getAccRegion {poweRbal} | R Documentation |
getAccRegion
- Computes the region of acceptance based on quantiles
for a specified level of significance and method.
getAccRegion_sampled
- Computes a sampling-based region of acceptance
for the given null model based on quantiles for a specified level of
significance and method.
getAccRegion_exact
- Computes the exact region of acceptance for the
given null model based on quantiles for a specified level of significance
and method. Currently, this is only implemented for
null_model = "yule"
or "pda"
, and n
<=20.
computeAccRegion
- Computes the bounds of the region of acceptance
given the empirical distribution function (specified by the unique values
and their probabilities under the null model) for specified cut-offs
(e.g., 0.025 on both sides for a symmetric two-tailed test).
For values strictly outside of the interval the null hypothesis is
rejected.
This function also computes the probabilities to
reject the null hypothesis if the value equals the lower or upper bound of
the region of acceptance. This probability is 0 for correction method
"none" and for "small-sample" it ensures that the probability of rejection
exactly corresponds with the specified cut-offs.
getAccRegion(
tss,
null_model = "yule",
n,
distribs = "exact_if_possible",
N_null = 10000L,
N_alt = 1000L,
N_intervals = 1000L,
test_type = "two-tailed",
correction = "small-sample",
sig_lvl = 0.05
)
getAccRegion_sampled(
tss,
null_model = "yule",
n,
N_null,
N_alt = 1000L,
N_intervals = 1000L,
test_type = "two-tailed",
correction = "small-sample",
sig_lvl = 0.05
)
getAccRegion_exact(
tss,
null_model = "yule",
n,
N_alt = 1000L,
N_intervals = 1000L,
test_type = "two-tailed",
correction = "small-sample",
sig_lvl = 0.05
)
computeAccRegion(
unique_null_vals,
unique_null_probs,
correction,
cutoff_left,
cutoff_right
)
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 |
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 |
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 with that the
bounds of the critical region) are computed. Available are:
|
N_null |
Sample size (integer >=10) if distributions are sampled (default = 10000L). |
N_alt |
Sample size (integer >=10) for the alternative models to
estimate the power (default = 1000L). Only needed here if the
|
N_intervals |
Number (integer >=3, default = 1000L) of different
quantile/cut-off pairs investigated as potential bounds of the region of
acceptance. This parameter is only necessary if the |
test_type |
Determines the method. Available are:
|
correction |
Specifies the desired correction method.
Available are:
|
sig_lvl |
Level of significance (default=0.05, must be >0 and <1). |
unique_null_vals |
Numeric vector containing all the unique values under the null model. |
unique_null_probs |
Numeric vector containing the corresponding probabilities of the unique values under the null model. |
cutoff_left |
Numeric value (>=0, <1) specifying the cut-off of the distribution for the lower bound of the region of acceptance. The sum of the two cut-offs must be <1. |
cutoff_right |
Numeric value (>=0, <1) specifying the cut-off of the distribution for the upper bound of the region of acceptance. The sum of the two cut-offs must be <1. |
getAccRegion
Numeric matrix (one row per TSS) with four
columns: The first two columns contain the interval limits of the region
of acceptance, i.e., we reject the null hypothesis for values strictly
outside of this interval. The third and fourth columns contain the
probabilities to reject the null hypothesis if values equal the lower or
upper bound, respectively.
getAccRegion_sampled
Numeric matrix (one row per TSS) with
four columns - similar as getAccRegion
.
getAccRegion_exact
Numeric matrix (one row per TSS) with
four columns - similar as getAccRegion
.
computeAccRegion
Numeric vector with
four columns - similar as getAccRegion
.
getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L)
getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, null_model = "etm",
N_null = 20L, correction = "none", distribs = "sampled")
getAccRegion(tss = c("Sackin", "Colless", "B1I"), n = 6L, N_null = 20L,
test_type = "two-tailed-unbiased", N_intervals = 5L,
N_alt = 10L)
getAccRegion_sampled(tss = c("Sackin", "Colless", "B1I"), n = 6L,
N_null = 20L, correction = "none")
getAccRegion_exact(tss = c("Sackin", "Colless", "B1I"),
null_model = "etm", n = 8L)
computeAccRegion(unique_null_vals = c(1,2,3,4,5),
unique_null_probs = c(0.1,0.4,0.1,0.2,0.2),
correction = "small-sample",
cutoff_left = 0.15, cutoff_right = 0.15)