular experimentos qualitativos fatoriais nos delineamentos
#          experimentais básicos (DIC, DBC e DQL) e analisar pelo
#          algoritmo de ScottKnott.
# Mail   : <<< joseclaudio.faria@gmail.com >>>
#===============================================================================

#. Carregando pacotes necessários
# gexp
#install.packages('gexp')
library(gexp)

# ScottKnott
#install.packages('ScottKnott')
library(ScottKnott)

#.. ___EF/DIC___
r <- 4 # Número de reptições do experimento
set.seed(2019)
ef_dic <- gexp(mu=10,
               r=r,
               fl=list(A=c('a1', 'a2'),
                       B=c('b1', 'b2')),
               fe=list(f1=c(0, 3),
                       f2=c(3, 0)),
               inte=c(0,  #a1 b1 *
                      0,  #a2 b1
                      5,  #a1 b2
                      0), #a2 b2
#...           Descomente abaixo p/ erro experimental = 0
#               err=matrix(0,
#                          nrow=2^2*r),
               round=0,
               type='FE',
               design='CRD',
               )
summary(ef_dic)

par(mfrow=c(1, 1))
plot(ef_dic,
     main='EF sob DIC')

# Gráfico dos efeitos
par(mfrow=c(2, 1))
with(ef_dic$dfm,
     interaction.plot(A, B, Y1,
                      xlab='A',
                      ylab='Y1',
                      col='blue3',
                      lwd=2,
                      ylim=c(min(Y1), max(Y1))))

with(ef_dic$dfm,
     interaction.plot(B, A, Y1,
                      xlab='B',
                      ylab='Y1',
                      col='blue3',
                      lwd=2,
                      ylim=c(min(Y1), max(Y1))))

# *: Para entender o argumento 'inte' (efeito da interação)
(expand.grid(A=c('a1', 'a2'),
             B=c('b1', 'b2')))

# ANOVA
av_ef_dic <- aov(Y1 ~ A + B + A*B,
                 ef_dic$dfm)
summary(av_ef_dic)

# TCMM
#a1:B
summary(SK(av_ef_dic,
           which='A:B',
           fl1=1))

#a2:B
summary(SK(av_ef_dic,
           which='A:B',
           fl1=2))

#b1:A
summary(SK(av_ef_dic,
           which='B:A',
           fl1=1))

#b2:A
summary(SK(av_ef_dic,
           which='B:A',
           fl1=2))

# Plot
par(mfrow=c(2, 2))
#a1:B
plot(SK(av_ef_dic,
        which='A:B',
        fl1=1),
     disp='mm',
     main='a1:B',
     xlab='',
     ylab='')

#a2:B
plot(SK(av_ef_dic,
        which='A:B',
        fl1=2),
     disp='mm',
     main='a2:B',
     xlab='',
     ylab='')

#b1:A
plot(SK(av_ef_dic,
        which='B:A',
        fl1=1),
     disp='mm',
     main='b1:A',
     xlab='tra',
     ylab='Y1')

#b2:A
plot(SK(av_ef_dic,
        which='B:A',
        fl1=2),
     disp='mm',
     main='b2:A',
     xlab='',
     ylab='')


#.. ___EF/DBC___
set.seed(2)
ef_dbc <- gexp(mu=10,
               r=1,
               fl=list(A=c('a1', 'a2'),
                       B=c('b1', 'b2')),
               fe=list(f1=c(0, 3),
                       f2=c(3, 0)),
               inte=c(0,  #a1 b1 *
                      0,  #a2 b1
                      5,  #a1 b2
                      0), #a2 b2
               blke=c(1, 2, 3, 4, 5),
               blkl=list(BLO=paste('b',
                                   1:5,
                                   sep='')),
               round=1,
               type='FE',
               design='RCBD',
               )
str(ef_dbc)
summary(ef_dbc)

par(mfrow=c(1, 1))
plot(ef_dbc,
     main='EF sob DBC')

# Gráfico dos efeitos
par(mfrow=c(2, 1))
with(ef_dbc$dfm,
     interaction.plot(A, B, Y1,
                      xlab='A',
                      ylab='Y1',
                      col='blue3',
                      lwd=2,
                      ylim=c(min(Y1), max(Y1))))

with(ef_dbc$dfm,
     interaction.plot(B, A, Y1,
                      xlab='B',
                      ylab='Y1',
                      col='blue3',
                      lwd=2,
                      ylim=c(min(Y1), max(Y1))))

# *: Para entender o argumento 'inte' (efeito da interação)
(expand.grid(A=c('a1', 'a2'),
             B=c('b1', 'b2')))

# ANOVA
av_ef_dbc <- aov(Y1 ~ BLO + A + B + A*B,
                 ef_dbc$dfm)
summary(av_ef_dbc)

library(ScottKnott)

# TCMM
#a1:B
summary(SK(av_ef_dbc,
           which='A:B',
           fl1=1))

#a2:B
summary(SK(av_ef_dbc,
           which='A:B',
           fl1=2))

#b1:A
summary(SK(av_ef_dbc,
           which='B:A',
           fl1=1))

#b2:A
summary(SK(av_ef_dbc,
           which='B:A',
           fl1=2))

# Plot
par(mfrow=c(2, 2))
#a1:B
plot(SK(av_ef_dbc,
        which='A:B',
        fl1=1),
     disp='mm',
     main='a1:B',
     xlab='',
     ylab='')

#a2:B
plot(SK(av_ef_dbc,
        which='A:B',
        fl1=2),
     disp='mm',
     main='a2:B',
     xlab='',
     ylab='')

#b1:A
plot(SK(av_ef_dbc,
        which='B:A',
        fl1=1),
     disp='mm',
     main='b1:A',
     xlab='tra',
     ylab='Y1')

#b2:A
plot(SK(av_ef_dbc,
        which='B:A',
        fl1=2),
     disp='mm',
     main='b2:A',
     xlab='',
     ylab='')


#.. ___EF/DQL___
set.seed(3)
ef_dql <- gexp(mu=10,
               r=1,
               fl=list(A=c('a1', 'a2', 'a3'),
                       B=c('b1', 'b2')),
               fe=list(f1=c(0, 3, 1),
                       f2=c(3, 0)),
               inte=c(0,  #a1 b1
                      0,  #a2 b1
                      0,  #a3 b1
                      5,  #a1 b2
                      0,  #a2 b2
                      0), #a3 b2
               rowe=c(1, 2, 3, 4, 5, 6),
               rowl=list(LIN=paste('l',
                                    1:6,
                                    sep='')),
               cole=c(6, 5, 4, 3, 2, 1),
               coll=list(COL=paste('c',
                                    1:6,
                                    sep='')),
               round=1,
               type='FE',
               design='LSD',
               )
str(ef_dql)
summary(ef_dql)

par(mfrow=c(1, 1))
plot(ef_dql,
     main='EF sob DQL')

# Gráfico dos efeitos
par(mfrow=c(2, 1))
with(ef_dql$dfm,
     interaction.plot(A, B, Y1,
                      xlab='A',
                      ylab='Y1',
                      col='blue3',
                      lwd=2,
                      ylim=c(min(Y1), max(Y1))))

with(ef_dql$dfm,
     interaction.plot(B, A, Y1,
                      xlab='B',
                      ylab='Y1',
                      col='blue3',
                      lwd=2,
                      ylim=c(min(Y1), max(Y1))))

# *: Para entender o argumento 'inte' (efeito da interação)
(expand.grid(A=c('a1', 'a2', 'a3'),
             B=c('b1', 'b2')))

ef_dql$dfm$A <- factor(ef_dql$dfm$A)
ef_dql$dfm$B <- factor(ef_dql$dfm$B)

# ANOVA
av_ef_dql <- aov(Y1 ~ LIN + COL + A + B + A*B,
                 ef_dql$dfm)
summary(av_ef_dql)

library(ScottKnott)

# TCMM
#a1:B
summary(SK(av_ef_dql,
           which='A:B',
           fl1=1))

#a2:B
summary(SK(av_ef_dql,
           which='A:B',
           fl1=2))

#a3:B
summary(SK(av_ef_dql,
           which='A:B',
           fl1=3))

#b1:A
summary(SK(av_ef_dql,
           which='B:A',
           fl1=1))

#b2:A
summary(SK(av_ef_dql,
           which='B:A',
           fl1=2))

# Plot
par(mfrow=c(2, 3))
#a1:B
plot(SK(av_ef_dql,
        which='A:B',
        fl1=1),
     disp='mm',
     main='a1:B',
     xlab='',
     ylab='')

#a2:B
plot(SK(av_ef_dql,
        which='A:B',
        fl1=2),
     disp='mm',
     main='a2:B',
     xlab='',
     ylab='')

#a3:B
plot(SK(av_ef_dql,
        which='A:B',
        fl1=3),
     disp='mm',
     main='a3:B',
     xlab='',
     ylab='')

#b1:A
plot(SK(av_ef_dql,
        which='B:A',
        fl1=1),
     disp='mm',
     main='b1:A',
     xlab='tra',
     ylab='Y1')

#b2:A
plot(SK(av_ef_dql,
        which='B:A',
        fl1=2),
     disp='mm',
     main='b2:A',
     xlab='',
     ylab='')