mgsa(o, sets, population = NULL, p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), ...)
"mgsa"(o, sets, population = NULL, p = seq(1, min(20, floor(length(sets)/3)), length.out = 10)/length(sets), ...)
"mgsa"(o, sets, population = NULL, p = seq(1, min(20, floor(length(sets)/3)), length.out = 10)/length(sets), ...)
"mgsa"(o, sets, population = NULL, p = seq(1, min(20, floor(length(sets)/3)), length.out = 10)/length(sets), ...)
"mgsa"(o, sets, population = NULL, p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), ...)
"mgsa"(o, sets, population = NULL, p = seq(min(0.1, 1/length(sets)), min(0.3, 20/length(sets)), length.out = 10), ...)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.alphanumeric.
betanumeric.
stepsinteger of length 1. A recommended value is 1e6 or greater.
burnininteger of length 1. A recommended value is half of total MCMC steps.
thininteger of length 1. A recommended value is 100 to reduce autocorrelation of subsequently collected samples.
flip.freqnumeric from (0,1].
restartsinteger of length 1. Must be greater or equal to 1. A recommended value is 5 or greater.
threadsMgsaMcmcResults 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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