mgsa( o, sets, population=NULL, alpha=seq(0.01,0.3, length.out=10), beta=seq(0.1,0.8, length.out=10), p=seq( min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out=10), steps=1e6, restarts=5, threads=0 )
"mgsa"(o, sets, population = NULL, alpha = seq(0.01, 0.3, length.out = 10), beta = seq(0.1, 0.8, length.out = 10), p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), steps = 1e+06, restarts = 5, threads = 0)
"mgsa"(o, sets, population = NULL, alpha = seq(0.01, 0.3, length.out = 10), beta = seq(0.1, 0.8, length.out = 10), p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), steps = 1e+06, restarts = 5, threads = 0)
"mgsa"(o, sets, population = NULL, alpha = seq(0.01, 0.3, length.out = 10), beta = seq(0.1, 0.8, length.out = 10), p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), steps = 1e+06, restarts = 5, threads = 0)
"mgsa"(o, sets, population = NULL, alpha = seq(0.01, 0.3, length.out = 10), beta = seq(0.1, 0.8, length.out = 10), p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), steps = 1e+06, restarts = 5, threads = 0)
"mgsa"(o, sets, population = NULL, alpha = seq(0.01, 0.3, length.out = 10), beta = seq(0.1, 0.8, length.out = 10), p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), steps = 1e+06, restarts = 5, threads = 0)numeric,
integer, character or logical. See
details.MgsaSets or a list. In this
case, each list entry is a vector of type numeric,
integer, character. See details.numeric, integer or character
vector. Default to NULL. See details.numeric.numeric.numeric.integer of length 1. A recommended value
is 1e6 or greater.integer of length 1. Must be greater or
equal to 1. A recommended value is 5 or greater.MgsaMcmcResults object.
character or integer. For convenience
numeric items can also be provided but these
values should essentially be integers. The type of items
in the observations o, the sets and in the
optional population should be consistent. In the
case of character items, o and
population should be of type character and
sets can either be an
MgsaSets or a list of
character vectors. In the case of integer
items, o should be of type integer,
numeric (but essentially with integer values), or
logical and entries in sets as well as the
population should be integer. When o
is logical, it is first coerced to integer with a
call on which. Observations outside the
population are not taken into account. If
population is NULL, it is defined as the
union of all sets. The default grid value for p is such that between 1 and
20 sets are active in expectation. The lower limit is
constrained to be lower than 0\.1 and the upper limit
lower than 0\.3 independently of the total number of sets
to make sure that complex solutions are penalized.
Marginal posteriors of activity of each set are estimated
using an MCMC sampler as described in Bauer et al., 2010.
Because convergence of an MCM sampler is difficult to
assess, it is recommended to run it several times (using
restarts). If variations between runs are too
large (see MgsaResults), the number of
steps (steps) of each MCMC run should be
increased.
MgsaResults, MgsaMcmcResults
## observing items A and B, with sets {A,B,C} and {B,C,D}
mgsa(c("A", "B"), list(set1 = LETTERS[1:3], set2 = LETTERS[2:4]))
## same case with integer representation of the items and logical observation
mgsa(c(TRUE,TRUE,FALSE,FALSE), list(set1 = 1:3, set2 = 2:4))
## a small example with gene ontology sets and plot
data(example)
fit = mgsa(example_o, example_go)
## Not run:
plot(fit)
## End(Not run)
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