Description Usage Arguments Value Author(s) See Also Examples
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
1 2 3 4 5 6 | 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)
|
model |
object of class |
type.prior |
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” |
mean.vague |
The mean of Normal priors for a “vague” prior |
prec.vague |
The precision of Normal priors for a “vague” prior |
A.vague |
The shape parameter of the Gamma prior for the precision parameters for a “vague” prior |
B.vague |
The rate (scale) parameter of the Gamma prior for the precision parameters for a “vague” prior |
prec.strong |
Precision for Normal mean parameters when
|
n.individuals |
Used for Binomial calculations to set prior
precision parameters when |
reffect.A |
The shape parameter of the Gamma prior for the precision parameter of the random.effect for a “vague” prior |
reffect.B |
The rate (scale) parameter of the Gamma prior for the precision parameter of the random.effect for a “vague” prior |
M.sd |
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 |
UPPER |
Cutoff for guessing parameters on logit scale noting that logit(1) is undefined |
STRONG.PREC |
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)) |
PREC.INT |
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 |
params |
if |
segRatio |
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 |
Type of prior: one of “vague”, “strong” or “specified” |
bugs.code |
Text containing prior statements for |
random.effect |
Logical indicating whether model contains random
effect (Default: |
equal.variances |
Logical indicating equal or separate variances for each component |
params |
List containing Normal means on logit scale
|
call |
function call |
Peter Baker p.baker1@uq.edu.au
setModel
setInits
expected.segRatio
segRatio
setControl
dumpData
dumpInits
or for an easier way to
run a segregation ratio mixture model see
runSegratioMM
1 2 3 4 5 6 7 8 9 10 |
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