Package 'cld'

Description

Generic function to convert various objects to cld_object format. This is useful when you have CLD results from other packages or custom formats and want to use them with cld's methods.

Usage

as_cld(x, ...)

## S3 method for class 'data.frame'
as_cld(x, ...)

## S3 method for class 'character'
as_cld(x, ...)

## S3 method for class 'cld_object'
as_cld(x, ...)

Arguments

Details

The as_cld() function provides a way to convert various formats to the standard cld_object structure. This is particularly useful when:

• Working with CLD results from other packages

• Converting custom formats to use cld's print and coercion methods

• Creating CLD objects programmatically

Value

A cld_object (data frame with class c("cld_object", "data.frame")) containing three columns:

• group - Character. The names of the groups

• cld - Character. The compact letter display for each group

• spaced_cld - Character. A monospaced version of the CLD where spaces are replaced with underscores, useful for maintaining alignment in output

See Also

• make_cld() for creating compact letter displays

• pval_matrix_to_df() for converting p-value matrices

• cld_methods for other cld_object methods

Other helper functions: pval_matrix_to_df()

Other data conversion functions: pval_matrix_to_df()

Examples

# Convert from data frame
df <- data.frame(
  group = c("A", "B", "C"),
  cld = c("a", "b", "b")
)
as_cld(df)

# Convert from named character vector
letters_vec <- c(A = "a", B = "b", C = "b")
as_cld(letters_vec)

Description

Print and coercion methods for cld_object

Usage

## S3 method for class 'cld_object'
print(x, ...)

## S3 method for class 'cld_object'
as.data.frame(x, ...)

## S3 method for class 'cld_object'
as.character(x, ...)

## S3 method for class 'cld_object'
as_tibble(x, ...)

Arguments

Value

• print.cld_object(): Invisibly returns the input object

• as.data.frame.cld_object(): Returns a plain data frame

• as.character.cld_object(): Returns a named character vector of letters

• as_tibble.cld_object(): Returns a tibble

See Also

• make_cld() for creating compact letter displays

• as_cld() for converting objects to cld_object

Examples

obj <- pairwise.wilcox.test(chickwts$weight, chickwts$feed, exact = FALSE)
result <- make_cld(obj)

# Print method
print(result)

# Convert to plain data frame
as.data.frame(result)

# Convert to named character vector
as.character(result)

# Convert to tibble
if (requireNamespace("tibble", quietly = TRUE)) {
  tibble::as_tibble(result)
}

Description

Creates a compact letter display for results of pair-wise comparisons (e.g., ANOVA post-hoc tests, Kruskal-Wallis post-hoc tests, and others). Groups that share the same letter are not significantly different from each other. This provides a visual summary of which groups differ significantly, commonly used in agricultural, biological, and statistical reporting.

Usage

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

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

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

## S3 method for class 'PostHocTest'
make_cld(obj, ..., alpha = 0.05)

## S3 method for class 'DunnTest'
make_cld(obj, ..., alpha = 0.05)

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

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

## S3 method for class 'data.frame'
make_cld(
  obj,
  ...,
  alpha = 0.05,
  gr1_col = "group1",
  gr2_col = "group2",
  p_val_col = "p.adj",
  remove_space = FALSE
)

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

Arguments

Value

A data frame of class c("cld_object", "data.frame") with three columns:

• group - Character. The names of the groups being compared

• cld - Character. The compact letter display for each group. Groups sharing at least one letter are not significantly different from each other

• spaced_cld - Character. A monospaced version of the CLD where spaces are replaced with underscores, useful for maintaining alignment in output

The rows are ordered according to the group names. Groups with the same letter(s) do not differ significantly at the specified alpha level.

Handling Group Names with Hyphens

The underlying multcompView package uses hyphens (-) as the default separator between group names in comparison strings (e.g., "GroupA-GroupB"). This creates a conflict when group names themselves contain hyphens (e.g., "Plant-based", "Treatment-1").

Automatic Handling: Most methods (matrix, data.frame, pairwise.htest, pairwise_pval_df, and others) automatically detect and handle hyphens in group names by:

1. Temporarily replacing hyphens with alternative characters (underscore, en-dash, etc.)

2. Processing the comparisons

3. Restoring the original hyphens in the output

An informational message is shown when this occurs. To suppress it, use quiet_hyphen_warning = TRUE.

Formula Method Limitation: The formula method (e.g., make_cld(p_value ~ comparison, data = df)) has limited support for group names with hyphens because it receives pre-formatted comparison strings where the separator hyphens cannot be reliably distinguished from hyphens within group names.

Best Practice for Hyphenated Group Names: Use the data.frame method with gr1_col and gr2_col parameters. This method handles hyphens automatically and seamlessly. For example:

# Instead of: make_cld(p_value ~ comparison, data = df)
# Use: make_cld(df, gr1_col = "group1", gr2_col = "group2", p_val_col = "p_value")

Alternative Workarounds for Formula Method:

• Convert your data to matrix format (also handles hyphens automatically)

• Replace hyphens in group names with underscores before creating comparisons

• Use a different separator in comparison strings (e.g., " vs " with swap_vs = TRUE)

References

Piepho HP (2004). An algorithm for a letter-based representation of all-pairwise comparisons. Journal of Computational and Graphical Statistics, 13(2), 456-466. doi:10.1198/1061860043515, available at https://www.tandfonline.com/doi/abs/10.1198/1061860043515

See Also

Helper Functions:

• pval_matrix_to_df() for converting p-value matrices to data frames

• as_cld() for converting other formats to cld_object

Output Methods:

• print.cld_object() for displaying CLD results

• cld_methods for coercion methods (as.data.frame, as.character, as_tibble)

Underlying Algorithm:

• multcompView::multcompLetters() for the letter generation algorithm

Examples

# Example 1: Using pairwise.htest (Wilcoxon test)
obj1 <- stats::pairwise.wilcox.test(chickwts$weight, chickwts$feed, exact = FALSE)
cld::make_cld(obj1)

# Example 2: Using pairwise.htest (t-test)
obj2 <- with(OrchardSprays, stats::pairwise.t.test(decrease, treatment))
cld::make_cld(obj2)

# Example 3: Using pairwise.htest (proportion test)

smokers <- c(83, 90, 129, 70)
patients <- c(86, 93, 136, 82)
obj3 <- stats::pairwise.prop.test(smokers, patients)
cld::make_cld(obj3)


# Example 4: Using PMCMR objects

obj4 <- PMCMRplus::kwAllPairsConoverTest(count ~ spray, data = InsectSprays)
cld::make_cld(obj4)


# Example 5: Using DescTools objects

obj5 <- DescTools::ConoverTest(weight ~ group, data = PlantGrowth)
cld::make_cld(obj5)


# Example 6: Using rstatix data frames (via data.frame method)

obj6 <- rstatix::games_howell_test(PlantGrowth, weight ~ group)
cld::make_cld(obj6, gr1_col = "group1", gr2_col = "group2", p_val_col = "p.adj")


# Example 7: Using formula interface
my_dataframe <- utils::read.table(
  text = c(
    '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'
  ),
  header = TRUE
)
cld::make_cld(p.adjust ~ Comparison, data = my_dataframe)

# Example 8: Using a symmetric matrix 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

# Make CLD
cld::make_cld(obj8)

# Example 9: Using data.frame method with custom column names
my_data <- data.frame(
  group1 = c("A", "A", "B"),
  group2 = c("B", "C", "C"),
  p.adj = c(0.001, 0.045, 0.892)
)
cld::make_cld(my_data, gr1_col = "group1", gr2_col = "group2", p_val_col = "p.adj")

Description

This function converts a matrix of p-values into a data frame with three columns: gr1 (column names), gr2 (row names), and p_values (the p-values). This is particularly useful when you have a symmetric matrix of p-values from pairwise comparisons and need to convert it to a long-format data frame for further analysis or for use with make_cld().

Usage

pval_matrix_to_df(x)

Arguments

Value

A data frame of class c("pairwise_pval_df", "data.frame") with columns:

• gr1 - names from the columns of the matrix

• gr2 - names from the rows of the matrix

• p_values - the corresponding p-values from the matrix (excluding NA values)

See Also

• make_cld() for creating compact letter displays from the resulting data frame

• as_cld() for converting other formats to cld_object

Other helper functions: as_cld()

Other data conversion functions: as_cld()

Examples

# Create a matrix of p-values
m <- matrix(c(NA, 0.05, 0.01, 0.05, NA, 0.03, 0.01, 0.03, NA), nrow = 3)
colnames(m) <- c("A", "B", "C")
rownames(m) <- c("A", "B", "C")

# Convert to data frame
cld::pval_matrix_to_df(m)