Capítulo 18 Blocos aumentados de Federer
RamalhoEg11.13: blocos incompletos com 4 populações comuns e 25 populações regulares.RamalhoTb11.17: blocos incompletos com testemunhas comuns a todos os blocos.ZimmermannTb8.5: TODO
18.1 RamalhoEg11.13
Os dados em RamalhoEg11.13 referem-se a produção (kg/parcela) de populações de milho avaliadas no delieneamento de blocos aumentados.
# Importa os dados.
da <- as_tibble(labestData::RamalhoEg11.13)
str(da)## Classes 'tbl_df', 'tbl' and 'data.frame': 45 obs. of 3 variables:
## $ pop : Factor w/ 29 levels "I","II","III",..: 1 2 3 4 5 6 7 8 9 10 ...
## $ bloc: Factor w/ 5 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 1 ...
## $ prod: num 7.5 8.2 9.4 6.8 8.1 4.2 7.4 9.3 8.9 7.5 ...with(da, table(pop, bloc)) %>%
addmargins() %>%
print.table(zero.print = ".")## bloc
## pop 1 2 3 4 5 Sum
## I 1 1 1 1 1 5
## II 1 1 1 1 1 5
## III 1 1 1 1 1 5
## IV 1 1 1 1 1 5
## 1 1 . . . . 1
## 2 1 . . . . 1
## 3 1 . . . . 1
## 4 1 . . . . 1
## 5 1 . . . . 1
## 6 1 . . . . 1
## 7 . 1 . . . 1
## 8 . 1 . . . 1
## 9 . 1 . . . 1
## 10 . 1 . . . 1
## 11 . 1 . . . 1
## 12 . 1 . . . 1
## 13 . . 1 . . 1
## 14 . . 1 . . 1
## 15 . . 1 . . 1
## 16 . . 1 . . 1
## 17 . . 1 . . 1
## 18 . . . 1 . 1
## 19 . . . 1 . 1
## 20 . . . 1 . 1
## 21 . . . 1 . 1
## 22 . . . . 1 1
## 23 . . . . 1 1
## 24 . . . . 1 1
## 25 . . . . 1 1
## Sum 10 10 9 8 8 45library(igraph)
edg <- by(data = as.integer(da$pop),
INDICES = da$bloc,
FUN = combn,
m = 2) %>%
flatten_int()
p <- 4
pal <- RColorBrewer::brewer.pal(n = nlevels(da$bloc) + 1,
name = "Set1")
clr <- rep(head(pal, n = -1),
tapply(da$pop, da$bloc, length) - p)
clr <- append(clr, rep(tail(pal, n = 1), p),
after = 0)
ghp <- graph(edg, directed = FALSE)
plot(ghp,
vertex.color = clr,
layout = layout_nicely,
edge.curved = FALSE)
#18.2 RamalhoTb11.17
da <- as_tibble(labestData::RamalhoTb11.17)
str(da)## Classes 'tbl_df', 'tbl' and 'data.frame': 48 obs. of 4 variables:
## $ exp : Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
## $ linh: Factor w/ 18 levels "1","2","3","4",..: 1 2 3 4 5 6 7 8 1 2 ...
## $ bloc: Factor w/ 2 levels "1","2": 1 1 1 1 1 1 1 1 2 2 ...
## $ prod: num 5.7 6.1 7.2 5.2 4.3 5.5 5.9 4.5 5.7 6.1 ...with(da, table(bloc, linh, exp)) %>%
addmargins(margin = 1:2) %>%
print.table(zero.print = ".")## , , exp = 1
##
## linh
## bloc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Sum
## 1 1 1 1 1 1 1 1 1 . . . . . . . . . . 8
## 2 1 1 1 1 1 1 1 1 . . . . . . . . . . 8
## Sum 2 2 2 2 2 2 2 2 . . . . . . . . . . 16
##
## , , exp = 2
##
## linh
## bloc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Sum
## 1 1 1 1 . . . . . 1 1 1 1 1 . . . . . 8
## 2 1 1 1 . . . . . 1 1 1 1 1 . . . . . 8
## Sum 2 2 2 . . . . . 2 2 2 2 2 . . . . . 16
##
## , , exp = 3
##
## linh
## bloc 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Sum
## 1 1 1 1 . . . . . . . . . . 1 1 1 1 1 8
## 2 1 1 1 . . . . . . . . . . 1 1 1 1 1 8
## Sum 2 2 2 . . . . . . . . . . 2 2 2 2 2 16library(igraph)
# Todos os possíveis pares.
pares <- apply(combn(sort(as.character(levels(da$linh))), m = 2),
MARGIN = 2,
FUN = paste0,
collapse = "_") %>%
tibble(name = ., value = 0)
head(pares)
da$cond <- interaction(da$exp, da$bloc, drop = TRUE)
# Quantas vezes cada par ocorre junto.
by(data = as.character(da$linh),
INDICES = da$cond,
FUN = function(x) {
apply(combn(sort(x), m = 2),
MARGIN = 2,
FUN = paste0,
collapse = "_")
}) %>%
flatten_chr() %>%
table() %>%
c() %>%
enframe() %>%
bind_rows(pares) %>%
distinct(name, .keep_all = TRUE) %>%
split(x = .$name, f = .$value)
edg <- by(data = as.integer(da$linh),
INDICES = da$cond,
FUN = combn,
m = 2) %>%
flatten_int()
p <- 3
pal <- RColorBrewer::brewer.pal(n = nlevels(da$cond) + 1,
name = "Set1")
clr <- rep(head(pal, n = -1),
tapply(da$cond, da$cond, length) - p)
clr <- append(clr, rep(tail(pal, n = 1), p),
after = 0)
ghp <- graph(edg, directed = FALSE)
plot(ghp,
vertex.color = clr,
layout = layout_nicely,
edge.curved = FALSE)# Modelo de efeitos fixos.
m0 <- lm(terms(prod ~ exp/bloc + linh, keep.order = TRUE),
data = da)
# Quado de anova com hipóteses marginais.
Anova(m0)## Anova Table (Type II tests)
##
## Response: prod
## Sum Sq Df F value Pr(>F)
## exp 3.3078 2 4.3208 0.0244504 *
## exp:bloc 10.3162 3 8.9838 0.0003271 ***
## linh 20.4282 17 3.1394 0.0047220 **
## Residuals 9.5693 25
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Médias marginais ajustadas.
emm <- emmeans(m0, specs = ~linh)## NOTE: A nesting structure was detected in the fitted model:
## bloc %in% expemm## linh emmean SE df lower.CL upper.CL
## 1 5.63 0.253 25 5.11 6.15
## 2 6.25 0.253 25 5.73 6.77
## 3 7.15 0.253 25 6.63 7.67
## 4 5.24 0.484 25 4.25 6.24
## 5 4.99 0.484 25 4.00 5.99
## 6 5.79 0.484 25 4.80 6.79
## 7 6.19 0.484 25 5.20 7.19
## 8 6.14 0.484 25 5.15 7.14
## 9 5.53 0.484 25 4.53 6.52
## 10 6.93 0.484 25 5.93 7.92
## 11 6.23 0.484 25 5.23 7.22
## 12 6.23 0.484 25 5.23 7.22
## 13 5.93 0.484 25 4.93 6.92
## 14 7.28 0.484 25 6.28 8.27
## 15 7.58 0.484 25 6.58 8.57
## 16 8.38 0.484 25 7.38 9.37
## 17 7.23 0.484 25 6.23 8.22
## 18 7.13 0.484 25 6.13 8.12
##
## Results are averaged over the levels of: bloc, exp
## Confidence level used: 0.95# Extração da matriz de funções lineares.
L <- attr(emm, "linfct")
grid <- attr(emm, "grid")
rownames(L) <- grid[[1]]
# Entenda como são obtidas as médias marginais.
MASS::fractions(t(L))## 1 2 3 4 5 6 7 8 9 10 11 12 13 14
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1 1 1 1
## exp2 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## exp3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## exp1:bloc2 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6
## exp2:bloc2 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6
## exp3:bloc2 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6 1/6
## linh2 0 1 0 0 0 0 0 0 0 0 0 0 0 0
## linh3 0 0 1 0 0 0 0 0 0 0 0 0 0 0
## linh4 0 0 0 1 0 0 0 0 0 0 0 0 0 0
## linh5 0 0 0 0 1 0 0 0 0 0 0 0 0 0
## linh6 0 0 0 0 0 1 0 0 0 0 0 0 0 0
## linh7 0 0 0 0 0 0 1 0 0 0 0 0 0 0
## linh8 0 0 0 0 0 0 0 1 0 0 0 0 0 0
## linh9 0 0 0 0 0 0 0 0 1 0 0 0 0 0
## linh10 0 0 0 0 0 0 0 0 0 1 0 0 0 0
## linh11 0 0 0 0 0 0 0 0 0 0 1 0 0 0
## linh12 0 0 0 0 0 0 0 0 0 0 0 1 0 0
## linh13 0 0 0 0 0 0 0 0 0 0 0 0 1 0
## linh14 0 0 0 0 0 0 0 0 0 0 0 0 0 1
## linh15 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## linh16 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## linh17 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## linh18 0 0 0 0 0 0 0 0 0 0 0 0 0 0
## 15 16 17 18
## (Intercept) 1 1 1 1
## exp2 1/3 1/3 1/3 1/3
## exp3 1/3 1/3 1/3 1/3
## exp1:bloc2 1/6 1/6 1/6 1/6
## exp2:bloc2 1/6 1/6 1/6 1/6
## exp3:bloc2 1/6 1/6 1/6 1/6
## linh2 0 0 0 0
## linh3 0 0 0 0
## linh4 0 0 0 0
## linh5 0 0 0 0
## linh6 0 0 0 0
## linh7 0 0 0 0
## linh8 0 0 0 0
## linh9 0 0 0 0
## linh10 0 0 0 0
## linh11 0 0 0 0
## linh12 0 0 0 0
## linh13 0 0 0 0
## linh14 0 0 0 0
## linh15 1 0 0 0
## linh16 0 1 0 0
## linh17 0 0 1 0
## linh18 0 0 0 1# Contrastes par a par.
ctr <- summary(glht(m0, linfct = all_pairwise(L)),
test = adjusted(type = "fdr"))
# Erros padrões de vários tamanhos conforme estrutura de associação.
v <- c("coefficients", "sigma", "tstat", "pvalues")
ctr$test[v] %>%
as.data.frame() %>%
split(., round(.$sigma, 4)) %>%
map(head, n = 5)## $`0.3572`
## coefficients sigma tstat pvalues
## 1vs2 -0.6166667 0.3571985 -1.726398 0.22397225
## 1vs3 -1.5166667 0.3571985 -4.246006 0.01005365
## 2vs3 -0.9000000 0.3571985 -2.519608 0.09439470
##
## $`0.5456`
## coefficients sigma tstat pvalues
## 1vs4 0.3888889 0.5456297 0.7127341 0.6477138
## 1vs5 0.6388889 0.5456297 1.1709204 0.4112625
## 1vs6 -0.1611111 0.5456297 -0.2952756 0.8794324
## 1vs7 -0.5611111 0.5456297 -1.0283735 0.4671447
## 1vs8 -0.5111111 0.5456297 -0.9367363 0.5191158
##
## $`0.6187`
## coefficients sigma tstat pvalues
## 4vs5 0.25 0.6186859 0.4040823 0.8178837
## 4vs6 -0.55 0.6186859 -0.8889810 0.5418575
## 4vs7 -0.95 0.6186859 -1.5355126 0.2837080
## 4vs8 -0.90 0.6186859 -1.4546962 0.3063777
## 5vs6 -0.80 0.6186859 -1.2930633 0.3532791
##
## $`0.7144`
## coefficients sigma tstat pvalues
## 4vs9 -0.2833333 0.7143969 -0.3966049 0.8179899
## 4vs10 -1.6833333 0.7143969 -2.3562998 0.1100068
## 4vs11 -0.9833333 0.7143969 -1.3764524 0.3255944
## 4vs12 -0.9833333 0.7143969 -1.3764524 0.3255944
## 4vs13 -0.6833333 0.7143969 -0.9565177 0.5119170# Comparações múltiplas a 10%.
results_m0 <- wzRfun::apmc(X = L,
model = m0,
focus = "linh",
test = "fdr")
results_m0## linh fit lwr upr cld
## 1 1 5.633333 5.113140 6.153526 df
## 2 2 6.250000 5.729807 6.770193 bcf
## 3 3 7.150000 6.629807 7.670193 ab
## 4 4 5.244444 4.248351 6.240538 ef
## 5 5 4.994444 3.998351 5.990538 f
## 6 6 5.794444 4.798351 6.790538 bcf
## 7 7 6.194444 5.198351 7.190538 bcf
## 8 8 6.144444 5.148351 7.140538 bcf
## 9 9 5.527778 4.531684 6.523872 cf
## 10 10 6.927778 5.931684 7.923872 acf
## 11 11 6.227778 5.231684 7.223872 bcf
## 12 12 6.227778 5.231684 7.223872 bcf
## 13 13 5.927778 4.931684 6.923872 bcf
## 14 14 7.277778 6.281684 8.273872 ace
## 15 15 7.577778 6.581684 8.573872 ac
## 16 16 8.377778 7.381684 9.373872 a
## 17 17 7.227778 6.231684 8.223872 ace
## 18 18 7.127778 6.131684 8.123872 acde# Gráfico de segmentos para as estimativas intervalares.
ggplot(data = results_m0,
mapping = aes(x = fit, y = reorder(linh, fit))) +
geom_point() +
geom_errorbarh(mapping = aes(xmin = lwr, xmax = upr),
height = 0) +
geom_label(mapping = aes(label = sprintf("%0.2f%s", fit, cld)),
label.padding = unit(0.15, "lines"),
fill = "black",
colour = "white",
size = 3,
nudge_x = 0.25,
vjust = 0.5) +
labs(x = "Produção",
y = "Linhagens")
# Ajuste do modelo de efeito aleatório de bloc.
mm0 <- lmer(prod ~ exp + (1 | exp:bloc) + linh, data = da)
# Quadro de teste de Wald.
anova(mm0)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## exp 0.8282 0.41412 2 4.1993 1.0819 0.417863
## linh 20.4282 1.20166 17 24.9999 3.1393 0.004722 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Estimativas dos parâmetros dos termos de efeito.
summary(mm0)## Linear mixed model fit by REML. t-tests use Satterthwaite's method
## [lmerModLmerTest]
## Formula: prod ~ exp + (1 | exp:bloc) + linh
## Data: da
##
## REML criterion at convergence: 77.4
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.11931 -0.49619 -0.06273 0.61283 2.11931
##
## Random effects:
## Groups Name Variance Std.Dev.
## exp:bloc (Intercept) 0.3820 0.6180
## Residual 0.3828 0.6187
## Number of obs: 48, groups: exp:bloc, 6
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 5.6389 0.5453 5.6424 10.342 7.04e-05 ***
## exp2 0.5167 0.7138 4.1993 0.724 0.507470
## exp3 -0.5333 0.7138 4.1993 -0.747 0.494653
## linh2 0.6167 0.3572 24.9999 1.726 0.096616 .
## linh3 1.5167 0.3572 24.9999 4.246 0.000263 ***
## linh4 -0.3889 0.5456 24.9999 -0.713 0.482611
## linh5 -0.6389 0.5456 24.9999 -1.171 0.252672
## linh6 0.1611 0.5456 24.9999 0.295 0.770222
## linh7 0.5611 0.5456 24.9999 1.028 0.313623
## linh8 0.5111 0.5456 24.9999 0.937 0.357857
## linh9 -0.1056 0.5456 24.9999 -0.193 0.848165
## linh10 1.2944 0.5456 24.9999 2.372 0.025681 *
## linh11 0.5944 0.5456 24.9999 1.089 0.286341
## linh12 0.5944 0.5456 24.9999 1.089 0.286341
## linh13 0.2944 0.5456 24.9999 0.540 0.594220
## linh14 1.6444 0.5456 24.9999 3.014 0.005841 **
## linh15 1.9444 0.5456 24.9999 3.564 0.001504 **
## linh16 2.7444 0.5456 24.9999 5.030 3.45e-05 ***
## linh17 1.5944 0.5456 24.9999 2.922 0.007273 **
## linh18 1.4944 0.5456 24.9999 2.739 0.011199 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 20 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Médias marginais ajustadas.
emm <- emmeans(mm0, specs = ~linh)
emm## linh emmean SE df lower.CL upper.CL
## 1 5.63 0.357 8.85 4.82 6.44
## 2 6.25 0.357 8.85 5.44 7.06
## 3 7.15 0.357 8.85 6.34 7.96
## 4 5.24 0.546 23.60 4.12 6.37
## 5 4.99 0.546 23.60 3.87 6.12
## 6 5.79 0.546 23.60 4.67 6.92
## 7 6.19 0.546 23.60 5.07 7.32
## 8 6.14 0.546 23.60 5.02 7.27
## 9 5.53 0.546 23.60 4.40 6.65
## 10 6.93 0.546 23.60 5.80 8.05
## 11 6.23 0.546 23.60 5.10 7.35
## 12 6.23 0.546 23.60 5.10 7.35
## 13 5.93 0.546 23.60 4.80 7.05
## 14 7.28 0.546 23.60 6.15 8.40
## 15 7.58 0.546 23.60 6.45 8.70
## 16 8.38 0.546 23.60 7.25 9.50
## 17 7.23 0.546 23.60 6.10 8.35
## 18 7.13 0.546 23.60 6.00 8.25
##
## Results are averaged over the levels of: exp
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95# Extração da matriz de funções lineares.
grid <- attr(emm, "grid")
L <- attr(emm, "linfct")
rownames(L) <- grid[[1]]
# Contrastes par a par.
ctr <- summary(glht(mm0, linfct = all_pairwise(L)),
test = adjusted(type = "fdr"))
# Erros padrões de vários tamanhos conforme estrutura de associação.
v <- c("coefficients", "sigma", "tstat", "pvalues")
ctr$test[v] %>%
as.data.frame() %>%
split(., round(.$sigma, 4)) %>%
map(head, n = 10)## $`0.3572`
## coefficients sigma tstat pvalues
## 1vs2 -0.6166667 0.3571994 -1.726393 0.1953686366
## 1vs3 -1.5166667 0.3571994 -4.245995 0.0008324193
## 2vs3 -0.9000000 0.3571994 -2.519601 0.0599188193
##
## $`0.5456`
## coefficients sigma tstat pvalues
## 1vs4 0.3888889 0.5456311 0.7127323 0.63885745
## 1vs5 0.6388889 0.5456311 1.1709173 0.39329466
## 1vs6 -0.1611111 0.5456311 -0.2952748 0.87664885
## 1vs7 -0.5611111 0.5456311 -1.0283709 0.45254429
## 1vs8 -0.5111111 0.5456311 -0.9367339 0.50623391
## 1vs9 0.1055556 0.5456311 0.1934559 0.93105435
## 1vs10 -1.2944444 0.5456311 -2.3723803 0.07511403
## 1vs11 -0.5944444 0.5456311 -1.0894622 0.41802344
## 1vs12 -0.5944444 0.5456311 -1.0894622 0.41802344
## 1vs13 -0.2944444 0.5456311 -0.5396402 0.73922228
##
## $`0.6187`
## coefficients sigma tstat pvalues
## 4vs5 0.25 0.6186875 0.40408122 0.8138094
## 4vs6 -0.55 0.6186875 -0.88897869 0.5298539
## 4vs7 -0.95 0.6186875 -1.53550864 0.2577408
## 4vs8 -0.90 0.6186875 -1.45469240 0.2822839
## 5vs6 -0.80 0.6186875 -1.29305991 0.3331836
## 5vs7 -1.20 0.6186875 -1.93958986 0.1616402
## 5vs8 -1.15 0.6186875 -1.85877362 0.1692643
## 6vs7 -0.40 0.6186875 -0.64652995 0.6831400
## 6vs8 -0.35 0.6186875 -0.56571371 0.7227523
## 7vs8 0.05 0.6186875 0.08081624 0.9737754
##
## $`0.7144`
## coefficients sigma tstat pvalues
## 4vs9 -0.2833333 0.7143988 -0.3966039 0.8140301309
## 4vs10 -1.6833333 0.7143988 -2.3562937 0.0763276441
## 4vs11 -0.9833333 0.7143988 -1.3764488 0.3036289266
## 4vs12 -0.9833333 0.7143988 -1.3764488 0.3036289266
## 4vs13 -0.6833333 0.7143988 -0.9565152 0.4984445537
## 4vs14 -2.0333333 0.7143988 -2.8462161 0.0270762013
## 4vs15 -2.3333333 0.7143988 -3.2661496 0.0128308891
## 4vs16 -3.1333333 0.7143988 -4.3859724 0.0005888908
## 4vs17 -1.9833333 0.7143988 -2.7762272 0.0323617163
## 4vs18 -1.8833333 0.7143988 -2.6362493 0.0442265534# As mesmas comparações múltiplas.
results_mm0 <- wzRfun::apmc(L,
model = mm0,
focus = "linh",
test = "fdr")
results_mm0## linh fit lwr upr cld
## 1 1 5.633333 4.933599 6.333067 de
## 2 2 6.250000 5.550266 6.949734 bce
## 3 3 7.150000 6.450266 7.849734 ab
## 4 4 5.244444 4.175265 6.313623 de
## 5 5 4.994444 3.925265 6.063623 e
## 6 6 5.794444 4.725265 6.863623 bce
## 7 7 6.194444 5.125265 7.263623 bce
## 8 8 6.144444 5.075265 7.213623 bce
## 9 9 5.527778 4.458599 6.596957 ce
## 10 10 6.927778 5.858599 7.996957 acd
## 11 11 6.227778 5.158599 7.296957 bce
## 12 12 6.227778 5.158599 7.296957 bce
## 13 13 5.927778 4.858599 6.996957 bce
## 14 14 7.277778 6.208599 8.346957 ac
## 15 15 7.577778 6.508599 8.646957 ab
## 16 16 8.377778 7.308599 9.446957 a
## 17 17 7.227778 6.158599 8.296957 ac
## 18 18 7.127778 6.058599 8.196957 ac# Gráfico de segmentos para as estimativas intervalares.
ggplot(data = results_mm0,
mapping = aes(x = fit, y = reorder(linh, fit))) +
geom_point() +
geom_errorbarh(mapping = aes(xmin = lwr, xmax = upr),
height = 0) +
geom_label(mapping = aes(label = sprintf("%0.2f%s", fit, cld)),
label.padding = unit(0.15, "lines"),
fill = "black",
colour = "white",
size = 3,
nudge_x = 0.25,
vjust = 0.5) +
labs(x = "Produção",
y = "Linhagens")
18.3 ZimmermannTb8.5
Os dados em ZimmermannTb8.5 são de um experimento para avaliação de cultivares de feijão em um delineamento de blocos aumentados de Federer. O experimento teve \(b = 10\) blocos de tamanho \(k = 9\) para acomodar \(t = 54\) linhagens das quais 4 são tratamentos comuns a todos os blocos.
da <- labestData::ZimmermannTb8.5
str(da)## 'data.frame': 90 obs. of 3 variables:
## $ bloc: Factor w/ 10 levels "1","2","3","4",..: 1 1 1 1 1 1 1 1 1 2 ...
## $ linh: Factor w/ 54 levels "1","2","3","4",..: 9 5 8 3 4 1 2 6 7 14 ...
## $ prod: num 761 1516 1692 1563 2019 ...with(da, table(linh, bloc)) %>%
addmargins() %>%
print.table(zero.print = ".")## bloc
## linh 1 2 3 4 5 6 7 8 9 10 Sum
## 1 1 1 1 1 1 1 1 1 1 1 10
## 2 1 1 1 1 1 1 1 1 1 1 10
## 3 1 1 1 1 1 1 1 1 1 1 10
## 4 1 1 1 1 1 1 1 1 1 1 10
## 5 1 . . . . . . . . . 1
## 6 1 . . . . . . . . . 1
## 7 1 . . . . . . . . . 1
## 8 1 . . . . . . . . . 1
## 9 1 . . . . . . . . . 1
## 17 . 1 . . . . . . . . 1
## 18 . 1 . . . . . . . . 1
## 19 . 1 . . . . . . . . 1
## 20 . 1 . . . . . . . . 1
## 21 . 1 . . . . . . . . 1
## 29 . . 1 . . . . . . . 1
## 30 . . 1 . . . . . . . 1
## 31 . . 1 . . . . . . . 1
## 32 . . 1 . . . . . . . 1
## 33 . . 1 . . . . . . . 1
## 41 . . . 1 . . . . . . 1
## 42 . . . 1 . . . . . . 1
## 43 . . . 1 . . . . . . 1
## 44 . . . 1 . . . . . . 1
## 45 . . . 1 . . . . . . 1
## 53 . . . . 1 . . . . . 1
## 54 . . . . 1 . . . . . 1
## 55 . . . . 1 . . . . . 1
## 56 . . . . 1 . . . . . 1
## 57 . . . . 1 . . . . . 1
## 65 . . . . . 1 . . . . 1
## 66 . . . . . 1 . . . . 1
## 67 . . . . . 1 . . . . 1
## 68 . . . . . 1 . . . . 1
## 69 . . . . . 1 . . . . 1
## 77 . . . . . . 1 . . . 1
## 78 . . . . . . 1 . . . 1
## 79 . . . . . . 1 . . . 1
## 80 . . . . . . 1 . . . 1
## 81 . . . . . . 1 . . . 1
## 89 . . . . . . . 1 . . 1
## 90 . . . . . . . 1 . . 1
## 91 . . . . . . . 1 . . 1
## 92 . . . . . . . 1 . . 1
## 93 . . . . . . . 1 . . 1
## 101 . . . . . . . . 1 . 1
## 102 . . . . . . . . 1 . 1
## 103 . . . . . . . . 1 . 1
## 104 . . . . . . . . 1 . 1
## 105 . . . . . . . . 1 . 1
## 113 . . . . . . . . . 1 1
## 114 . . . . . . . . . 1 1
## 115 . . . . . . . . . 1 1
## 116 . . . . . . . . . 1 1
## 117 . . . . . . . . . 1 1
## Sum 9 9 9 9 9 9 9 9 9 9 90library(igraph)
# Todos os possíveis pares.
pares <- apply(combn(sort(as.character(levels(da$linh))), m = 2),
MARGIN = 2,
FUN = paste0,
collapse = "_") %>%
tibble(name = ., value = 0)
# Quantas vezes cada par ocorre junto.
by(data = as.character(da$linh),
INDICES = da$bloc,
FUN = function(x) {
apply(combn(sort(x), m = 2),
MARGIN = 2,
FUN = paste0,
collapse = "_")
}) %>%
flatten_chr() %>%
table() %>%
c() %>%
enframe() %>%
bind_rows(pares) %>%
distinct(name, .keep_all = TRUE) %>%
split(x = .$name, f = .$value)
edg <- by(data = as.integer(da$linh),
INDICES = da$bloc,
FUN = combn,
m = 2) %>%
flatten_int()
p <- 4
pal <- sample(colors(), size = nlevels(da$bloc) + 1)
clr <- rep(head(pal, n = -1),
tapply(da$bloc, da$bloc, length) - p)
clr <- append(clr, rep(tail(pal, n = 1), p),
after = 0)
ghp <- graph(edg, directed = FALSE)
plot(ghp,
vertex.color = clr,
vertex.size = 7,
layout = layout_nicely,
edge.curved = FALSE)# Modelo de efeitos fixos.
m0 <- lm(terms(prod ~ bloc + linh, keep.order = TRUE),
data = da)
# Quado de anova com hipóteses marginais.
Anova(m0)## Anova Table (Type II tests)
##
## Response: prod
## Sum Sq Df F value Pr(>F)
## bloc 492838 9 2.1975 0.05508 .
## linh 6403550 53 4.8486 1.716e-05 ***
## Residuals 672811 27
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Médias marginais ajustadas.
emm <- emmeans(m0, specs = ~linh)
emm## linh emmean SE df lower.CL upper.CL
## 1 1841 49.9 27 1738 1943
## 2 1598 49.9 27 1496 1701
## 3 1773 49.9 27 1670 1875
## 4 1983 49.9 27 1881 2086
## 5 1501 174.7 27 1142 1859
## 6 1230 174.7 27 871 1588
## 7 1188 174.7 27 829 1546
## 8 1677 174.7 27 1318 2035
## 9 746 174.7 27 387 1104
## 17 1652 174.7 27 1293 2010
## 18 1622 174.7 27 1263 1980
## 19 1903 174.7 27 1544 2261
## 20 1754 174.7 27 1395 2112
## 21 1402 174.7 27 1043 1760
## 29 1231 174.7 27 872 1589
## 30 1203 174.7 27 844 1561
## 31 1594 174.7 27 1235 1952
## 32 1274 174.7 27 915 1632
## 33 1783 174.7 27 1424 2141
## 41 1585 174.7 27 1227 1944
## 42 1447 174.7 27 1089 1806
## 43 1304 174.7 27 946 1663
## 44 1612 174.7 27 1254 1971
## 45 1363 174.7 27 1005 1722
## 53 1838 174.7 27 1480 2197
## 54 1177 174.7 27 819 1536
## 55 1208 174.7 27 850 1567
## 56 1474 174.7 27 1116 1833
## 57 1468 174.7 27 1110 1827
## 65 1221 174.7 27 863 1580
## 66 1488 174.7 27 1130 1847
## 67 1356 174.7 27 998 1715
## 68 1528 174.7 27 1170 1887
## 69 1898 174.7 27 1540 2257
## 77 1767 174.7 27 1408 2125
## 78 2243 174.7 27 1884 2601
## 79 2535 174.7 27 2176 2893
## 80 2095 174.7 27 1736 2453
## 81 1694 174.7 27 1335 2052
## 89 1992 174.7 27 1634 2351
## 90 1217 174.7 27 859 1576
## 91 1266 174.7 27 908 1625
## 92 1210 174.7 27 852 1569
## 93 1490 174.7 27 1132 1849
## 101 1422 174.7 27 1064 1781
## 102 1874 174.7 27 1516 2233
## 103 1868 174.7 27 1510 2227
## 104 1440 174.7 27 1082 1799
## 105 1792 174.7 27 1434 2151
## 113 2100 174.7 27 1742 2459
## 114 1370 174.7 27 1012 1729
## 115 2276 174.7 27 1918 2635
## 116 1525 174.7 27 1167 1884
## 117 2271 174.7 27 1913 2630
##
## Results are averaged over the levels of: bloc
## Confidence level used: 0.95# Extração da matriz de funções lineares.
L <- attr(emm, "linfct")
grid <- attr(emm, "grid")
rownames(L) <- grid[[1]]
# Entenda como são obtidas as médias marginais.
MASS::fractions(t(L))## 1 2 3 4 5 6 7 8 9 17 18
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1
## bloc2 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc3 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc4 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc5 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc6 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc7 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc8 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc9 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## linh2 0 1 0 0 0 0 0 0 0 0 0
## linh3 0 0 1 0 0 0 0 0 0 0 0
## linh4 0 0 0 1 0 0 0 0 0 0 0
## linh5 0 0 0 0 1 0 0 0 0 0 0
## linh6 0 0 0 0 0 1 0 0 0 0 0
## linh7 0 0 0 0 0 0 1 0 0 0 0
## linh8 0 0 0 0 0 0 0 1 0 0 0
## linh9 0 0 0 0 0 0 0 0 1 0 0
## linh17 0 0 0 0 0 0 0 0 0 1 0
## linh18 0 0 0 0 0 0 0 0 0 0 1
## linh19 0 0 0 0 0 0 0 0 0 0 0
## linh20 0 0 0 0 0 0 0 0 0 0 0
## linh21 0 0 0 0 0 0 0 0 0 0 0
## linh29 0 0 0 0 0 0 0 0 0 0 0
## linh30 0 0 0 0 0 0 0 0 0 0 0
## linh31 0 0 0 0 0 0 0 0 0 0 0
## linh32 0 0 0 0 0 0 0 0 0 0 0
## linh33 0 0 0 0 0 0 0 0 0 0 0
## linh41 0 0 0 0 0 0 0 0 0 0 0
## linh42 0 0 0 0 0 0 0 0 0 0 0
## linh43 0 0 0 0 0 0 0 0 0 0 0
## linh44 0 0 0 0 0 0 0 0 0 0 0
## linh45 0 0 0 0 0 0 0 0 0 0 0
## linh53 0 0 0 0 0 0 0 0 0 0 0
## linh54 0 0 0 0 0 0 0 0 0 0 0
## linh55 0 0 0 0 0 0 0 0 0 0 0
## linh56 0 0 0 0 0 0 0 0 0 0 0
## linh57 0 0 0 0 0 0 0 0 0 0 0
## linh65 0 0 0 0 0 0 0 0 0 0 0
## linh66 0 0 0 0 0 0 0 0 0 0 0
## linh67 0 0 0 0 0 0 0 0 0 0 0
## linh68 0 0 0 0 0 0 0 0 0 0 0
## linh69 0 0 0 0 0 0 0 0 0 0 0
## linh77 0 0 0 0 0 0 0 0 0 0 0
## linh78 0 0 0 0 0 0 0 0 0 0 0
## linh79 0 0 0 0 0 0 0 0 0 0 0
## linh80 0 0 0 0 0 0 0 0 0 0 0
## linh81 0 0 0 0 0 0 0 0 0 0 0
## linh89 0 0 0 0 0 0 0 0 0 0 0
## linh90 0 0 0 0 0 0 0 0 0 0 0
## linh91 0 0 0 0 0 0 0 0 0 0 0
## linh92 0 0 0 0 0 0 0 0 0 0 0
## linh93 0 0 0 0 0 0 0 0 0 0 0
## linh101 0 0 0 0 0 0 0 0 0 0 0
## linh102 0 0 0 0 0 0 0 0 0 0 0
## linh103 0 0 0 0 0 0 0 0 0 0 0
## linh104 0 0 0 0 0 0 0 0 0 0 0
## linh105 0 0 0 0 0 0 0 0 0 0 0
## linh113 0 0 0 0 0 0 0 0 0 0 0
## linh114 0 0 0 0 0 0 0 0 0 0 0
## linh115 0 0 0 0 0 0 0 0 0 0 0
## linh116 0 0 0 0 0 0 0 0 0 0 0
## linh117 0 0 0 0 0 0 0 0 0 0 0
## 19 20 21 29 30 31 32 33 41 42 43
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1
## bloc2 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc3 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc4 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc5 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc6 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc7 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc8 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc9 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## linh2 0 0 0 0 0 0 0 0 0 0 0
## linh3 0 0 0 0 0 0 0 0 0 0 0
## linh4 0 0 0 0 0 0 0 0 0 0 0
## linh5 0 0 0 0 0 0 0 0 0 0 0
## linh6 0 0 0 0 0 0 0 0 0 0 0
## linh7 0 0 0 0 0 0 0 0 0 0 0
## linh8 0 0 0 0 0 0 0 0 0 0 0
## linh9 0 0 0 0 0 0 0 0 0 0 0
## linh17 0 0 0 0 0 0 0 0 0 0 0
## linh18 0 0 0 0 0 0 0 0 0 0 0
## linh19 1 0 0 0 0 0 0 0 0 0 0
## linh20 0 1 0 0 0 0 0 0 0 0 0
## linh21 0 0 1 0 0 0 0 0 0 0 0
## linh29 0 0 0 1 0 0 0 0 0 0 0
## linh30 0 0 0 0 1 0 0 0 0 0 0
## linh31 0 0 0 0 0 1 0 0 0 0 0
## linh32 0 0 0 0 0 0 1 0 0 0 0
## linh33 0 0 0 0 0 0 0 1 0 0 0
## linh41 0 0 0 0 0 0 0 0 1 0 0
## linh42 0 0 0 0 0 0 0 0 0 1 0
## linh43 0 0 0 0 0 0 0 0 0 0 1
## linh44 0 0 0 0 0 0 0 0 0 0 0
## linh45 0 0 0 0 0 0 0 0 0 0 0
## linh53 0 0 0 0 0 0 0 0 0 0 0
## linh54 0 0 0 0 0 0 0 0 0 0 0
## linh55 0 0 0 0 0 0 0 0 0 0 0
## linh56 0 0 0 0 0 0 0 0 0 0 0
## linh57 0 0 0 0 0 0 0 0 0 0 0
## linh65 0 0 0 0 0 0 0 0 0 0 0
## linh66 0 0 0 0 0 0 0 0 0 0 0
## linh67 0 0 0 0 0 0 0 0 0 0 0
## linh68 0 0 0 0 0 0 0 0 0 0 0
## linh69 0 0 0 0 0 0 0 0 0 0 0
## linh77 0 0 0 0 0 0 0 0 0 0 0
## linh78 0 0 0 0 0 0 0 0 0 0 0
## linh79 0 0 0 0 0 0 0 0 0 0 0
## linh80 0 0 0 0 0 0 0 0 0 0 0
## linh81 0 0 0 0 0 0 0 0 0 0 0
## linh89 0 0 0 0 0 0 0 0 0 0 0
## linh90 0 0 0 0 0 0 0 0 0 0 0
## linh91 0 0 0 0 0 0 0 0 0 0 0
## linh92 0 0 0 0 0 0 0 0 0 0 0
## linh93 0 0 0 0 0 0 0 0 0 0 0
## linh101 0 0 0 0 0 0 0 0 0 0 0
## linh102 0 0 0 0 0 0 0 0 0 0 0
## linh103 0 0 0 0 0 0 0 0 0 0 0
## linh104 0 0 0 0 0 0 0 0 0 0 0
## linh105 0 0 0 0 0 0 0 0 0 0 0
## linh113 0 0 0 0 0 0 0 0 0 0 0
## linh114 0 0 0 0 0 0 0 0 0 0 0
## linh115 0 0 0 0 0 0 0 0 0 0 0
## linh116 0 0 0 0 0 0 0 0 0 0 0
## linh117 0 0 0 0 0 0 0 0 0 0 0
## 44 45 53 54 55 56 57 65 66 67 68
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1
## bloc2 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc3 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc4 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc5 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc6 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc7 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc8 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc9 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## linh2 0 0 0 0 0 0 0 0 0 0 0
## linh3 0 0 0 0 0 0 0 0 0 0 0
## linh4 0 0 0 0 0 0 0 0 0 0 0
## linh5 0 0 0 0 0 0 0 0 0 0 0
## linh6 0 0 0 0 0 0 0 0 0 0 0
## linh7 0 0 0 0 0 0 0 0 0 0 0
## linh8 0 0 0 0 0 0 0 0 0 0 0
## linh9 0 0 0 0 0 0 0 0 0 0 0
## linh17 0 0 0 0 0 0 0 0 0 0 0
## linh18 0 0 0 0 0 0 0 0 0 0 0
## linh19 0 0 0 0 0 0 0 0 0 0 0
## linh20 0 0 0 0 0 0 0 0 0 0 0
## linh21 0 0 0 0 0 0 0 0 0 0 0
## linh29 0 0 0 0 0 0 0 0 0 0 0
## linh30 0 0 0 0 0 0 0 0 0 0 0
## linh31 0 0 0 0 0 0 0 0 0 0 0
## linh32 0 0 0 0 0 0 0 0 0 0 0
## linh33 0 0 0 0 0 0 0 0 0 0 0
## linh41 0 0 0 0 0 0 0 0 0 0 0
## linh42 0 0 0 0 0 0 0 0 0 0 0
## linh43 0 0 0 0 0 0 0 0 0 0 0
## linh44 1 0 0 0 0 0 0 0 0 0 0
## linh45 0 1 0 0 0 0 0 0 0 0 0
## linh53 0 0 1 0 0 0 0 0 0 0 0
## linh54 0 0 0 1 0 0 0 0 0 0 0
## linh55 0 0 0 0 1 0 0 0 0 0 0
## linh56 0 0 0 0 0 1 0 0 0 0 0
## linh57 0 0 0 0 0 0 1 0 0 0 0
## linh65 0 0 0 0 0 0 0 1 0 0 0
## linh66 0 0 0 0 0 0 0 0 1 0 0
## linh67 0 0 0 0 0 0 0 0 0 1 0
## linh68 0 0 0 0 0 0 0 0 0 0 1
## linh69 0 0 0 0 0 0 0 0 0 0 0
## linh77 0 0 0 0 0 0 0 0 0 0 0
## linh78 0 0 0 0 0 0 0 0 0 0 0
## linh79 0 0 0 0 0 0 0 0 0 0 0
## linh80 0 0 0 0 0 0 0 0 0 0 0
## linh81 0 0 0 0 0 0 0 0 0 0 0
## linh89 0 0 0 0 0 0 0 0 0 0 0
## linh90 0 0 0 0 0 0 0 0 0 0 0
## linh91 0 0 0 0 0 0 0 0 0 0 0
## linh92 0 0 0 0 0 0 0 0 0 0 0
## linh93 0 0 0 0 0 0 0 0 0 0 0
## linh101 0 0 0 0 0 0 0 0 0 0 0
## linh102 0 0 0 0 0 0 0 0 0 0 0
## linh103 0 0 0 0 0 0 0 0 0 0 0
## linh104 0 0 0 0 0 0 0 0 0 0 0
## linh105 0 0 0 0 0 0 0 0 0 0 0
## linh113 0 0 0 0 0 0 0 0 0 0 0
## linh114 0 0 0 0 0 0 0 0 0 0 0
## linh115 0 0 0 0 0 0 0 0 0 0 0
## linh116 0 0 0 0 0 0 0 0 0 0 0
## linh117 0 0 0 0 0 0 0 0 0 0 0
## 69 77 78 79 80 81 89 90 91 92 93
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1
## bloc2 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc3 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc4 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc5 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc6 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc7 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc8 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc9 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## linh2 0 0 0 0 0 0 0 0 0 0 0
## linh3 0 0 0 0 0 0 0 0 0 0 0
## linh4 0 0 0 0 0 0 0 0 0 0 0
## linh5 0 0 0 0 0 0 0 0 0 0 0
## linh6 0 0 0 0 0 0 0 0 0 0 0
## linh7 0 0 0 0 0 0 0 0 0 0 0
## linh8 0 0 0 0 0 0 0 0 0 0 0
## linh9 0 0 0 0 0 0 0 0 0 0 0
## linh17 0 0 0 0 0 0 0 0 0 0 0
## linh18 0 0 0 0 0 0 0 0 0 0 0
## linh19 0 0 0 0 0 0 0 0 0 0 0
## linh20 0 0 0 0 0 0 0 0 0 0 0
## linh21 0 0 0 0 0 0 0 0 0 0 0
## linh29 0 0 0 0 0 0 0 0 0 0 0
## linh30 0 0 0 0 0 0 0 0 0 0 0
## linh31 0 0 0 0 0 0 0 0 0 0 0
## linh32 0 0 0 0 0 0 0 0 0 0 0
## linh33 0 0 0 0 0 0 0 0 0 0 0
## linh41 0 0 0 0 0 0 0 0 0 0 0
## linh42 0 0 0 0 0 0 0 0 0 0 0
## linh43 0 0 0 0 0 0 0 0 0 0 0
## linh44 0 0 0 0 0 0 0 0 0 0 0
## linh45 0 0 0 0 0 0 0 0 0 0 0
## linh53 0 0 0 0 0 0 0 0 0 0 0
## linh54 0 0 0 0 0 0 0 0 0 0 0
## linh55 0 0 0 0 0 0 0 0 0 0 0
## linh56 0 0 0 0 0 0 0 0 0 0 0
## linh57 0 0 0 0 0 0 0 0 0 0 0
## linh65 0 0 0 0 0 0 0 0 0 0 0
## linh66 0 0 0 0 0 0 0 0 0 0 0
## linh67 0 0 0 0 0 0 0 0 0 0 0
## linh68 0 0 0 0 0 0 0 0 0 0 0
## linh69 1 0 0 0 0 0 0 0 0 0 0
## linh77 0 1 0 0 0 0 0 0 0 0 0
## linh78 0 0 1 0 0 0 0 0 0 0 0
## linh79 0 0 0 1 0 0 0 0 0 0 0
## linh80 0 0 0 0 1 0 0 0 0 0 0
## linh81 0 0 0 0 0 1 0 0 0 0 0
## linh89 0 0 0 0 0 0 1 0 0 0 0
## linh90 0 0 0 0 0 0 0 1 0 0 0
## linh91 0 0 0 0 0 0 0 0 1 0 0
## linh92 0 0 0 0 0 0 0 0 0 1 0
## linh93 0 0 0 0 0 0 0 0 0 0 1
## linh101 0 0 0 0 0 0 0 0 0 0 0
## linh102 0 0 0 0 0 0 0 0 0 0 0
## linh103 0 0 0 0 0 0 0 0 0 0 0
## linh104 0 0 0 0 0 0 0 0 0 0 0
## linh105 0 0 0 0 0 0 0 0 0 0 0
## linh113 0 0 0 0 0 0 0 0 0 0 0
## linh114 0 0 0 0 0 0 0 0 0 0 0
## linh115 0 0 0 0 0 0 0 0 0 0 0
## linh116 0 0 0 0 0 0 0 0 0 0 0
## linh117 0 0 0 0 0 0 0 0 0 0 0
## 101 102 103 104 105 113 114 115 116 117
## (Intercept) 1 1 1 1 1 1 1 1 1 1
## bloc2 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc3 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc4 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc5 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc6 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc7 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc8 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc9 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## bloc10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10 1/10
## linh2 0 0 0 0 0 0 0 0 0 0
## linh3 0 0 0 0 0 0 0 0 0 0
## linh4 0 0 0 0 0 0 0 0 0 0
## linh5 0 0 0 0 0 0 0 0 0 0
## linh6 0 0 0 0 0 0 0 0 0 0
## linh7 0 0 0 0 0 0 0 0 0 0
## linh8 0 0 0 0 0 0 0 0 0 0
## linh9 0 0 0 0 0 0 0 0 0 0
## linh17 0 0 0 0 0 0 0 0 0 0
## linh18 0 0 0 0 0 0 0 0 0 0
## linh19 0 0 0 0 0 0 0 0 0 0
## linh20 0 0 0 0 0 0 0 0 0 0
## linh21 0 0 0 0 0 0 0 0 0 0
## linh29 0 0 0 0 0 0 0 0 0 0
## linh30 0 0 0 0 0 0 0 0 0 0
## linh31 0 0 0 0 0 0 0 0 0 0
## linh32 0 0 0 0 0 0 0 0 0 0
## linh33 0 0 0 0 0 0 0 0 0 0
## linh41 0 0 0 0 0 0 0 0 0 0
## linh42 0 0 0 0 0 0 0 0 0 0
## linh43 0 0 0 0 0 0 0 0 0 0
## linh44 0 0 0 0 0 0 0 0 0 0
## linh45 0 0 0 0 0 0 0 0 0 0
## linh53 0 0 0 0 0 0 0 0 0 0
## linh54 0 0 0 0 0 0 0 0 0 0
## linh55 0 0 0 0 0 0 0 0 0 0
## linh56 0 0 0 0 0 0 0 0 0 0
## linh57 0 0 0 0 0 0 0 0 0 0
## linh65 0 0 0 0 0 0 0 0 0 0
## linh66 0 0 0 0 0 0 0 0 0 0
## linh67 0 0 0 0 0 0 0 0 0 0
## linh68 0 0 0 0 0 0 0 0 0 0
## linh69 0 0 0 0 0 0 0 0 0 0
## linh77 0 0 0 0 0 0 0 0 0 0
## linh78 0 0 0 0 0 0 0 0 0 0
## linh79 0 0 0 0 0 0 0 0 0 0
## linh80 0 0 0 0 0 0 0 0 0 0
## linh81 0 0 0 0 0 0 0 0 0 0
## linh89 0 0 0 0 0 0 0 0 0 0
## linh90 0 0 0 0 0 0 0 0 0 0
## linh91 0 0 0 0 0 0 0 0 0 0
## linh92 0 0 0 0 0 0 0 0 0 0
## linh93 0 0 0 0 0 0 0 0 0 0
## linh101 1 0 0 0 0 0 0 0 0 0
## linh102 0 1 0 0 0 0 0 0 0 0
## linh103 0 0 1 0 0 0 0 0 0 0
## linh104 0 0 0 1 0 0 0 0 0 0
## linh105 0 0 0 0 1 0 0 0 0 0
## linh113 0 0 0 0 0 1 0 0 0 0
## linh114 0 0 0 0 0 0 1 0 0 0
## linh115 0 0 0 0 0 0 0 1 0 0
## linh116 0 0 0 0 0 0 0 0 1 0
## linh117 0 0 0 0 0 0 0 0 0 1# Contrastes par a par.
ctr <- summary(glht(m0, linfct = all_pairwise(L)),
test = adjusted(type = "fdr"))
# Erros padrões de vários tamanhos conforme estrutura de associação.
v <- c("coefficients", "sigma", "tstat", "pvalues")
ctr$test[v] %>%
as.data.frame() %>%
split(., round(.$sigma, 4)) %>%
map(head, n = 5)## $`70.5959`
## coefficients sigma tstat pvalues
## 1vs2 242.6 70.59593 3.4364588 0.018100710
## 1vs3 68.2 70.59593 0.9660614 0.532872490
## 1vs4 -142.3 70.59593 -2.0156970 0.159974826
## 2vs3 -174.4 70.59593 -2.4703975 0.082192175
## 2vs4 -384.9 70.59593 -5.4521559 0.001253037
##
## $`181.7074`
## coefficients sigma tstat pvalues
## 1vs5 340.125 181.7074 1.8718281 0.1946125392
## 1vs6 611.125 181.7074 3.3632369 0.0202619743
## 1vs7 653.125 181.7074 3.5943777 0.0151433683
## 1vs8 164.125 181.7074 0.9032379 0.5633535256
## 1vs9 1095.125 181.7074 6.0268600 0.0005076058
##
## $`223.2439`
## coefficients sigma tstat pvalues
## 5vs6 271 223.2439 1.2139188 0.42352320
## 5vs7 313 223.2439 1.4020538 0.34771778
## 5vs8 -176 223.2439 -0.7883753 0.61910926
## 5vs9 755 223.2439 3.3819509 0.01988748
## 6vs7 42 223.2439 0.1881350 0.92876332
##
## $`249.5943`
## coefficients sigma tstat pvalues
## 5vs17 -151 249.5943 -0.6049818 0.7127985
## 5vs18 -121 249.5943 -0.4847867 0.7773143
## 5vs19 -402 249.5943 -1.6106137 0.2683567
## 5vs20 -253 249.5943 -1.0136450 0.5106724
## 5vs21 99 249.5943 0.3966437 0.8236821# Comparações múltiplas.
results_m0 <- wzRfun::apmc(X = L,
model = m0,
focus = "linh",
test = "fdr")
results_m0## linh fit lwr upr cld
## 1 1 1840.900 1738.4750 1943.325 cdi
## 2 2 1598.300 1495.8750 1700.725 fhl
## 3 3 1772.700 1670.2750 1875.125 cfijk
## 4 4 1983.200 1880.7750 2085.625 ad
## 5 5 1500.775 1142.2874 1859.263 dhijl
## 6 6 1229.775 871.2874 1588.263 lm
## 7 7 1187.775 829.2874 1546.263 ghm
## 8 8 1676.775 1318.2874 2035.263 cdfhijl
## 9 9 745.775 387.2874 1104.263 m
## 10 17 1651.775 1293.2874 2010.263 cdfhijl
## 11 18 1621.775 1263.2874 1980.263 cdfhijl
## 12 19 1902.775 1544.2874 2261.263 bdefijl
## 13 20 1753.775 1395.2874 2112.263 cdfhijl
## 14 21 1401.775 1043.2874 1760.263 fijlm
## 15 29 1230.775 872.2874 1589.263 lm
## 16 30 1202.775 844.2874 1561.263 lm
## 17 31 1593.775 1235.2874 1952.263 cdfhijl
## 18 32 1273.775 915.2874 1632.263 jlm
## 19 33 1782.775 1424.2874 2141.263 cdfhijl
## 20 41 1585.025 1226.5374 1943.513 cdfhijl
## 21 42 1447.025 1088.5374 1805.513 fijlm
## 22 43 1304.025 945.5374 1662.513 klm
## 23 44 1612.025 1253.5374 1970.513 cdfhijl
## 24 45 1363.025 1004.5374 1721.513 ilm
## 25 53 1838.025 1479.5374 2196.513 bdefgijl
## 26 54 1177.025 818.5374 1535.513 hm
## 27 55 1208.025 849.5374 1566.513 lm
## 28 56 1474.025 1115.5374 1832.513 dhijl
## 29 57 1468.025 1109.5374 1826.513 fhijl
## 30 65 1221.275 862.7874 1579.763 ehm
## 31 66 1488.275 1129.7874 1846.763 dhijl
## 32 67 1356.275 997.7874 1714.763 ilm
## 33 68 1528.275 1169.7874 1886.763 dhijl
## 34 69 1898.275 1539.7874 2256.763 bdfijl
## 35 77 1766.775 1408.2874 2125.263 cdfhijl
## 36 78 2242.775 1884.2874 2601.263 abc
## 37 79 2534.775 2176.2874 2893.263 b
## 38 80 2094.775 1736.2874 2453.263 bdf
## 39 81 1693.775 1335.2874 2052.263 cdfhijl
## 40 89 1992.275 1633.7874 2350.763 bdfik
## 41 90 1217.275 858.7874 1575.763 lm
## 42 91 1266.275 907.7874 1624.763 jlm
## 43 92 1210.275 851.7874 1568.763 lm
## 44 93 1490.275 1131.7874 1848.763 dhijl
## 45 101 1422.025 1063.5374 1780.513 fijlm
## 46 102 1874.025 1515.5374 2232.513 bdfhijl
## 47 103 1868.025 1509.5374 2226.513 bdfhijl
## 48 104 1440.025 1081.5374 1798.513 fijlm
## 49 105 1792.025 1433.5374 2150.513 cdfhijl
## 50 113 2100.275 1741.7874 2458.763 bdf
## 51 114 1370.275 1011.7874 1728.763 ilm
## 52 115 2276.275 1917.7874 2634.763 abc
## 53 116 1525.275 1166.7874 1883.763 dhijl
## 54 117 2271.275 1912.7874 2629.763 abc# Gráfico de segmentos para as estimativas intervalares.
ggplot(data = results_m0,
mapping = aes(x = fit, y = reorder(linh, fit))) +
geom_point() +
geom_errorbarh(mapping = aes(xmin = lwr, xmax = upr),
height = 0) +
geom_label(mapping = aes(
x = min(lwr),
label = sprintf("%0.0f %s", fit, cld)),
label.padding = unit(0.15, "lines"),
fill = "black",
colour = "white",
size = 3,
nudge_x = -80,
hjust = 0,
vjust = 0.5) +
labs(x = "Produção",
y = "Linhagens")
# Ajuste do modelo de efeito aleatório de bloc.
mm0 <- lmer(prod ~ (1 | bloc) + linh, data = da)
# Quadro de teste de Wald.
anova(mm0)## Type III Analysis of Variance Table with Satterthwaite's method
## Sum Sq Mean Sq NumDF DenDF F value Pr(>F)
## linh 7281233 137382 53 28.027 5.5131 3.277e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1# Estimativas dos parâmetros dos termos de efeito.
summary(mm0)## Linear mixed model fit by REML. t-tests use Satterthwaite's method
## [lmerModLmerTest]
## Formula: prod ~ (1 | bloc) + linh
## Data: da
##
## REML criterion at convergence: 482.9
##
## Scaled residuals:
## Min 1Q Median 3Q Max
## -2.287 0.000 0.000 0.000 1.693
##
## Random effects:
## Groups Name Variance Std.Dev.
## bloc (Intercept) 7460 86.37
## Residual 24919 157.86
## Number of obs: 90, groups: bloc, 10
##
## Fixed effects:
## Estimate Std. Error df t value Pr(>|t|)
## (Intercept) 1840.90 56.90 31.05 32.352 < 2e-16 ***
## linh2 -242.60 70.60 27.00 -3.436 0.001923 **
## linh3 -68.20 70.60 27.00 -0.966 0.342587
## linh4 142.30 70.60 27.00 2.016 0.053884 .
## linh5 -333.20 174.55 31.37 -1.909 0.065453 .
## linh6 -604.20 174.55 31.37 -3.462 0.001572 **
## linh7 -646.20 174.55 31.37 -3.702 0.000819 ***
## linh8 -157.20 174.55 31.37 -0.901 0.374659
## linh9 -1088.20 174.55 31.37 -6.234 5.99e-07 ***
## linh17 -128.04 174.55 31.37 -0.734 0.468646
## linh18 -158.04 174.55 31.37 -0.905 0.372121
## linh19 122.96 174.55 31.37 0.704 0.486361
## linh20 -26.04 174.55 31.37 -0.149 0.882340
## linh21 -378.04 174.55 31.37 -2.166 0.038034 *
## linh29 -678.74 174.55 31.37 -3.889 0.000490 ***
## linh30 -706.74 174.55 31.37 -4.049 0.000313 ***
## linh31 -315.74 174.55 31.37 -1.809 0.080058 .
## linh32 -635.74 174.55 31.37 -3.642 0.000965 ***
## linh33 -126.74 174.55 31.37 -0.726 0.473163
## linh41 -359.64 174.55 31.37 -2.060 0.047731 *
## linh42 -497.64 174.55 31.37 -2.851 0.007640 **
## linh43 -640.64 174.55 31.37 -3.670 0.000893 ***
## linh44 -332.64 174.55 31.37 -1.906 0.065881 .
## linh45 -581.64 174.55 31.37 -3.332 0.002217 **
## linh53 13.04 174.55 31.37 0.075 0.940918
## linh54 -647.96 174.55 31.37 -3.712 0.000797 ***
## linh55 -616.96 174.55 31.37 -3.535 0.001291 **
## linh56 -350.96 174.55 31.37 -2.011 0.053018 .
## linh57 -356.96 174.55 31.37 -2.045 0.049312 *
## linh65 -593.36 174.55 31.37 -3.399 0.001855 **
## linh66 -326.36 174.55 31.37 -1.870 0.070876 .
## linh67 -458.36 174.55 31.37 -2.626 0.013241 *
## linh68 -286.36 174.55 31.37 -1.641 0.110873
## linh69 83.64 174.55 31.37 0.479 0.635115
## linh77 -20.33 174.55 31.37 -0.116 0.908039
## linh78 455.67 174.55 31.37 2.611 0.013737 *
## linh79 747.67 174.55 31.37 4.284 0.000162 ***
## linh80 307.67 174.55 31.37 1.763 0.087688 .
## linh81 -93.33 174.55 31.37 -0.535 0.596642
## linh89 118.49 174.55 31.37 0.679 0.502227
## linh90 -656.51 174.55 31.37 -3.761 0.000696 ***
## linh91 -607.51 174.55 31.37 -3.481 0.001494 **
## linh92 -663.51 174.55 31.37 -3.801 0.000624 ***
## linh93 -383.51 174.55 31.37 -2.197 0.035512 *
## linh101 -413.88 174.55 31.37 -2.371 0.024047 *
## linh102 38.12 174.55 31.37 0.218 0.828538
## linh103 32.12 174.55 31.37 0.184 0.855185
## linh104 -395.88 174.55 31.37 -2.268 0.030354 *
## linh105 -43.88 174.55 31.37 -0.251 0.803144
## linh113 295.65 174.55 31.37 1.694 0.100200
## linh114 -434.35 174.55 31.37 -2.488 0.018340 *
## linh115 471.65 174.55 31.37 2.702 0.011017 *
## linh116 -279.35 174.55 31.37 -1.600 0.119529
## linh117 466.65 174.55 31.37 2.674 0.011809 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1##
## Correlation matrix not shown by default, as p = 54 > 12.
## Use print(x, correlation=TRUE) or
## vcov(x) if you need it# Médias marginais ajustadas.
emm <- emmeans(mm0, specs = ~linh)
emm## linh emmean SE df lower.CL upper.CL
## 1 1841 56.9 31.1 1725 1957
## 2 1598 56.9 31.1 1482 1714
## 3 1773 56.9 31.1 1657 1889
## 4 1983 56.9 31.1 1867 2099
## 5 1508 173.9 33.7 1154 1861
## 6 1237 173.9 33.7 883 1590
## 7 1195 173.9 33.7 841 1548
## 8 1684 173.9 33.7 1330 2037
## 9 753 173.9 33.7 399 1106
## 17 1713 173.9 33.7 1359 2066
## 18 1683 173.9 33.7 1329 2036
## 19 1964 173.9 33.7 1610 2317
## 20 1815 173.9 33.7 1461 2168
## 21 1463 173.9 33.7 1109 1816
## 29 1162 173.9 33.7 809 1516
## 30 1134 173.9 33.7 781 1488
## 31 1525 173.9 33.7 1172 1879
## 32 1205 173.9 33.7 852 1559
## 33 1714 173.9 33.7 1361 2068
## 41 1481 173.9 33.7 1128 1835
## 42 1343 173.9 33.7 990 1697
## 43 1200 173.9 33.7 847 1554
## 44 1508 173.9 33.7 1155 1862
## 45 1259 173.9 33.7 906 1613
## 53 1854 173.9 33.7 1500 2207
## 54 1193 173.9 33.7 839 1546
## 55 1224 173.9 33.7 870 1577
## 56 1490 173.9 33.7 1136 1843
## 57 1484 173.9 33.7 1130 1837
## 65 1248 173.9 33.7 894 1601
## 66 1515 173.9 33.7 1161 1868
## 67 1383 173.9 33.7 1029 1736
## 68 1555 173.9 33.7 1201 1908
## 69 1925 173.9 33.7 1571 2278
## 77 1821 173.9 33.7 1467 2174
## 78 2297 173.9 33.7 1943 2650
## 79 2589 173.9 33.7 2235 2942
## 80 2149 173.9 33.7 1795 2502
## 81 1748 173.9 33.7 1394 2101
## 89 1959 173.9 33.7 1606 2313
## 90 1184 173.9 33.7 831 1538
## 91 1233 173.9 33.7 880 1587
## 92 1177 173.9 33.7 824 1531
## 93 1457 173.9 33.7 1104 1811
## 101 1427 173.9 33.7 1074 1780
## 102 1879 173.9 33.7 1526 2232
## 103 1873 173.9 33.7 1520 2226
## 104 1445 173.9 33.7 1092 1798
## 105 1797 173.9 33.7 1444 2150
## 113 2137 173.9 33.7 1783 2490
## 114 1407 173.9 33.7 1053 1760
## 115 2313 173.9 33.7 1959 2666
## 116 1562 173.9 33.7 1208 1915
## 117 2308 173.9 33.7 1954 2661
##
## Degrees-of-freedom method: kenward-roger
## Confidence level used: 0.95# Extração da matriz de funções lineares.
grid <- attr(emm, "grid")
L <- attr(emm, "linfct")
rownames(L) <- grid[[1]]
# Contrastes par a par.
ctr <- summary(glht(mm0, linfct = all_pairwise(L)),
test = adjusted(type = "fdr"))
# Erros padrões de vários tamanhos conforme estrutura de associação.
v <- c("coefficients", "sigma", "tstat", "pvalues")
ctr$test[v] %>%
as.data.frame() %>%
split(., round(.$sigma, 4)) %>%
map(head, n = 10)## $`70.5959`
## coefficients sigma tstat pvalues
## 1vs2 242.6 70.59593 3.4364589 4.402507e-03
## 1vs3 68.2 70.59593 0.9660614 4.837786e-01
## 1vs4 -142.3 70.59593 -2.0156970 1.144750e-01
## 2vs3 -174.4 70.59593 -2.4703975 4.576589e-02
## 2vs4 -384.9 70.59593 -5.4521559 2.967107e-06
## 3vs4 -210.5 70.59593 -2.9817584 1.402568e-02
##
## $`174.5456`
## coefficients sigma tstat pvalues
## 1vs5 333.19672 174.5456 1.9089379 1.372476e-01
## 1vs6 604.19672 174.5456 3.4615407 4.117306e-03
## 1vs7 646.19672 174.5456 3.7021655 1.960902e-03
## 1vs8 157.19672 174.5456 0.9006054 5.190524e-01
## 1vs9 1088.19672 174.5456 6.2344550 1.081249e-07
## 1vs17 128.04468 174.5456 0.7335887 6.109110e-01
## 1vs18 158.04468 174.5456 0.9054635 5.164162e-01
## 1vs19 -122.95532 174.5456 -0.7044309 6.282357e-01
## 1vs20 26.04468 174.5456 0.1492142 9.457783e-01
## 1vs21 378.04468 174.5456 2.1658791 8.747687e-02
##
## $`223.2439`
## coefficients sigma tstat pvalues
## 5vs6 271 223.2439 1.2139188 0.3712970179
## 5vs7 313 223.2439 1.4020538 0.2995158408
## 5vs8 -176 223.2439 -0.7883753 0.5789594716
## 5vs9 755 223.2439 3.3819509 0.0051240477
## 6vs7 42 223.2439 0.1881350 0.9286445682
## 6vs8 -447 223.2439 -2.0022941 0.1162606742
## 6vs9 484 223.2439 2.1680321 0.0873554306
## 7vs8 -489 223.2439 -2.1904292 0.0837452168
## 7vs9 442 223.2439 1.9798971 0.1204238024
## 8vs9 931 223.2439 4.1703263 0.0004067836
##
## $`237.9654`
## coefficients sigma tstat pvalues
## 5vs17 -205.15204 237.9654 -0.86210878 0.5383603
## 5vs18 -175.15204 237.9654 -0.73604002 0.6100663
## 5vs19 -456.15204 237.9654 -1.91688408 0.1356202
## 5vs20 -307.15204 237.9654 -1.29074257 0.3415321
## 5vs21 44.84796 237.9654 0.18846423 0.9286446
## 5vs29 345.53982 237.9654 1.45205923 0.2794936
## 5vs30 373.53982 237.9654 1.56972341 0.2374389
## 5vs31 -17.46018 237.9654 -0.07337277 0.9676827
## 5vs32 302.53982 237.9654 1.27136068 0.3489247
## 5vs33 -206.46018 237.9654 -0.86760597 0.5362566# As mesmas comparações múltiplas.
results_mm0 <- wzRfun::apmc(L,
model = mm0,
focus = "linh",
test = "fdr")
results_mm0## linh fit lwr upr cld
## 1 1 1840.9000 1729.3728 1952.427 ej
## 2 2 1598.3000 1486.7728 1709.827 kp
## 3 3 1772.7000 1661.1728 1884.227 dghj
## 4 4 1983.2000 1871.6728 2094.727 bcef
## 5 5 1507.7033 1175.5469 1839.860 jkpq
## 6 6 1236.7033 904.5469 1568.860 npr
## 7 7 1194.7033 862.5469 1526.860 pr
## 8 8 1683.7033 1351.5469 2015.860 ejkpq
## 9 9 752.7033 420.5469 1084.860 r
## 10 17 1712.8553 1380.6990 2045.012 ejkop
## 11 18 1682.8553 1350.6990 2015.012 ejkpq
## 12 19 1963.8553 1631.6990 2296.012 bcejk
## 13 20 1814.8553 1482.6990 2147.012 bceijklm
## 14 21 1462.8553 1130.6990 1795.012 jkpq
## 15 29 1162.1635 830.0071 1494.320 oqr
## 16 30 1134.1635 802.0071 1466.320 qr
## 17 31 1525.1635 1193.0071 1857.320 jkpq
## 18 32 1205.1635 873.0071 1537.320 pr
## 19 33 1714.1635 1382.0071 2046.320 ejkp
## 20 41 1481.2601 1149.1038 1813.416 jkpq
## 21 42 1343.2601 1011.1038 1675.416 lnpq
## 22 43 1200.2601 868.1038 1532.416 pr
## 23 44 1508.2601 1176.1038 1840.416 jkpq
## 24 45 1259.2601 927.1038 1591.416 mnpr
## 25 53 1853.9407 1521.7843 2186.097 bceijkl
## 26 54 1192.9407 860.7843 1525.097 pr
## 27 55 1223.9407 891.7843 1556.097 npr
## 28 56 1489.9407 1157.7843 1822.097 jkpq
## 29 57 1483.9407 1151.7843 1816.097 jkpq
## 30 65 1247.5433 915.3869 1579.700 mnpr
## 31 66 1514.5433 1182.3869 1846.700 jkpq
## 32 67 1382.5433 1050.3869 1714.700 hinpq
## 33 68 1554.5433 1222.3869 1886.700 jkpq
## 34 69 1924.5433 1592.3869 2256.700 bcejk
## 35 77 1820.5744 1488.4180 2152.731 bceijklm
## 36 78 2296.5744 1964.4180 2628.731 ab
## 37 79 2588.5744 2256.4180 2920.731 a
## 38 80 2148.5744 1816.4180 2480.731 ade
## 39 81 1747.5744 1415.4180 2079.731 cejkp
## 40 89 1959.3856 1627.2293 2291.542 bceijk
## 41 90 1184.3856 852.2293 1516.542 pr
## 42 91 1233.3856 901.2293 1565.542 npr
## 43 92 1177.3856 845.2293 1509.542 pr
## 44 93 1457.3856 1125.2293 1789.542 jkpq
## 45 101 1427.0193 1094.8629 1759.176 jkpq
## 46 102 1879.0193 1546.8629 2211.176 bceijkl
## 47 103 1873.0193 1540.8629 2205.176 bceijkl
## 48 104 1445.0193 1112.8629 1777.176 jkpq
## 49 105 1797.0193 1464.8629 2129.176 bcejkn
## 50 113 2136.5546 1804.3982 2468.711 ade
## 51 114 1406.5546 1074.3982 1738.711 gkpq
## 52 115 2312.5546 1980.3982 2644.711 ac
## 53 116 1561.5546 1229.3982 1893.711 fjkpq
## 54 117 2307.5546 1975.3982 2639.711 ac# Gráfico de segmentos para as estimativas intervalares.
ggplot(data = results_mm0,
mapping = aes(x = fit, y = reorder(linh, fit))) +
geom_point() +
geom_errorbarh(mapping = aes(xmin = lwr, xmax = upr),
height = 0) +
geom_label(mapping = aes(
x = min(lwr),
label = sprintf("%0.0f %s", fit, cld)),
label.padding = unit(0.15, "lines"),
fill = "black",
colour = "white",
size = 3,
nudge_x = -80,
hjust = 0,
vjust = 0.5) +
labs(x = "Produção",
y = "Linhagens")
Manual de Planejamento e Análise de Experimentos com R
Walmes Marques Zeviani