Capítulo 17 Delineamento alfa látice
RamalhoEg11.10
: alfa látice.
17.1 RamalhoEg11.10
Os dados em RamalhoEg11.10
são da produção de grãos (kg/parcela) de 40 cultivares de sorgo avaliadas em experimento feito no delineamento de alfa-látice com 3 repetições.
da <- as_tibble(labestData::RamalhoEg11.10)
da$rept <- gl(3, 40)
str(da)
## Classes 'tbl_df', 'tbl' and 'data.frame': 120 obs. of 4 variables:
## $ bloc: Factor w/ 8 levels "1","2","3","4",..: 1 1 1 1 1 2 2 2 2 2 ...
## $ cult: Factor w/ 40 levels "1","2","3","4",..: 1 9 17 25 33 2 10 18 26 34 ...
## $ prod: num 8.39 7.66 8.7 5.21 8.69 6.19 4.4 3.69 6.75 6.85 ...
## $ rept: Factor w/ 3 levels "1","2","3": 1 1 1 1 1 1 1 1 1 1 ...
with(da, table(cult, bloc, rept)) %>%
addmargins(1:2) %>%
print.table(zero.print = ".")
## , , rept = 1
##
## bloc
## cult 1 2 3 4 5 6 7 8 Sum
## 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
## 26 . 1 . . . . . . 1
## 27 . . 1 . . . . . 1
## 28 . . . 1 . . . . 1
## 29 . . . . 1 . . . 1
## 30 . . . . . 1 . . 1
## 31 . . . . . . 1 . 1
## 32 . . . . . . . 1 1
## 33 1 . . . . . . . 1
## 34 . 1 . . . . . . 1
## 35 . . 1 . . . . . 1
## 36 . . . 1 . . . . 1
## 37 . . . . 1 . . . 1
## 38 . . . . . 1 . . 1
## 39 . . . . . . 1 . 1
## 40 . . . . . . . 1 1
## Sum 5 5 5 5 5 5 5 5 40
##
## , , rept = 2
##
## bloc
## cult 1 2 3 4 5 6 7 8 Sum
## 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
## 26 . . . . . 1 . . 1
## 27 . . . . . . 1 . 1
## 28 . . . . . . . 1 1
## 29 1 . . . . . . . 1
## 30 . 1 . . . . . . 1
## 31 . . 1 . . . . . 1
## 32 . . . 1 . . . . 1
## 33 . . . 1 . . . . 1
## 34 . . . . 1 . . . 1
## 35 . . . . . 1 . . 1
## 36 . . . . . . 1 . 1
## 37 . . . . . . . 1 1
## 38 1 . . . . . . . 1
## 39 . 1 . . . . . . 1
## 40 . . 1 . . . . . 1
## Sum 5 5 5 5 5 5 5 5 40
##
## , , rept = 3
##
## bloc
## cult 1 2 3 4 5 6 7 8 Sum
## 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
## 26 . . . 1 . . . . 1
## 27 . . . . 1 . . . 1
## 28 . . . . . 1 . . 1
## 29 . . . . . . 1 . 1
## 30 . . . . . . . 1 1
## 31 1 . . . . . . . 1
## 32 . 1 . . . . . . 1
## 33 . . . . . 1 . . 1
## 34 . . . . . . 1 . 1
## 35 . . . . . . . 1 1
## 36 1 . . . . . . . 1
## 37 . 1 . . . . . . 1
## 38 . . 1 . . . . . 1
## 39 . . . 1 . . . . 1
## 40 . . . . 1 . . . 1
## Sum 5 5 5 5 5 5 5 5 40
library(igraph)
da$cond <- with(da, interaction(rept, bloc))
# Todos os possíveis pares.
pares <- apply(combn(sort(as.character(levels(da$cult))), m = 2),
MARGIN = 2,
FUN = paste0,
collapse = "_") %>%
tibble(name = ., value = 0)
head(pares)
# Quantas vezes cada par ocorre junto.
by(data = as.character(da$cult),
INDICES = da$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$cult),
INDICES = da$cond,
FUN = combn,
m = 2) %>%
flatten_int()
ghp <- graph(edg, directed = FALSE)
plot(ghp,
vertex.size = 5,
vertex.label.dist = 1,
layout = layout_in_circle,
edge.curved = FALSE)
# Modelo de efeitos fixos.
m0 <- lm(terms(prod ~ rept/bloc + cult, 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)
## rept 17.760 2 7.9262 0.0009179 ***
## rept:bloc 51.482 21 2.1881 0.0101548 *
## cult 207.015 39 4.7378 6.35e-08 ***
## Residuals 63.861 57
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
# Médias marginais ajustadas.
emm <- emmeans(m0, specs = ~cult)
## NOTE: A nesting structure was detected in the fitted model:
## bloc %in% rept
emm
## cult emmean SE df lower.CL upper.CL
## 1 8.23 0.695 57 6.84 9.62
## 2 6.36 0.695 57 4.97 7.75
## 3 5.10 0.695 57 3.70 6.49
## 4 4.14 0.695 57 2.75 5.53
## 5 5.06 0.695 57 3.67 6.45
## 6 4.33 0.695 57 2.94 5.72
## 7 4.54 0.695 57 3.15 5.93
## 8 9.01 0.695 57 7.62 10.40
## 9 5.00 0.695 57 3.60 6.39
## 10 4.61 0.695 57 3.22 6.00
## 11 4.13 0.695 57 2.74 5.53
## 12 4.34 0.695 57 2.94 5.73
## 13 9.38 0.695 57 7.99 10.77
## 14 4.55 0.695 57 3.16 5.94
## 15 5.67 0.695 57 4.28 7.06
## 16 5.10 0.695 57 3.71 6.49
## 17 7.32 0.695 57 5.93 8.72
## 18 3.97 0.695 57 2.58 5.36
## 19 4.86 0.695 57 3.47 6.25
## 20 7.57 0.695 57 6.18 8.96
## 21 4.43 0.695 57 3.04 5.82
## 22 4.48 0.695 57 3.09 5.87
## 23 3.93 0.695 57 2.53 5.32
## 24 4.82 0.695 57 3.42 6.21
## 25 4.56 0.697 57 3.16 5.95
## 26 6.74 0.697 57 5.34 8.13
## 27 3.62 0.697 57 2.23 5.02
## 28 4.39 0.697 57 3.00 5.79
## 29 3.81 0.697 57 2.42 5.21
## 30 4.92 0.697 57 3.53 6.32
## 31 4.23 0.697 57 2.84 5.63
## 32 6.23 0.697 57 4.84 7.63
## 33 5.32 0.697 57 3.93 6.72
## 34 6.44 0.697 57 5.05 7.84
## 35 5.09 0.697 57 3.70 6.49
## 36 3.46 0.697 57 2.06 4.85
## 37 4.64 0.697 57 3.25 6.04
## 38 3.94 0.697 57 2.55 5.34
## 39 4.53 0.697 57 3.14 5.93
## 40 4.15 0.697 57 2.75 5.54
##
## Results are averaged over the levels of: bloc, rept
## 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
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1
## rept2 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept1:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## cult2 0 1 0 0 0 0 0 0 0 0 0
## cult3 0 0 1 0 0 0 0 0 0 0 0
## cult4 0 0 0 1 0 0 0 0 0 0 0
## cult5 0 0 0 0 1 0 0 0 0 0 0
## cult6 0 0 0 0 0 1 0 0 0 0 0
## cult7 0 0 0 0 0 0 1 0 0 0 0
## cult8 0 0 0 0 0 0 0 1 0 0 0
## cult9 0 0 0 0 0 0 0 0 1 0 0
## cult10 0 0 0 0 0 0 0 0 0 1 0
## cult11 0 0 0 0 0 0 0 0 0 0 1
## cult12 0 0 0 0 0 0 0 0 0 0 0
## cult13 0 0 0 0 0 0 0 0 0 0 0
## cult14 0 0 0 0 0 0 0 0 0 0 0
## cult15 0 0 0 0 0 0 0 0 0 0 0
## cult16 0 0 0 0 0 0 0 0 0 0 0
## cult17 0 0 0 0 0 0 0 0 0 0 0
## cult18 0 0 0 0 0 0 0 0 0 0 0
## cult19 0 0 0 0 0 0 0 0 0 0 0
## cult20 0 0 0 0 0 0 0 0 0 0 0
## cult21 0 0 0 0 0 0 0 0 0 0 0
## cult22 0 0 0 0 0 0 0 0 0 0 0
## cult23 0 0 0 0 0 0 0 0 0 0 0
## cult24 0 0 0 0 0 0 0 0 0 0 0
## cult25 0 0 0 0 0 0 0 0 0 0 0
## cult26 0 0 0 0 0 0 0 0 0 0 0
## cult27 0 0 0 0 0 0 0 0 0 0 0
## cult28 0 0 0 0 0 0 0 0 0 0 0
## cult29 0 0 0 0 0 0 0 0 0 0 0
## cult30 0 0 0 0 0 0 0 0 0 0 0
## cult31 0 0 0 0 0 0 0 0 0 0 0
## cult32 0 0 0 0 0 0 0 0 0 0 0
## cult33 0 0 0 0 0 0 0 0 0 0 0
## cult34 0 0 0 0 0 0 0 0 0 0 0
## cult35 0 0 0 0 0 0 0 0 0 0 0
## cult36 0 0 0 0 0 0 0 0 0 0 0
## cult37 0 0 0 0 0 0 0 0 0 0 0
## cult38 0 0 0 0 0 0 0 0 0 0 0
## cult39 0 0 0 0 0 0 0 0 0 0 0
## cult40 0 0 0 0 0 0 0 0 0 0 0
## 12 13 14 15 16 17 18 19 20 21 22
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1
## rept2 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept1:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## cult2 0 0 0 0 0 0 0 0 0 0 0
## cult3 0 0 0 0 0 0 0 0 0 0 0
## cult4 0 0 0 0 0 0 0 0 0 0 0
## cult5 0 0 0 0 0 0 0 0 0 0 0
## cult6 0 0 0 0 0 0 0 0 0 0 0
## cult7 0 0 0 0 0 0 0 0 0 0 0
## cult8 0 0 0 0 0 0 0 0 0 0 0
## cult9 0 0 0 0 0 0 0 0 0 0 0
## cult10 0 0 0 0 0 0 0 0 0 0 0
## cult11 0 0 0 0 0 0 0 0 0 0 0
## cult12 1 0 0 0 0 0 0 0 0 0 0
## cult13 0 1 0 0 0 0 0 0 0 0 0
## cult14 0 0 1 0 0 0 0 0 0 0 0
## cult15 0 0 0 1 0 0 0 0 0 0 0
## cult16 0 0 0 0 1 0 0 0 0 0 0
## cult17 0 0 0 0 0 1 0 0 0 0 0
## cult18 0 0 0 0 0 0 1 0 0 0 0
## cult19 0 0 0 0 0 0 0 1 0 0 0
## cult20 0 0 0 0 0 0 0 0 1 0 0
## cult21 0 0 0 0 0 0 0 0 0 1 0
## cult22 0 0 0 0 0 0 0 0 0 0 1
## cult23 0 0 0 0 0 0 0 0 0 0 0
## cult24 0 0 0 0 0 0 0 0 0 0 0
## cult25 0 0 0 0 0 0 0 0 0 0 0
## cult26 0 0 0 0 0 0 0 0 0 0 0
## cult27 0 0 0 0 0 0 0 0 0 0 0
## cult28 0 0 0 0 0 0 0 0 0 0 0
## cult29 0 0 0 0 0 0 0 0 0 0 0
## cult30 0 0 0 0 0 0 0 0 0 0 0
## cult31 0 0 0 0 0 0 0 0 0 0 0
## cult32 0 0 0 0 0 0 0 0 0 0 0
## cult33 0 0 0 0 0 0 0 0 0 0 0
## cult34 0 0 0 0 0 0 0 0 0 0 0
## cult35 0 0 0 0 0 0 0 0 0 0 0
## cult36 0 0 0 0 0 0 0 0 0 0 0
## cult37 0 0 0 0 0 0 0 0 0 0 0
## cult38 0 0 0 0 0 0 0 0 0 0 0
## cult39 0 0 0 0 0 0 0 0 0 0 0
## cult40 0 0 0 0 0 0 0 0 0 0 0
## 23 24 25 26 27 28 29 30 31 32 33
## (Intercept) 1 1 1 1 1 1 1 1 1 1 1
## rept2 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept1:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## cult2 0 0 0 0 0 0 0 0 0 0 0
## cult3 0 0 0 0 0 0 0 0 0 0 0
## cult4 0 0 0 0 0 0 0 0 0 0 0
## cult5 0 0 0 0 0 0 0 0 0 0 0
## cult6 0 0 0 0 0 0 0 0 0 0 0
## cult7 0 0 0 0 0 0 0 0 0 0 0
## cult8 0 0 0 0 0 0 0 0 0 0 0
## cult9 0 0 0 0 0 0 0 0 0 0 0
## cult10 0 0 0 0 0 0 0 0 0 0 0
## cult11 0 0 0 0 0 0 0 0 0 0 0
## cult12 0 0 0 0 0 0 0 0 0 0 0
## cult13 0 0 0 0 0 0 0 0 0 0 0
## cult14 0 0 0 0 0 0 0 0 0 0 0
## cult15 0 0 0 0 0 0 0 0 0 0 0
## cult16 0 0 0 0 0 0 0 0 0 0 0
## cult17 0 0 0 0 0 0 0 0 0 0 0
## cult18 0 0 0 0 0 0 0 0 0 0 0
## cult19 0 0 0 0 0 0 0 0 0 0 0
## cult20 0 0 0 0 0 0 0 0 0 0 0
## cult21 0 0 0 0 0 0 0 0 0 0 0
## cult22 0 0 0 0 0 0 0 0 0 0 0
## cult23 1 0 0 0 0 0 0 0 0 0 0
## cult24 0 1 0 0 0 0 0 0 0 0 0
## cult25 0 0 1 0 0 0 0 0 0 0 0
## cult26 0 0 0 1 0 0 0 0 0 0 0
## cult27 0 0 0 0 1 0 0 0 0 0 0
## cult28 0 0 0 0 0 1 0 0 0 0 0
## cult29 0 0 0 0 0 0 1 0 0 0 0
## cult30 0 0 0 0 0 0 0 1 0 0 0
## cult31 0 0 0 0 0 0 0 0 1 0 0
## cult32 0 0 0 0 0 0 0 0 0 1 0
## cult33 0 0 0 0 0 0 0 0 0 0 1
## cult34 0 0 0 0 0 0 0 0 0 0 0
## cult35 0 0 0 0 0 0 0 0 0 0 0
## cult36 0 0 0 0 0 0 0 0 0 0 0
## cult37 0 0 0 0 0 0 0 0 0 0 0
## cult38 0 0 0 0 0 0 0 0 0 0 0
## cult39 0 0 0 0 0 0 0 0 0 0 0
## cult40 0 0 0 0 0 0 0 0 0 0 0
## 34 35 36 37 38 39 40
## (Intercept) 1 1 1 1 1 1 1
## rept2 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept3 1/3 1/3 1/3 1/3 1/3 1/3 1/3
## rept1:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc2 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc3 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc4 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc5 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc6 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc7 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept1:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept2:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## rept3:bloc8 1/24 1/24 1/24 1/24 1/24 1/24 1/24
## cult2 0 0 0 0 0 0 0
## cult3 0 0 0 0 0 0 0
## cult4 0 0 0 0 0 0 0
## cult5 0 0 0 0 0 0 0
## cult6 0 0 0 0 0 0 0
## cult7 0 0 0 0 0 0 0
## cult8 0 0 0 0 0 0 0
## cult9 0 0 0 0 0 0 0
## cult10 0 0 0 0 0 0 0
## cult11 0 0 0 0 0 0 0
## cult12 0 0 0 0 0 0 0
## cult13 0 0 0 0 0 0 0
## cult14 0 0 0 0 0 0 0
## cult15 0 0 0 0 0 0 0
## cult16 0 0 0 0 0 0 0
## cult17 0 0 0 0 0 0 0
## cult18 0 0 0 0 0 0 0
## cult19 0 0 0 0 0 0 0
## cult20 0 0 0 0 0 0 0
## cult21 0 0 0 0 0 0 0
## cult22 0 0 0 0 0 0 0
## cult23 0 0 0 0 0 0 0
## cult24 0 0 0 0 0 0 0
## cult25 0 0 0 0 0 0 0
## cult26 0 0 0 0 0 0 0
## cult27 0 0 0 0 0 0 0
## cult28 0 0 0 0 0 0 0
## cult29 0 0 0 0 0 0 0
## cult30 0 0 0 0 0 0 0
## cult31 0 0 0 0 0 0 0
## cult32 0 0 0 0 0 0 0
## cult33 0 0 0 0 0 0 0
## cult34 1 0 0 0 0 0 0
## cult35 0 1 0 0 0 0 0
## cult36 0 0 1 0 0 0 0
## cult37 0 0 0 1 0 0 0
## cult38 0 0 0 0 1 0 0
## cult39 0 0 0 0 0 1 0
## cult40 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 dois tamanhos conforme estrutura de associados.
v <- c("coefficients", "sigma", "tstat", "pvalues")
ctr$test[v] %>%
as.data.frame() %>%
split(., round(.$sigma, 4)) %>%
map(head, n = 10)
## $`0.949`
## coefficients sigma tstat pvalues
## 1vs25 3.6743756 0.9490113 3.8717934 0.002919571
## 2vs26 -0.3799496 0.9490113 -0.4003637 0.899084187
## 3vs27 1.4715812 0.9490113 1.5506467 0.356269712
## 4vs28 -0.2486615 0.9490113 -0.2620216 0.941400657
## 5vs29 1.2429867 0.9490113 1.3097702 0.490391974
## 6vs30 -0.5918324 0.9490113 -0.6236305 0.839363717
## 7vs31 0.3077231 0.9490113 0.3242566 0.925430361
## 8vs32 2.7737768 0.9490113 2.9228070 0.029128960
## 17vs33 2.0037498 0.9490113 2.1114078 0.143983060
## 18vs34 -2.4730733 0.9490113 -2.6059472 0.057245759
##
## $`0.9495`
## coefficients sigma tstat pvalues
## 9vs29 1.18144428 0.9494504 1.2443455 0.5275456145
## 9vs37 0.35264282 0.9494504 0.3714178 0.9085575185
## 10vs30 -0.31139778 0.9494504 -0.3279769 0.9254303606
## 10vs38 0.66847966 0.9494504 0.7040701 0.8073797597
## 11vs31 -0.09883143 0.9494504 -0.1040933 0.9746075043
## 11vs39 -0.39901145 0.9494504 -0.4202552 0.8990841867
## 12vs32 -1.89630766 0.9494504 -1.9972689 0.1747054944
## 12vs40 0.19103712 0.9494504 0.2012081 0.9523227053
## 13vs25 4.82348132 0.9494504 5.0802880 0.0001953620
## 13vs33 4.05995763 0.9494504 4.2761136 0.0009660024
##
## $`0.9526`
## coefficients sigma tstat pvalues
## 1vs20 0.6640059 0.952648 0.6970108 0.8073797597
## 1vs24 3.4166603 0.952648 3.5864875 0.0059109884
## 1vs29 4.4182028 0.952648 4.6378123 0.0004554873
## 2vs17 -0.9664214 0.952648 -1.0144580 0.6527338201
## 2vs21 1.9305539 0.952648 2.0265133 0.1672947673
## 2vs30 1.4363775 0.952648 1.5077736 0.3715684344
## 3vs18 1.1243747 0.952648 1.1802624 0.5610398597
## 3vs22 0.6129858 0.952648 0.6434546 0.8331765265
## 3vs31 0.8637108 0.952648 0.9066421 0.7060494738
## 4vs19 -0.7205086 0.952648 -0.7563219 0.7897209996
##
## $`0.9527`
## coefficients sigma tstat pvalues
## 1vs9 3.2367585 0.9527453 3.3972969 0.0100248118
## 2vs10 1.7477753 0.9527453 1.8344623 0.2304895288
## 3vs11 0.9625422 0.9527453 1.0102829 0.6547124242
## 4vs12 -0.1948229 0.9527453 -0.2044859 0.9521294885
## 5vs13 -4.3243217 0.9527453 -4.5388017 0.0005517439
## 6vs14 -0.2188914 0.9527453 -0.2297481 0.9510989945
## 7vs15 -1.1290936 0.9527453 -1.1850949 0.5610398597
## 8vs16 3.9067203 0.9527453 4.1004878 0.0015404470
## 9vs17 -2.3296564 0.9527453 -2.4452038 0.0775150385
## 10vs18 0.6386517 0.9527453 0.6703279 0.8210826093
##
## $`0.9531`
## coefficients sigma tstat pvalues
## 9vs25 0.43761712 0.9530855 0.45915832 8.849520e-01
## 9vs33 -0.32590657 0.9530855 -0.34194895 9.214889e-01
## 10vs26 -2.12772494 0.9530855 -2.23245977 1.163281e-01
## 10vs34 -1.83442157 0.9530855 -1.92471888 1.992386e-01
## 11vs27 0.50903894 0.9530855 0.53409580 8.679902e-01
## 11vs35 -0.95845589 0.9530855 -1.00563479 6.561907e-01
## 12vs28 -0.05383853 0.9530855 -0.05648867 9.863078e-01
## 12vs36 0.87896922 0.9530855 0.92223548 6.998938e-01
## 13vs29 5.56730848 0.9530855 5.84135289 6.976168e-05
## 13vs37 4.73850701 0.9530855 4.97175462 2.086153e-04
##
## $`0.9532`
## coefficients sigma tstat pvalues
## 1vs38 4.2897785 0.9531826 4.5004791 0.0005882659
## 2vs39 1.8255831 0.9531826 1.9152501 0.2024742531
## 3vs40 0.9500527 0.9531826 0.9967163 0.6597631711
## 4vs33 -1.1785625 0.9531826 -1.2364498 0.5296394172
## 5vs34 -1.3883388 0.9531826 -1.4565297 0.4009962930
## 6vs35 -0.7620712 0.9531826 -0.7995018 0.7725117271
## 7vs36 1.0819971 0.9531826 1.1351415 0.5827814306
## 8vs37 4.3648943 0.9531826 4.5792844 0.0005031086
## 17vs32 1.0911817 0.9531826 1.1447771 0.5778873157
## 18vs25 -0.5855749 0.9531826 -0.6143365 0.8412772957
##
## $`0.9542`
## coefficients sigma tstat pvalues
## 9vs28 0.6039944 0.9542105 0.6329782 8.357042e-01
## 9vs34 -1.4498812 0.9542105 -1.5194564 3.672172e-01
## 10vs29 0.7969039 0.9542105 0.8351448 7.525024e-01
## 10vs35 -0.4816366 0.9542105 -0.5047488 8.706698e-01
## 11vs30 -0.7882170 0.9542105 -0.8260411 7.565542e-01
## 11vs36 0.6754426 0.9542105 0.7078549 8.073798e-01
## 12vs31 0.1046952 0.9542105 0.1097192 9.746075e-01
## 12vs37 -0.3051901 0.9542105 -0.3198352 9.254304e-01
## 13vs32 3.1473895 0.9542105 3.2984227 1.235312e-02
## 13vs38 5.4388842 0.9542105 5.6998789 6.976168e-05
##
## $`0.9544`
## coefficients sigma tstat pvalues
## 1vs36 4.7735607 0.9543642 5.0018230 0.000195362
## 2vs37 1.7158778 0.9543642 1.7979277 0.239900923
## 3vs38 1.1542026 0.9543642 1.2093943 0.550527236
## 4vs39 -0.3903077 0.9543642 -0.4089715 0.899084187
## 5vs40 0.9104125 0.9543642 0.9539467 0.683955275
## 6vs33 -0.9908815 0.9543642 -1.0382635 0.639421840
## 7vs34 -1.9046863 0.9543642 -1.9957646 0.174705494
## 8vs35 3.9151552 0.9543642 4.1023703 0.001540447
## 17vs26 0.5864718 0.9543642 0.6145157 0.841277296
## 18vs27 0.3472065 0.9543642 0.3638093 0.912773616
##
## $`0.9561`
## coefficients sigma tstat pvalues
## 25vs33 -0.7635237 0.9561095 -0.7985735 0.7725117
## 26vs34 0.2933034 0.9561095 0.3067675 0.9280964
## 27vs35 -1.4674948 0.9561095 -1.5348606 0.3609207
## 28vs36 0.9328077 0.9561095 0.9756286 0.6736514
## 29vs37 -0.8288015 0.9561095 -0.8668478 0.7353326
## 30vs38 0.9798774 0.9561095 1.0248590 0.6475922
## 31vs39 -0.3001800 0.9561095 -0.3139599 0.9255727
## 32vs40 2.0873448 0.9561095 2.1831650 0.1249369
##
## $`0.9575`
## coefficients sigma tstat pvalues
## 1vs10 3.6212988 0.9575454 3.7818560 0.0036583256
## 2vs11 2.2245946 0.9575454 2.3232262 0.0996304845
## 3vs12 0.7590156 0.9575454 0.7926680 0.7768624358
## 4vs13 -5.2385201 0.9575454 -5.4707798 0.0001013326
## 5vs14 0.5076260 0.9575454 0.5301326 0.8680485073
## 6vs15 -1.3392635 0.9575454 -1.3986422 0.4350785939
## 7vs16 -0.5603363 0.9575454 -0.5851798 0.8592816773
## 8vs9 4.0122515 0.9575454 4.1901422 0.0012133771
## 9vs22 0.5118031 0.9575454 0.5344949 0.8679902073
## 10vs23 0.6843045 0.9575454 0.7146445 0.8073797597
##
## $`0.958`
## coefficients sigma tstat pvalues
## 1vs17 0.9071021 0.9579806 0.9468899 0.6842317811
## 2vs18 2.3864270 0.9579806 2.4911016 0.0719003853
## 3vs19 0.2333300 0.9579806 0.2435644 0.9486341947
## 4vs20 -3.4254085 0.9579806 -3.5756553 0.0060410128
## 5vs21 0.6288614 0.9579806 0.6564448 0.8252263005
## 6vs22 -0.1531718 0.9579806 -0.1598903 0.9565239536
## 7vs23 0.6140398 0.9579806 0.6409731 0.8331765265
## 8vs24 4.1921533 0.9579806 4.3760315 0.0007670826
##
## $`0.9586`
## coefficients sigma tstat pvalues
## 1vs31 3.999286679 0.9585686 4.172144484 0.001268911
## 1vs33 2.910851935 0.9585686 3.036665240 0.022667341
## 2vs32 0.124760251 0.9585686 0.130152659 0.969439559
## 2vs34 -0.086646274 0.9585686 -0.090391313 0.974607504
## 3vs25 0.538799741 0.9585686 0.562087829 0.860824462
## 3vs35 0.004086337 0.9585686 0.004262957 0.996995344
## 4vs26 -2.595840498 0.9585686 -2.708038329 0.045767811
## 4vs36 0.684146274 0.9585686 0.713716553 0.807379760
## 5vs27 1.431941000 0.9585686 1.493832581 0.378528094
## 5vs37 0.414185268 0.9585686 0.432087249 0.897414496
##
## $`0.9593`
## coefficients sigma tstat pvalues
## 1vs11 4.0981181 0.959285 4.2720549 0.0009660024
## 2vs12 2.0210679 0.959285 2.1068483 0.1448075524
## 3vs13 -4.2846816 0.959285 -4.4665367 0.0006274912
## 4vs14 -0.4065724 0.959285 -0.4238285 0.8990841867
## 5vs15 -0.6127461 0.959285 -0.6387529 0.8331765265
## 6vs16 -0.7705062 0.959285 -0.8032088 0.7725117271
## 7vs9 -0.4548050 0.959285 -0.4741084 0.8828461254
## 8vs10 4.3967918 0.959285 4.5834053 0.0005031086
## 9vs19 0.1321473 0.959285 0.1377561 0.9678503770
## 10vs20 -2.9572929 0.959285 -3.0828096 0.0202987147
##
## $`0.9875`
## coefficients sigma tstat pvalues
## 25vs29 0.7438272 0.9874907 0.7532498 0.7911238
## 26vs30 1.8163272 0.9874907 1.8393360 0.2290877
## 27vs31 -0.6078704 0.9874907 -0.6155707 0.8412773
## 28vs32 -1.8424691 0.9874907 -1.8658092 0.2193685
## 33vs37 0.6785494 0.9874907 0.6871451 0.8107701
## 34vs38 2.5029012 0.9874907 2.5346075 0.0659147
## 35vs39 0.5594444 0.9874907 0.5665314 0.8608245
## 36vs40 -0.6879321 0.9874907 -0.6966467 0.8073798
##
## $`0.9923`
## coefficients sigma tstat pvalues
## 1vs34 1.7868773 0.9922861 1.8007682 0.239900923
## 2vs35 1.2661387 0.9922861 1.2759815 0.508072344
## 3vs36 1.6379848 0.9922861 1.6507183 0.302419939
## 4vs37 -0.5000131 0.9922861 -0.5039001 0.870669764
## 5vs38 1.1145625 0.9922861 1.1232269 0.585725371
## 6vs39 -0.2026268 0.9922861 -0.2042020 0.952129489
## 7vs40 0.3940650 0.9922861 0.3971285 0.899084187
## 8vs33 3.6863449 0.9922861 3.7150021 0.004418366
## 17vs28 2.9336508 0.9922861 2.9564566 0.027125092
## 18vs29 0.1582522 0.9922861 0.1594825 0.956523954
##
## $`0.9945`
## coefficients sigma tstat pvalues
## 1vs5 3.17521605 0.9944695 3.19287427 0.0162671650
## 2vs6 2.02820988 0.9944695 2.03948929 0.1640009726
## 3vs7 0.55598765 0.9944695 0.55907965 0.8608244622
## 4vs8 -4.86490741 0.9944695 -4.89196244 0.0002476081
## 9vs13 -4.38586420 0.9944695 -4.41025515 0.0006951601
## 10vs14 0.06154321 0.9944695 0.06188547 0.9856458182
## 11vs15 -1.53564815 0.9944695 -1.54418829 0.3580647086
## 12vs16 -0.76336420 0.9944695 -0.76760947 0.7856266938
## 17vs21 2.89697531 0.9944695 2.91308615 0.0297047039
## 18vs22 -0.51138889 0.9944695 -0.51423286 0.8685196622
##
## $`0.9946`
## coefficients sigma tstat pvalues
## 1vs18 4.2599505 0.9946014 4.2830730 0.0009626221
## 1vs22 3.7485616 0.9946014 3.7689084 0.0037662172
## 2vs19 1.4953823 0.9946014 1.5034990 0.3730807800
## 2vs23 2.4320798 0.9946014 2.4452808 0.0775150385
## 3vs20 -2.4715699 0.9946014 -2.4849853 0.0721724560
## 3vs24 0.2810844 0.9946014 0.2826101 0.9380361059
## 4vs17 -3.1823123 0.9946014 -3.1995855 0.0160963729
## 4vs21 -0.2853370 0.9946014 -0.2868857 0.9380361059
## 5vs18 1.0847345 0.9946014 1.0906223 0.6050469228
## 5vs22 0.5733456 0.9946014 0.5764576 0.8604683451
##
## $`0.9953`
## coefficients sigma tstat pvalues
## 1vs13 -1.14910570 0.9953462 -1.1544785 0.5756113059
## 2vs14 1.80931851 0.9953462 1.8177782 0.2344991881
## 3vs15 -0.57310592 0.9953462 -0.5757855 0.8604683451
## 4vs16 -0.95818714 0.9953462 -0.9626672 0.6793770715
## 5vs9 0.06154245 0.9953462 0.0618302 0.9856458182
## 6vs10 -0.28043458 0.9953462 -0.2817458 0.9380361059
## 7vs11 0.40655458 0.9953462 0.4084555 0.8990841867
## 8vs12 4.67008446 0.9953462 4.6919199 0.0003873533
## 9vs21 0.56731895 0.9953462 0.5699715 0.8608244622
## 10vs22 0.12726280 0.9953462 0.1278578 0.9695642870
##
## $`0.9954`
## coefficients sigma tstat pvalues
## 1vs3 3.13557588 0.9954392 3.14994210 0.0179507323
## 1vs7 3.69156353 0.9954392 3.70847712 0.0044565718
## 2vs4 2.21589086 0.9954392 2.22604337 0.1168359535
## 2vs8 -2.64901655 0.9954392 -2.66115351 0.0511481572
## 3vs5 0.03964017 0.9954392 0.03982179 0.9910415425
## 4vs6 -0.18768098 0.9954392 -0.18854088 0.9528513167
## 5vs7 0.51634748 0.9954392 0.51871323 0.8685196622
## 6vs8 -4.67722642 0.9954392 -4.69865600 0.0003873533
## 17vs19 2.46180370 0.9954392 2.47308290 0.0735188222
## 17vs23 3.39850123 0.9954392 3.41407209 0.0096259935
##
## $`0.9964`
## coefficients sigma tstat pvalues
## 1vs12 3.89459146 0.9964235 3.90857055 0.0026692392
## 2vs13 -3.02262924 0.9964235 -3.03347854 0.0226910393
## 3vs14 0.54726618 0.9964235 0.54923051 0.8636167595
## 4vs15 -1.52694444 0.9964235 -1.53242520 0.3609207363
## 5vs16 -0.04398878 0.9964235 -0.04414668 0.9890336263
## 6vs9 -0.66497492 0.9964235 -0.66736176 0.8210826093
## 7vs10 -0.07026469 0.9964235 -0.07051689 0.9843700069
## 8vs11 4.87361111 0.9964235 4.89110425 0.0002476081
## 9vs24 0.17990176 0.9964235 0.18054749 0.9540956599
## 10vs17 -2.71419670 0.9964235 -2.72393893 0.0444516977
##
## $`0.9967`
## coefficients sigma tstat pvalues
## 9vs32 -1.23847470 0.9967488 -1.24251440 0.527545614
## 9vs38 1.05302001 0.9967488 1.05645478 0.627437998
## 10vs25 0.05307677 0.9967488 0.05324990 0.986817451
## 10vs39 0.07780782 0.9967488 0.07806161 0.982122009
## 11vs26 -2.60454420 0.9967488 -2.61303979 0.056553715
## 11vs40 -0.01248953 0.9967488 -0.01253027 0.996995344
## 12vs27 0.71256559 0.9967488 0.71488986 0.807379760
## 12vs33 -0.98373953 0.9967488 -0.98694832 0.667661762
## 13vs28 4.98985863 0.9967488 5.00613472 0.000195362
## 13vs34 2.93598297 0.9967488 2.94555966 0.027752732
##
## $`0.9971`
## coefficients sigma tstat pvalues
## 9vs10 0.3845403 0.9971127 0.3856539 0.9040117863
## 9vs12 0.6578330 0.9971127 0.6597378 0.8235498509
## 9vs14 0.4460836 0.9971127 0.4473753 0.8878480980
## 9vs16 -0.1055312 0.9971127 -0.1058368 0.9746075043
## 10vs11 0.4768193 0.9971127 0.4782000 0.8803964097
## 10vs13 -4.7704045 0.9971127 -4.7842182 0.0003057101
## 10vs15 -1.0588289 0.9971127 -1.0618949 0.6239188749
## 11vs12 -0.2035266 0.9971127 -0.2041160 0.9521294885
## 11vs14 -0.4152761 0.9971127 -0.4164786 0.8990841867
## 11vs16 -0.9668908 0.9971127 -0.9696907 0.6778089965
##
## $`0.9982`
## coefficients sigma tstat pvalues
## 1vs28 3.84075293 0.9982422 3.84751609 0.0031168700
## 2vs29 2.54467923 0.9982422 2.54916014 0.0638862194
## 3vs30 0.17432519 0.9982422 0.17463216 0.9540956599
## 4vs31 -0.09012773 0.9982422 -0.09028643 0.9746075043
## 5vs32 -1.17693225 0.9982422 -1.17900470 0.5610398597
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##
## $`0.9986`
## coefficients sigma tstat pvalues
## 25vs37 -0.0849743 0.9985669 -0.08509625 0.97760321
## 26vs38 2.7962046 0.9985669 2.80021759 0.03796339
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## 32vs36 2.7752769 0.9985669 2.77925984 0.03962863
##
## $`0.9999`
## coefficients sigma tstat pvalues
## 1vs14 3.682842055 0.9999418 3.683056268 0.004716946
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## 2vs9 1.363234954 0.9998878 1.363387921 0.458530132
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##
## $`1.0002`
## coefficients sigma tstat pvalues
## 25vs26 -2.1808017 1.000204 -2.1803564 0.12515360
## 25vs32 -1.6760918 1.000204 -1.6757495 0.28998762
## 26vs27 3.1135831 1.000204 3.1129473 0.01897595
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## 33vs38 1.3789266 1.000204 1.3786450 0.44931912
##
## $`1.0004`
## coefficients sigma tstat pvalues
## 1vs21 3.8040774 1.000359 3.8027139 0.003509033
## 1vs40 4.0856286 1.000359 4.0841641 0.001579339
## 2vs22 1.8750381 1.000359 1.8743660 0.217241045
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## 5vs36 1.5983446 1.000359 1.5977717 0.328912946
##
## $`1.0009`
## coefficients sigma tstat pvalues
## 1vs2 1.873524 1.000868 1.8718993 0.2174641549
## 1vs4 4.089414 1.000868 4.0858691 0.0015793392
## 1vs6 3.901733 1.000868 3.8983508 0.0027143799
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## 1vs37 3.589401 1.000922 3.5860961 0.0059109884
## 2vs3 1.262052 1.000868 1.2609582 0.5162598228
## 2vs5 1.301693 1.000868 1.3005640 0.4950172354
## 2vs7 1.818040 1.000868 1.8164638 0.2344991881
## 2vs28 1.967229 1.000922 1.9654179 0.1831651525
##
## $`1.0014`
## coefficients sigma tstat pvalues
## 1vs23 4.3056034 1.001431 4.2994530 0.0009429991
## 2vs24 1.5431367 1.001431 1.5409324 0.3589865805
## 3vs17 -2.2284737 1.001431 -2.2252904 0.1168359535
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## 7vs21 0.1125139 1.001431 0.1123532 0.9746075043
## 8vs22 4.5240546 1.001431 4.5175922 0.0005801377
##
## $`1.0016`
## coefficients sigma tstat pvalues
## 1vs15 2.562469963 1.001608 2.558356722 0.0631592923
## 2vs16 1.257703717 1.001608 1.255684869 0.5192408373
## 3vs9 0.101182622 1.001608 0.101020205 0.9746075043
## 4vs10 -0.468115562 1.001608 -0.467364149 0.8849519909
## 5vs11 0.922902061 1.001608 0.921420632 0.6998937583
## 6vs12 -0.007141962 1.001608 -0.007130497 0.9969953445
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## 8vs14 4.458335056 1.001608 4.451178599 0.0006341809
## 9vs23 1.068844866 1.001608 1.067129171 0.6206075688
## 10vs24 -0.204638586 1.001608 -0.204310103 0.9521294885
##
## $`1.0017`
## coefficients sigma tstat pvalues
## 1vs32 1.9982838 1.0017 1.9948920 0.17470549
## 2vs25 1.8008521 1.0017 1.7977954 0.23990092
## 3vs26 -1.6420020 1.0017 -1.6392149 0.30817786
## 4vs27 0.5177426 1.0017 0.5168639 0.86851966
## 5vs28 0.6655369 1.0017 0.6644072 0.82108261
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## 8vs31 4.7747797 1.0017 4.7666752 0.00031547
## 17vs38 3.3826764 1.0017 3.3769348 0.01044713
## 18vs39 -0.5608439 1.0017 -0.5598919 0.86082446
##
## $`1.0024`
## coefficients sigma tstat pvalues
## 9vs26 -1.74318459 1.002409 -1.73899554 0.2690701202
## 9vs36 1.53680218 1.002409 1.53310908 0.3609207363
## 10vs27 0.98585820 1.002409 0.98348908 0.6693437354
## 10vs37 -0.03189753 1.002409 -0.03182088 0.9929534645
## 11vs28 -0.25736518 1.002409 -0.25674670 0.9434440369
## 11vs38 0.19166040 1.002409 0.19119982 0.9528513167
## 12vs29 0.52361132 1.002409 0.52235303 0.8685196622
## 12vs39 -0.19548480 1.002409 -0.19501503 0.9523227053
## 13vs30 4.45900677 1.002409 4.44829131 0.0006341809
## 13vs40 5.23473428 1.002409 5.22215467 0.0001689052
##
## $`1.0028`
## coefficients sigma tstat pvalues
## 1vs30 3.3099011 1.002825 3.3005782 0.0123531198
## 2vs31 2.1257631 1.002825 2.1197756 0.1425969850
## 3vs32 -1.1372921 1.002825 -1.1340887 0.5827814306
## 4vs25 -0.4150388 1.002825 -0.4138698 0.8990841867
## 5vs26 -1.6816421 1.002825 -1.6769055 0.2899876236
## 6vs27 0.7054236 1.002825 0.7034367 0.8073797597
## 7vs28 0.1491894 1.002825 0.1487692 0.9611221569
## 8vs29 5.1936958 1.002825 5.1790669 0.0001689052
## 17vs40 3.1785264 1.002825 3.1695736 0.0171038187
## 18vs33 -1.3490986 1.002825 -1.3452987 0.4686541478
##
## $`1.0037`
## coefficients sigma tstat pvalues
## 1vs35 3.1396622 1.003656 3.1282269 0.01846545
## 2vs36 2.9000371 1.003656 2.8894746 0.03109889
## 3vs37 0.4538254 1.003656 0.4521725 0.88716284
## 4vs38 0.2003641 1.003656 0.1996343 0.95232271
## 5vs39 0.5238906 1.003656 0.5219825 0.86851966
## 6vs40 0.1838952 1.003656 0.1832254 0.95409566
## 7vs33 -0.7807116 1.003656 -0.7778681 0.78562669
## 8vs34 2.5623703 1.003656 2.5530376 0.06363874
## 17vs31 3.0921845 1.003656 3.0809221 0.02029871
## 18vs32 -2.2616667 1.003656 -2.2534293 0.11194816
##
## $`1.0041`
## coefficients sigma tstat pvalues
## 9vs31 0.76252818 1.004071 0.75943672 7.896996e-01
## 9vs35 -0.09709629 1.004071 -0.09670264 9.746075e-01
## 10vs32 -1.62301505 1.004071 -1.61643498 3.186347e-01
## 10vs36 1.15226184 1.004071 1.14759031 5.769557e-01
## 11vs25 -0.42374249 1.004071 -0.42202454 8.990842e-01
## 11vs37 -0.50871679 1.004071 -0.50665434 8.706698e-01
## 12vs26 -2.40101755 1.004071 -2.39128329 8.619918e-02
## 12vs38 0.39518705 1.004071 0.39358487 9.009596e-01
## 13vs27 5.75626275 1.004071 5.73292557 6.976168e-05
## 13vs39 4.84821236 1.004071 4.82855662 2.695691e-04
##
## $`1.0045`
## coefficients sigma tstat pvalues
## 25vs27 0.9327814 1.004486 0.9286159 0.69615792
## 25vs31 0.3249111 1.004486 0.3234601 0.92543036
## 26vs28 2.3471790 1.004486 2.3366972 0.09800305
## 26vs32 0.5047099 1.004486 0.5024560 0.87066976
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## 33vs35 0.2288103 1.004486 0.2277885 0.95109899
## 33vs39 0.7882547 1.004486 0.7847346 0.78334035
##
## $`1.0049`
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## 1vs19 3.3689058 1.004878 3.3525535 0.011000275
## 2vs20 -1.2095176 1.004878 -1.2036467 0.554063629
## 3vs21 0.6685016 1.004878 0.6652567 0.821082609
## 4vs22 -0.3408528 1.004878 -0.3391983 0.922590643
## 5vs23 1.1303873 1.004878 1.1249005 0.585725371
## 6vs24 -0.4850732 1.004878 -0.4827187 0.880033423
## 7vs17 -2.7844614 1.004878 -2.7709459 0.039707605
## 8vs18 5.0354435 1.004878 5.0110020 0.000195362
##
## $`1.0059`
## coefficients sigma tstat pvalues
## 9vs30 0.07314257 1.005853 0.07271698 9.839130e-01
## 9vs40 0.84887008 1.005853 0.84393089 7.452354e-01
## 10vs31 0.37798783 1.005853 0.37578849 9.073989e-01
## 10vs33 -0.71044691 1.005853 -0.70631314 8.073798e-01
## 11vs32 -2.09983431 1.005853 -2.08761632 1.491869e-01
## 11vs34 -2.31124084 1.005853 -2.29779277 1.042658e-01
## 12vs25 -0.22021584 1.005853 -0.21893451 9.521295e-01
## 12vs35 -0.75492925 1.005853 -0.75053665 7.911238e-01
## 13vs26 2.64267960 1.005853 2.62730304 5.484041e-02
## 13vs36 5.92266638 1.005853 5.88820505 6.976168e-05
##
## $`1.0063`
## coefficients sigma tstat pvalues
## 1vs26 1.49357391 1.006267 1.48427213 0.3839583753
## 2vs27 2.73363350 1.006267 2.71660879 0.0450302316
## 3vs28 0.70517706 1.006267 0.70078531 0.8073797597
## 4vs29 0.32878837 1.006267 0.32674072 0.9254303606
## 5vs30 0.13468502 1.006267 0.13384622 0.9684952770
## 6vs31 0.09755325 1.006267 0.09694571 0.9746075043
## 7vs32 -1.69327973 1.006267 -1.68273421 0.2899876236
## 8vs25 4.44986862 1.006267 4.42215543 0.0006803552
## 9vs11 0.86135961 1.006267 0.85599518 0.7397742170
## 9vs15 -0.67428854 1.006267 -0.67008916 0.8210826093
##
## $`1.0071`
## coefficients sigma tstat pvalues
## 1vs39 3.69910666 1.007095 3.67304649 0.004756736
## 2vs40 2.21210503 1.007095 2.19652078 0.122097167
## 3vs33 -0.22472394 1.007095 -0.22314077 0.952129489
## 4vs34 -2.30253713 1.007095 -2.28631578 0.106061713
## 5vs35 -0.03555383 1.007095 -0.03530336 0.992316398
## 6vs36 0.87182726 1.007095 0.86568524 0.735332630
## 7vs37 -0.10216222 1.007095 -0.10144248 0.974607504
## 8vs38 5.06527151 1.007095 5.02958672 0.000195362
## 17vs27 3.70005491 1.007095 3.67398806 0.004756736
## 18vs28 -0.41919760 1.007095 -0.41624436 0.899084187
##
## $`1.0075`
## coefficients sigma tstat pvalues
## 9vs27 1.3703985 1.007509 1.3601852 0.4596013217
## 9vs39 0.4623482 1.007509 0.4589024 0.8849519909
## 10vs28 0.2194541 1.007509 0.2178185 0.9521294885
## 10vs40 0.4643297 1.007509 0.4608692 0.8849519909
## 11vs29 0.3200847 1.007509 0.3176991 0.9254303606
## 11vs33 -1.1872662 1.007509 -1.1784177 0.5610398597
## 12vs30 -0.5846904 1.007509 -0.5803328 0.8604683451
## 12vs34 -2.1077142 1.007509 -2.0920058 0.1490872694
## 13vs31 5.1483924 1.007509 5.1100225 0.0001953620
## 13vs35 4.2887679 1.007509 4.2568046 0.0009844507
##
## $`1.0088`
## coefficients sigma tstat pvalues
## 25vs35 -0.5347134 1.008772 -0.5300637 0.86804851
## 26vs36 3.2799868 1.008772 3.2514648 0.01407145
## 27vs37 -1.0177557 1.008772 -1.0089056 0.65471242
## 28vs38 0.4490256 1.008772 0.4451210 0.88784810
## 29vs39 -0.7190961 1.008772 -0.7128430 0.80737976
## 30vs40 0.7757275 1.008772 0.7689820 0.78562669
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## 32vs34 -0.2114065 1.008772 -0.2095682 0.95212949
##
## $`1.0122`
## coefficients sigma tstat pvalues
## 25vs39 0.02473104 1.012194 0.0244331 0.99699534
## 26vs40 2.59205467 1.012194 2.5608277 0.06314626
## 27vs33 -1.69630511 1.012194 -1.6758694 0.28998762
## 28vs34 -2.05387566 1.012194 -2.0291322 0.16707679
## 29vs35 -1.27854056 1.012194 -1.2631377 0.51597539
## 30vs36 1.46365961 1.012194 1.4460266 0.40625090
## 31vs37 -0.40988536 1.012194 -0.4049474 0.89908419
## 32vs38 2.29149471 1.012194 2.2638886 0.11018166
# Comparações múltiplas a 10%.
results_m0 <- wzRfun::apmc(X = L,
model = m0,
focus = "cult",
test = "fdr")
results_m0
## cult fit lwr upr cld
## 1 1 8.231858 6.840485 9.623232 ac
## 2 2 6.358335 4.966961 7.749709 bcdfgh
## 3 3 5.096283 3.704909 6.487656 dhj
## 4 4 4.142444 2.751070 5.533818 hj
## 5 5 5.056642 3.665269 6.448016 dhj
## 6 6 4.330125 2.938751 5.721499 ghj
## 7 7 4.540295 3.148921 5.931669 ghj
## 8 8 9.007351 7.615978 10.398725 ab
## 9 9 4.995100 3.602525 6.387674 dhj
## 10 10 4.610560 3.217985 6.003134 ghj
## 11 11 4.133740 2.741166 5.526315 ghj
## 12 12 4.337267 2.944693 5.729841 ghj
## 13 13 9.380964 7.988390 10.773538 a
## 14 14 4.549016 3.156442 5.941591 ghj
## 15 15 5.669388 4.276814 7.061963 cghj
## 16 16 5.100631 3.708057 6.493206 dhj
## 17 17 7.324756 5.933383 8.716130 acde
## 18 18 3.971908 2.580534 5.363282 hj
## 19 19 4.862953 3.471579 6.254326 ehj
## 20 20 7.567852 6.176479 8.959226 acd
## 21 21 4.427781 3.036407 5.819155 ghj
## 22 22 4.483297 3.091923 5.874670 ghj
## 23 23 3.926255 2.534881 5.317629 hj
## 24 24 4.815198 3.423824 6.206572 ehj
## 25 25 4.557483 3.162089 5.952877 ghj
## 26 26 6.738284 5.342890 8.133679 adeg
## 27 27 3.624701 2.229307 5.020096 ij
## 28 28 4.391105 2.995711 5.786500 ghj
## 29 29 3.813656 2.418261 5.209050 fij
## 30 30 4.921957 3.526563 6.317352 dhj
## 31 31 4.232572 2.837178 5.627966 ghj
## 32 32 6.233575 4.838180 7.628969 cghi
## 33 33 5.321006 3.925612 6.716401 dhj
## 34 34 6.444981 5.049587 7.840375 bcdgh
## 35 35 5.092196 3.696802 6.487590 dhj
## 36 36 3.458298 2.062904 4.853692 j
## 37 37 4.642457 3.247063 6.037851 ghj
## 38 38 3.942080 2.546686 5.337474 hj
## 39 39 4.532752 3.137358 5.928146 ghj
## 40 40 4.146230 2.750836 5.541624 ghj
# Gráfico de segmentos para as estimativas intervalares.
ggplot(data = results_m0,
mapping = aes(x = fit, y = reorder(cult, 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 = "Cultivares")
Manual de Planejamento e Análise de Experimentos com R
Walmes Marques Zeviani