Make a compact letter display (cld) for pair-wise comparison

Make a compact letter display for results of pair-wise comparisons (e.g., ANOVA post-hoc tests, Kruskal-Wallis post-hoc tests and other).

make_cld(obj, ..., alpha = 0.05)

# S3 method for pairwise.htest
make_cld(obj, ..., alpha = 0.05)

# S3 method for posthocTGH
make_cld(obj, ..., alpha = obj$intermediate$alpha)

# S3 method for posthoc_anova
make_cld(obj, ..., alpha = 1 - obj$input$conf_level)

# S3 method for PMCMR
make_cld(obj, ..., alpha = 0.05)

# S3 method for formula
make_cld(obj, ..., data = NULL, alpha = 0.05)

# S3 method for matrix
make_cld(obj, ..., alpha = 0.05)

# S3 method for data.frame
make_cld(obj, ..., formula = p.adjust ~ Comparison, alpha = 0.05)

# S3 method for pairwise_pval_df
make_cld(obj, ..., alpha = 0.05)

Arguments

obj	Object with pair-wise comparisons (e.g., post-hoc test results). Currently supported objects: posthocTGH from package userfriendlyscience; PMCMR from package PMCMR.
...	Further arguments to methods.
alpha	(numeric from 0 to 1) Significance level.
data	A dataset with p values and names of comparisons. This argument is used if obj is formula. More details in examples.
formula	An R model stats::formula() where left-hand side term indicates variable with p values and right-hand side term defines variable with comparisons, e.g. p.adjust ~ Comparison. Usually is used in combination with data.

Value

(A data frame with) compact letter display.

Examples

library(biostat)

# Example 1: class `pairwise.htest`

obj1 <- pairwise.wilcox.test(chickwts$weight, chickwts$feed, exact = FALSE)
make_cld(obj1)
#>       group cld spaced_cld
#> 1    casein   a        a__
#> 2 horsebean   b        _b_
#> 3   linseed  bc        _bc
#> 4  meatmeal  ac        a_c
#> 5   soybean   c        __c
#> 6 sunflower   a        a__


# Example 2: class `pairwise.htest`

obj2 <- with(OrchardSprays, pairwise.t.test(decrease, treatment))
make_cld(obj2)
#>   group cld spaced_cld
#> 1     A   a        a__
#> 2     B   a        a__
#> 3     C   a        a__
#> 4     D  ab        ab_
#> 5     E  bc        _bc
#> 6     F   c        __c
#> 7     G   c        __c
#> 8     H   c        __c


# Example 3: class `pairwise.htest`
# \donttest{
smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
obj3 <- pairwise.prop.test(smokers, patients)
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect
#> Warning: Chi-squared approximation may be incorrect

make_cld(obj3)
#>   group cld spaced_cld
#> 1     1   a          a
#> 2     2   a          a
#> 3     3   a          a
#> 4     4   a          a
# }

# Example 4: class `PMCMR`
# \donttest{
obj4 <- PMCMR::posthoc.kruskal.conover.test(count ~ spray, data = InsectSprays)
#> Warning: Ties are present. Quantiles were corrected for ties.
make_cld(obj4)
#>   group cld spaced_cld
#> 1     A   a        a__
#> 2     B   a        a__
#> 3     C   b        _b_
#> 4     D   c        __c
#> 5     E  bc        _bc
#> 6     F   a        a__
# }


# Example 5: class `posthocTGH`

obj5 <- posthoc_anova(weight ~ Diet,
  data = ChickWeight,
  method = "Games-Howell"
)
make_cld(obj5)
#>   group cld spaced_cld
#> 1     1   a         a_
#> 2     2   b         _b
#> 3     3   b         _b
#> 4     4   b         _b


# Example 6: class `posthoc_anova`

obj6 <- posthoc_anova(weight ~ Diet,
  data = ChickWeight,
  method = "Games-Howell"
)
make_cld(obj6)
#>   group cld spaced_cld
#> 1     1   a         a_
#> 2     2   b         _b
#> 3     3   b         _b
#> 4     4   b         _b


# Example 7: class `formula`

my_dataframe <- data.table::fread(
  'Comparison     p.value p.adjust
    "EE - GB = 0"        1 1.000000
    "EE - CY = 0" 0.001093 0.003279
    "GB - CY = 0" 0.005477 0.008216'
)

make_cld(p.adjust ~ Comparison, data = my_dataframe)
#>   group cld spaced_cld
#> 1    EE   a         a_
#> 2    GB   a         a_
#> 3   GB0  ab         ab
#> 4   CY0   b         _b


# Example 8: class `matrix`

# (for symetric matrices of p values)

# Create matrix
m <- c(
  1.00, 0.22, 0.05, 0.00,
  0.22, 1.00, 0.17, 0.01,
  0.05, 0.17, 1.00, 0.22,
  0.00, 0.01, 0.22, 1.00
)
obj8 <- matrix(m, nrow = 4)
rownames(obj8) <- colnames(obj8) <- c("P", "O", "I", "U")
obj8
#>      P    O    I    U
#> P 1.00 0.22 0.05 0.00
#> O 0.22 1.00 0.17 0.01
#> I 0.05 0.17 1.00 0.22
#> U 0.00 0.01 0.22 1.00

# Make cld
make_cld(obj8)
#>   group cld spaced_cld
#> 1     P   a         a_
#> 2     O   a         a_
#> 3     I  ab         ab
#> 4     U   b         _b