perm-internal {perm} | R Documentation |
Internal Functions for perm package: Not to be called by User
Description
These functions are where the algorithms are done. There is much room for improvement in the speed of the exact functions.
Usage
ksample.exact.mc(scores, group, nmc = 10^4 - 1, seed = 1234321,
digits = 12, p.conf.level = 0.99, setSEED = TRUE)
ksample.pclt(scores, group)
trend.exact.mc(scores, group, alternative = "two.sided", nmc = 10^3 - 1,
seed = 1234321, digits = 12, p.conf.level = 0.99, setSEED = TRUE)
trend.pclt(scores, group)
twosample.exact.ce(scores, group, cm = NULL, digits = 12)
twosample.exact.mc(scores, group, alternative = "two.sided", nmc = 10^4 - 1,
seed = 1234321, digits = 12, p.conf.level = 0.99, setSEED = TRUE)
twosample.pclt(scores, group)
twosample.exact.network(scores, group, digits = 12)
getcnt(nodehk, cnt.edge, edgesize)
Arguments
scores |
vector of response scores |
group |
covariate vector |
alternative |
one of 'less', 'greater', 'two.sided' or 'two.sidedAbs' |
nmc |
number of Monte Carlo replications |
seed |
random number seed |
digits |
digits for rounding of test statistic, equal to that many digits are called tied |
p.conf.level |
confidence level for p-value, used with mc methods |
setSEED |
logical, set to FALSE when performing simulations on mc methods |
cm |
for speed you can input the matrix created from chooseMatrix (see |
nodehk |
nodes for which indeces of arcs are needed |
cnt.edge |
vector of first index for each node |
edgesize |
vector of number of arcs for each node |
Details
Network algorithm is very basic, only works for two group tests. The function getcnt
(called by twosample.exact.network)
gets a vector of indeces representing arcs for set of nodes.
Value
The function getcnt
returns
a vector of indeces representing arcs for set of nodes