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Test Differences Between Sets of Transmission Trees

tree_test.Rd

Performs a statistical test to assess whether there are significant differences between sets of transmission trees. Supports PERMANOVA (via "vegan::adonis2"), Chi-Square, or Fisher's Exact Test.

Usage

tree_test(
  ...,
  method = c("permanova", "chisq", "fisher"),
  within_dist = patristic,
  between_dist = euclidean,
  test_args = list()
)

Arguments

...

Two or more sets of transmission trees. Each set must be a list of data frames with columns from (infector) and to (infectee).

method

A character string specifying the test method. Options are "permanova", #' "chisq", or "fisher". Default is "permanova".

within_dist

A function to compute pairwise distances within a tree for PERMANOVA. Takes a data frame, returns a square matrix. Default is patristic.

between_dist

A function to compute distance between two trees for PERMANOVA. Takes two matrices, returns a numeric value. Default is euclidean.

test_args

A list of additional arguments to pass to the underlying test function (vegan::adonis2, stats::chisq.test, or stats::fisher.test). Default is an empty list.

Value

  • For "permanova": A "vegan::adonis2" object containing the test results.

  • For "chisq" or "fisher": An "htest" object with the test results.

Details

This function compares sets of transmission trees using one of three statistical tests.

PERMANOVA: Evaluates whether the topological distribution of transmission trees differs between sets.

  • Null Hypothesis (H0): Transmission trees in all sets are drawn from the same distribution, implying similar topologies.

  • Alternative Hypothesis (H1): At least one set of transmission trees comes from a different distribution.

Chi-Square or Fisher’s Exact Test: Evaluates whether the distribution of infector-infectee pairs differs between sets.

  • Null Hypothesis (H0): The frequency of infector-infectee pairs is consistent across all sets.

  • Alternative Hypothesis (H1): The frequency of infector-infectee pairs differs between at least two sets.

Examples

set.seed(1)
# Generate example sets
setA <- replicate(10, igraph::as_long_data_frame(
  make_tree(n_cases = 10, R = 2, stochastic = TRUE)
), simplify = FALSE)
setB <- replicate(10, igraph::as_long_data_frame(
  make_tree(n_cases = 10, R = 2, stochastic = TRUE)
), simplify = FALSE)
setC <- replicate(10, igraph::as_long_data_frame(
  make_tree(n_cases = 10, R = 4, stochastic = TRUE)
), simplify = FALSE)

# PERMANOVA test
tree_test(setA, setB, setC,  method = "permanova")
#> Warning: number of items to replace is not a multiple of replacement length
#> Permutation test for adonis under reduced model
#> Permutation: free
#> Number of permutations: 999
#> 
#> (function (formula, data, permutations = 999, method = "bray", sqrt.dist = FALSE, add = FALSE, by = NULL, parallel = getOption("mc.cores"), na.action = na.fail, strata = NULL, ...) 
#>          Df SumOfSqs      R2      F Pr(>F)  
#> Model     2   103.13 0.10511 1.5856  0.053 .
#> Residual 27   878.10 0.89489                
#> Total    29   981.23 1.00000                
#> ---
#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

# Chi-Square test
tree_test(setA, setB, setC, method = "chisq")
#> Warning: Chi-squared approximation may be incorrect
#> 
#> 	Pearson's Chi-squared test
#> 
#> data:  count data
#> X-squared = 72.91, df = 54, p-value = 0.04412
#>