This functions get the compact letter display for
objects of class "glht"
. Modification was done to get the
letters to design with missing cells, non completelly crossed
factorial designs and nested factorial designs. These models are
usually declared by a model matrix to have all effects
estimated. It is assumed that Tukey contrasts was used.
cld2(object, level = 0.05)
object | an object returned by |
---|---|
level | the nominal significance level. |
an object of class "cld"
with letters to resume mean
comparisons.
# Toy data 1: experiment with cultivars in several locations. td1 <- expand.grid(loc = gl(5, 1), block = gl(3, 1), cult = LETTERS[1:6]) td1 <- subset(td1, !(loc == 1 & cult == "A")) td1 <- subset(td1, !(loc == 2 & cult == "B")) xtabs(~loc + cult, td1)#> cult #> loc A B C D E F #> 1 0 3 3 3 3 3 #> 2 3 0 3 3 3 3 #> 3 3 3 3 3 3 3 #> 4 3 3 3 3 3 3 #> 5 3 3 3 3 3 3#>#>logLik(m0)#> 'log Lik.' -86.46157 (df=30)# The same model but without rank deficience. td1$loccult <- with(td1, interaction(loc, cult, drop = TRUE)) m1 <- lmer(y ~ loccult + (1 | loc:block), data = td1) logLik(m1)#> 'log Lik.' -86.46157 (df=30)library(doBy) X <- LE_matrix(lm(nobars(formula(m1)), data = td1), effect = "loccult") rownames(X) <- levels(td1$loccult) dim(X)#> [1] 28 28Xs <- X[grepl(x = rownames(X), "^1\\."),] Xc <- apc(Xs) library(multcomp) g <- summary(glht(m1, linfct = Xc), test = adjusted(type = "fdr")) cld2(g)#> 1.B 1.C 1.D 1.E 1.F #> "e" "d" "c" "b" "a"#> #> Simultaneous Confidence Intervals #> #> Fit: lmer(formula = y ~ loccult + (1 | loc:block), data = td1) #> #> Quantile = 1.96 #> 95% confidence level #> #> #> Linear Hypotheses: #> Estimate lwr upr #> 1.B == 0 17.0000 11.6346 22.3654 #> 1.C == 0 30.0000 24.6346 35.3654 #> 1.D == 0 45.0000 39.6346 50.3654 #> 1.E == 0 60.0000 54.6346 65.3654 #> 1.F == 0 75.0000 69.6346 80.3654 #>