powerComp {poweRbal}

Comparison of the power of TSS under different models

Description

powerComp - Compare the power of a set of TSS to identify trees generated under different alternative models given a null model.

Usage

powerComp(
  tss,
  null_model = "yule",
  alt_models,
  n,
  distribs = "sampled",
  sample_size = 1000L,
  test_type = "two-tailed",
  sig_lvl = 0.05,
  with_CIradius = FALSE
)

Arguments

tss

Vector containing the names (as character) of the tree shape statistics that should be compared. You may either use the short names provided in tssInfo to use the already included TSS, or use the name of a list object containing similar information as the entries in tssInfo. Example:
Use "new_tss" as the name for the list object new_tss containing at least the function new_tss$func = function(tree){...}, and optionally also the information new_tss$short, new_tss$simple, new_tss$name, new_tss$type, new_tss$only_binary, and new_tss$safe_n.

null_model

The null model that is to be used to determine the power of the tree shape statistics. In general, it must be a function that produces rooted binary trees in phylo format.
If the respective model is included in this package, then specify the model and its parameters by using a character or list. Available are all options listed under parameter tm in the documentation of function genTrees (type ?genTrees).
If you want to include your own tree model, then use the name of a list object containing the function (dependent on one parameter n). Example:
Use "new_tm" for the list object
new_tm <- list(func = function(n, Ntrees){...}).

alt_models

List containing the alternative models that are to be used to determine the power of the tree shape statistics. Functions that produce rooted binary trees in phylo format. The information of each single model must be in the format described for null_model.

n

Integer value that specifies the desired number of leaves, i.e., vertices with in-degree 1 and out-degree 0.

distribs

Determines how the distributions and quantiles are computed. Available are:

• "sampled" (default): sample_size many trees are sample under the respective models to determine the quantiles for the null model and how many trees under the alternative models lie outside.

sample_size

Sample size (integer >=10) if distributions are sampled (default= 1000L).

test_type

Determines the method. Available are:

• "two-tailed" (default)

sig_lvl

Level of significance (default=0.05, must be >0 and <1).

with_CIradius

Determines if the radii for the 95%-confidence intervals of the power values should also be computed and returned (default = FALSE).

Value

powerComp Numeric matrix, one row per TSS and one column per alternative model if with_CIradius is FALSE (default). Else a list with two such numeric matrices, the first for the power and the second for the confidence interval radius.

Author(s)

Sophie Kersting

Examples

powerComp(tss = c("Sackin", "Colless", "B1I"),
          alt_models = list(list("aldous",-1), "pda", "etm"), n=10L,
          sample_size = 40L, with_CIradius = TRUE)