Description Usage Arguments Value Details References Examples
A function to compute Empirical Bayes estimates using deconvolution
1 2 3 
tau 
a vector of (implicitly m) discrete support points for θ 
X 
the vector of sample values: a vector of counts for Poisson, a vector of zscores for Normal, a 2d matrix with rows consisting of pairs, (trial size n_i, number of successes X_i) for Binomial. See details below 
y 
the multinomial counts. See details below 
Q 
the Q matrix, implies y and P are supplied as well; see details below 
P 
the P matrix, implies Q and y are supplied as well; see details below 
n 
the number of support points for X. Applies only to
Poisson and Normal. In the former, implies that support of X is
1 to n or 0 to n1 depending on the 
family 
the exponential family, one of 
ignoreZero 
if the zero values should be ignored (default =

deltaAt 
the theta value where a delta function is desired
(default 
c0 
the regularization parameter (default 1) 
scale 
if the Q matrix should be scaled so that the spline
basis has mean 0 and columns sum of squares to be one, (default

pDegree 
the degree of the splines to use (default 5). In notation used in the references below, p = pDegree + 1 
aStart 
the starting values for the nonlinear optimization, default is a vector of 1s 
... 
further args to function 
a list of 9 items consisting of
mle 
the maximum likelihood estimate \hat{α} 
Q 
the m by p matrix Q 
P 
the n by m matrix P 
S 
the ratio of artificial to genuine information per the reference below, where it was referred to as R(α) 
cov 
the covariance matrix for the mle 
cov.g 
the covariance matrix for the g 
mat 
an m by 6 matrix containing columns for theta, g, std. error of g, G (the cdf of g), std. error of G, and the bias of g 
loglik 
the negative loglikelihood function for the data taking a pvector argument 
statsFunction 
a function to compute the statistics returned above 
The data X
is always required with two exceptions. In the Poisson case,
y
alone may be specified and X
omitted, in which case the sample space of
the observations $X$ is assumed to be 1, 2, .., length(y)
. The second exception is
for experimentation with other exponential families besides the three implemented here:
y
, P
and Q
can be specified together.
Bradley Efron. Empirical Bayes Deconvolution Estimates. Biometrika 103(1), 120, ISSN 00063444. doi:10.1093/biomet/asv068. http://biomet.oxfordjournals.org/content/103/1/1.full.pdf+html
Bradley Efron and Trevor Hastie. Computer Age Statistical Inference. Cambridge University Press. ISBN 978117149892. Chapter 21.
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.