rNormal_reg.wfit: Fitter Function for Bayesian Linear Models

In knygren/glmbayes: Bayesian Generalized Linear Models (iid Samples)

Description

Basic computing engine called to find the posterior mode and a UL decomposition

Usage

rNormal_reg.wfit(
  x,
  y,
  P,
  mu,
  w,
  offset = NULL,
  method = "qr",
  tol = 1e-07,
  singular.ok = TRUE,
  ...
)

Arguments

x	design matrix of dimension n * p.
y	vector of observations of length n, or a matrix with n rows.
P	Prior precision matrix of dimension p * p.
mu	Prior mean vector of length p.
w	vector of weights (length n) to be used in the fitting process for the wfit functions. Weighted least squares is used with weights w, i.e., sum(w * e^2) is minimized.
offset	(numeric of length n). This can be used to specify an a priori known component to be included in the linear predictor during fitting.
method	currently, only method = "qr" is supported.
tol	tolerance for the qr decomposition. Default is 1e-7.
singular.ok	logical. If FALSE, a singular model is an error.
...	currently disregarded.

Value

a list wih components:

Examples

## ----dobson-------------------------------------------------------------------
## Dobson (1990) Page 93: Randomized Controlled Trial :
counts <- c(18,17,15,20,10,20,25,13,12)
outcome <- gl(3,1,9)
treatment <- gl(3,3)

## Prior mean vector 
mu<-matrix(0,5)           
mu[1,1]=log(mean(counts)) 
## Prior standard deviation and Variance
mysd<-1           
V=((mysd)^2)*diag(5)  
## Call to glmb
glmb.D93<-glmb(n=1000,counts ~ outcome + treatment,
               family = poisson(),pfamily=dNormal(mu=mu,Sigma=V))
## ----glmb confint-------------------------------------------------------------
confint(glmb.D93)

knygren/glmbayes documentation built on Sept. 4, 2020, 4:39 p.m.