Order Letters According to Numeric Vector

This function order the letters in the compact letter display to the highest estimate receive the letter a. This is a convetion in most software for analysis of experiments.

ordered_cld(let, means = let)

Arguments

let	Character vector with the letters returned by cld() or cld2().
means	Numeric vector with the corresponding estimates in which the highest value will have the letter a.

Value

A character vector with the letters rearranged.

See also

cld2().

Examples

# Toy data.
set.seed(4321)
td <- data.frame(trt = rep(sample(1:8), each = 5))
td$y <- rnorm(nrow(td), mean = sort(td$trt), sd = 2)

plot(y ~ trt, data = td)

# Fit the model.
td$trt <- factor(td$trt)
m0 <- lm(y ~ trt, data = td)
anova(m0)
#> Analysis of Variance Table
#> 
#> Response: y
#>           Df  Sum Sq Mean Sq F value    Pr(>F)    
#> trt        7 241.670  34.524  11.679 2.846e-07 ***
#> Residuals 32  94.597   2.956                      
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
summary(m0)
#> 
#> Call:
#> lm(formula = y ~ trt, data = td)
#> 
#> Residuals:
#>     Min      1Q  Median      3Q     Max 
#> -2.7817 -1.3129  0.0741  1.0963  4.0820 
#> 
#> Coefficients:
#>             Estimate Std. Error t value Pr(>|t|)    
#> (Intercept)   4.6693     0.7689   6.073  8.8e-07 ***
#> trt2         -0.5423     1.0874  -0.499 0.621408    
#> trt3         -2.6046     1.0874  -2.395 0.022632 *  
#> trt4          1.3644     1.0874   1.255 0.218652    
#> trt5         -2.6211     1.0874  -2.410 0.021857 *  
#> trt6         -2.8945     1.0874  -2.662 0.012054 *  
#> trt7          4.1115     1.0874   3.781 0.000645 ***
#> trt8          2.6965     1.0874   2.480 0.018603 *  
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
#> 
#> Residual standard error: 1.719 on 32 degrees of freedom
#> Multiple R-squared:  0.7187,	Adjusted R-squared:  0.6571 
#> F-statistic: 11.68 on 7 and 32 DF,  p-value: 2.846e-07
#> 

library(multcomp)
library(doBy)

X <- LE_matrix(m0, effect = "trt")
rownames(X) <- levels(td$trt)
Xc <- apc(X)

g <- summary(glht(m0, linfct = Xc),
             test = adjusted(type = "fdr"))

res <- data.frame(trt = levels(td$trt),
                  mean = X %*% coef(m0))

let <- cld2(g)
res$cld2 <- let$mcletters$Letters
res[order(res$mean, decreasing = TRUE), ]
#>   trt     mean cld2
#> 7   7 8.780843    a
#> 8   8 7.365762   ab
#> 4   4 6.033745   bd
#> 1   1 4.669300    d
#> 2   2 4.127017   cd
#> 3   3 2.064654    c
#> 5   5 2.048229    c
#> 6   6 1.774765    c

res$let2 <- ordered_cld(res$cld2, res$mean)
res[order(res$mean, decreasing = TRUE), ]
#>   trt     mean cld2 let2
#> 7   7 8.780843    a    a
#> 8   8 7.365762   ab   ab
#> 4   4 6.033745   bd   bc
#> 1   1 4.669300    d    c
#> 2   2 4.127017   cd   cd
#> 3   3 2.064654    c    d
#> 5   5 2.048229    c    d
#> 6   6 1.774765    c    d

# NOT RUN {
library(latticeExtra)
library(grid)

ci <- as.data.frame(
    confint(glht(m0, linfct = X),
            calpha = univariate_calpha())$confint)
ci <- cbind(res, ci)

segplot(reorder(trt, Estimate) ~ lwr + upr,
        centers = Estimate,
        data = ci,
        draw = FALSE,
        cld = ci$let2,
        par.settings = list(layout.widths = list(right.padding = 7))) +
    layer(panel.text(x = centers,
                     y = z,
                     labels = sprintf("%0.2f %s",
                                      centers,
                                      cld),
                     pos = 3))

ocld <- with(ci[order(ci$Estimate), ],
     ordered_cld(cld2, Estimate))
x <- attr(ocld, "ind")
index <- which(x, arr.ind = TRUE)
trellis.focus("panel", column = 1, row = 1, clip.off = TRUE)
xcor <- 1.03 + (index[, 2] - 1)/50
grid.segments(x0 = unit(xcor, "npc"),
              x1 = unit(xcor, "npc"),
              y0 = unit(index[, 1] + 0.5, units = "native"),
              y1 = unit(index[, 1] - 0.5, units = "native"),
              gp = gpar(lwd = 2, col = "blue"))
trellis.unfocus()

# }