DS.prior {BayesGOF} | R Documentation |
A function that generates the uncertainty diagnostic function (U-function
) and estimates DS(G,m) prior model.
DS.prior(input, max.m = 8, g.par, family = c("Normal","Binomial", "Poisson"), LP.type = c("L2", "MaxEnt"), smooth.crit = "BIC", iters = 200, B = 1000, max.theta = NULL)
input |
For |
max.m |
The truncation point m reflects the concentration of true unknown π around known g. |
g.par |
Vector with estimated parameters for specified conjugate prior distribution g (i.e beta prior: α and β; normal prior: μ and τ^2; gamma prior: α and β). |
family |
The distribution of y_i. Currently accommodates three families: |
LP.type |
User selects either |
smooth.crit |
User selects either |
iters |
Integer value that gives the maximum number of iterations allowed for convergence; default is 200. |
B |
Integer value for number of grid points used for distribution output; default is 1000. |
max.theta |
For |
Function can take m=0 and will return the Bayes estimate with given starting parameters. Returns an object of class DS.GF.obj
; this object can be used with plot command to plot the U-function (Ufunc
), Deviance Plots (mDev
), and DS-G comparison (DS_G
).
LP.par |
m smoothed LP-Fourier coefficients, where m is determined by maximum deviance. |
g.par |
Parameters for g. |
LP.max.uns |
Vector of all LP-Fourier coefficients prior to smoothing, where the length is the same as |
LP.max.smt |
Vector of all smoothed LP-Fourier coefficients, where the length is the same as |
prior.fit |
Fitted values for the estimated prior. |
UF.data |
Dataframe that contains values required for plotting the U-function. |
dev.df |
Dataframe that contains deviance values for values of m up to |
m.val |
The value of m (less than or equal to the maximum m from user) that has the maximum deviance and represents the appropriate number of LP-Fourier coefficients. |
sm.crit |
Smoothing criteria; either |
fam |
The user-selected family. |
LP.type |
User-selected representation of |
obs.data |
Observed data provided by user for |
Doug Fletcher, Subhadeep Mukhopadhyay
Mukhopadhyay, S. and Fletcher, D., 2018. "Generalized Empirical Bayes via Frequentist Goodness of Fit," Nature Scientific Reports, 8(1), p.9983, https://www.nature.com/articles/s41598-018-28130-5.
Mukhopadhyay, S., 2017. "Large-Scale Mode Identification and Data-Driven Sciences," Electronic Journal of Statistics, 11(1), pp.215-240.
data(rat) rat.start <- gMLE.bb(rat$y, rat$n)$estimate rat.ds <- DS.prior(rat, max.m = 4, rat.start, family = "Binomial") rat.ds plot(rat.ds, plot.type = "Ufunc") plot(rat.ds, plot.type = "DSg") plot(rat.ds, plot.type = "mDev")