rl.gibbs {blink} | R Documentation |
Gibbs sampler for empirically motivated Bayesian record linkage
Description
Gibbs sampler for empirically motivated Bayesian record linkage
Usage
rl.gibbs(
file.num = file.num,
X.s = X.s,
X.c = X.c,
num.gs = num.gs,
a = a,
b = b,
c = c,
d = d,
M = M
)
Arguments
file.num |
The number of the file |
X.s |
A vector of string variables |
X.c |
A vector of categorical variables |
num.gs |
Total number of gibb iterations |
a |
Shape parameter of Beta prior |
b |
Scale parameter of Beta prior |
c |
Positive constant |
d |
Any distance metric measuring the latent and observed string |
M |
The true value of the population size |
Value
lambda.out The estimated linkage structure via Gibbs sampling
Examples
data(RLdata500)
X.c <- as.matrix(RLdata500[c("by","bm","bd")])[1:3,]
p.c <- ncol(X.c)
X.s <- as.matrix(RLdata500[c(1,3)])[1:3,]
p.s <- ncol(X.s)
file.num <- rep(c(1,1,1),c(1,1,1))
d <- function(string1,string2){adist(string1,string2)}
lam.gs <- rl.gibbs(file.num,X.s,X.c,num.gs=2,a=.01,b=100,c=1,d, M=3)
[Package blink version 1.1.0 Index]