Description Usage Arguments Value Author(s)
Full model:
β~N(μ_0, Σ_0), σ^2~IG(α, β), y~N(Xβ, σ^2)
For the restricted likelihood, conditioning is done on a pair of location and scale statistics T(y)=(b(y), s(y)). Current implementation allows for these to be a pair of M-estimators as implemented in rlm
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 |
X |
a matrix or data frame containing the explanatory variables. The matrix should include a vector of 1's if intercept is desired. |
y |
vector of data |
psi |
the |
scale.est |
specification of the scale estimator (issued by name- either |
k2 |
specification of the scale estimator (issued by name- either |
alpha |
prior shape and scale for sigma^2 |
beta |
prior shape and scale for sigma^2 |
instDist |
Placeholder to allow for more user specific instrumental distributions. Currently not used. |
sdInstDist |
Vector of length 2 defining the scale for the instrumental distribution when instDist=NULL. In this case, the instrumental distribution for μ and σ^2 are independent normal and log normal distributions, respectively. The first value is then the standard deviation of the normal. The second is the standard deviation of \logσ^2 (i.e. the |
smooth |
scalar or vector of length 2 that will scale the initial bandwidth computed by |
N |
number of samples of the statistics to use for the kernel density estimation |
Nins |
number of samples from the instrumental distribution |
maxit |
the limit on the number of IWLS iterations. Same as in |
... |
arguments to pass to the psi functions |
list with elements impSamps
, w
, fit
. These are the Nins
by length(beta)+1
matrix of importance samples, the corresponding weights, and the fitted robust regression.
John R. Lewis lewis.865@osu.edu
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