Description Usage Arguments Details Value Author(s) References Examples
A function that generates the uncertainty diagnostic function (U-function
) and estimates DS(G,m) prior model.
1 2 3 4 5 |
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.
1 2 3 4 5 6 7 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.