phyloP in SPH mode
1 2 3 4 5 6  phyloP.sph(mod, msa = NULL, mode = "CON", features = NULL,
basewise = FALSE, subtree = NULL, ref.idx = 1, outfile = NULL,
outfile.only = FALSE, outfile.format = "default", prior.only = FALSE,
nsites = NULL, post.only = FALSE, fit.model = FALSE,
epsilon = ifelse(basewise, 1e06, 1e10), confidence.interval = NULL,
quantiles = FALSE)

mod 
An object of class 
msa 
The multiple alignment to be scored. 
mode 
The type of pvalue to compute. One of "CON", "ACC", "NNEUT", or "CONACC". 
features 
A features object of type 
basewise 
Logical. If 
subtree 
A character string giving the name of a node in the tree. Partition the tree into the subtree beneath the node and the complementary supertree, and consider conservation/acceleration in the subtree given the supertree. The branch above the specified node is included with the subtree. 
ref.idx 
index of reference sequence in the alignment. If zero, use frame of reference of entire alignment. If 1 and features is used, try to guess the frame of reference for each feature based on sequence name. 
outfile 
Character string. If given, write results to given file. 
outfile.only 
Logical. If 
outfile.format 
Character string describing output format. Possible formats depend on other options (see description below). 
prior.only 
Logical. If 
nsites 
Integer. Number of sites to consider if prior.only is

post.only 
Logical. If 
fit.model 
Logical. If 
epsilon 
Numeric value indicating the thhreshold used in truncating tails of distributions; tail probabilities less than this value are discarded. This only applies to the right tail. 
confidence.interval 
Numeric value between 0 and 1. If given, allow for uncertainty in the estimate of the actual number of substitutions by using a central confidence interval about the mean of given size. To be conservative, the maximum of this interval is used when computing a pvalue of conservation, and the minimum is used when computing a pvalue of acceleration. The variance of the posterior is computed exactly, but the confidence interval is based on the assumption that the combined distribution will be approximately normal (true for large numbers of sites by the central limit theorem). 
quantiles 
Logical. If 
Either a list, data frame, or matrix, depending on options.
Melissa J. Hubisz and Adam Siepel
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