May be used to automatically set up vague or strong priors or
explicitly set them for Bayesian finite mixture model specified as an
object of class modelSegratioMM using
setModel
setPriors(model, type.prior = c("strong",
"vague", "strong.tau","strong.s", "specified"),
mean.vague = 0.1, prec.vague = 0.1, A.vague = 0.1, B.vague = 0.1,
prec.strong=400, n.individuals=200, reffect.A = 44, reffect.B = 0.8,
M.sd = 0.025, STRONG.PREC=c(0.025, 0.975), UPPER = 0.995, PREC.INT=0.2,
params = NULL, segRatio = NULL)object of class modelSegratioMM specifying model
parameters, ploidy etc
The type of prior required being one of “strong”, “vague”, “strong.tau” “strong.s” or “specified”. The first four prior types will automatically set prior distributions whereas for the last, namely “specified”, the prior distribution parameters must be set explicitly. Note that strong priors get progressively stronger from “strong” to “strong.s”
The mean of Normal priors for a “vague” prior
The precision of Normal priors for a “vague” prior
The shape parameter of the Gamma prior for the precision parameters for a “vague” prior
The rate (scale) parameter of the Gamma prior for the precision parameters for a “vague” prior
Precision for Normal mean parameters when
type.prior is “strong”. Note that on logit scale
default is equivalent to having a 95%CI as +/- 0.1
Used for Binomial calculations to set prior
precision parameters when type.prior is “strong”.
The shape parameter of the Gamma prior for the precision parameter of the random.effect for a “vague” prior
The rate (scale) parameter of the Gamma prior for the precision parameter of the random.effect for a “vague” prior
Approximate standard deviation for the mean segregation ratios on raw probability scale - this is set to 0.025 which would give an approximate 95% interval of 0.1 for the segregation ratio
Cutoff for guessing parameters on logit scale noting that logit(1) is undefined
Interval on raw probabilty scale used to set strong priors on the the precision distribution parameters of the segregation ratios by using a 95% interval on the theoretical distribution and equating this on the logit scale (Default: c(0.025, 0.975))
Multiplier or setting prior for precision on logit scale corresponding to approx confidence region being precision*(1 - PREC.INT, 1 + PREC.INT) Default:0.2
if type.prior is “specified” then a list
of priors parameters must be set containing components M for means,
A and B for gamma prior parameters and if the model contains a
random.effect then reffect.A, and reffect.B for the gamma prior for
the precision of random effect taub. Note that the lengths of
M, prec, A and B should be equal to the number of components
If specified, this value overides the automatically generated value which is set as the expected segregation ratio given the ploidy level
Returns an object of class priorsSegratioMM which is a list
with components
Type of prior: one of “vague”, “strong” or “specified”
Text containing prior statements for BUGS file
Logical indicating whether model contains random
effect (Default: FALSE)
Logical indicating equal or separate variances for each component
List containing Normal means on logit scale
logit.means, precision on logit scale logit.prec, and
Gamma parameters A and B and finally reffect.A and
reffect.B if the model contains a random effect
function call
setModel setInits
expected.segRatio
segRatio
setControl
dumpData dumpInits or for an easier way to
run a segregation ratio mixture model see
runSegratioMM
# NOT RUN {
## simulate small autooctaploid data set
a1 <- sim.autoMarkers(8,c(0.7,0.2,0.1),n.markers=100,n.individuals=50)
## set up model with 3 components
x <- setModel(3,8)
x2 <- setPriors(x)
print(x2)
x2b <- setPriors(x, "strong")
print(x2b)
# }
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