Given the current draw of true PIV values, samples the linkage matrix D from its conditional distribution, and returns the updated log-likelihood.
Usage
simulateD(
data,
linksR,
truepivsA,
truepivsB,
gamma,
eta,
alpha,
phi,
model_dynamics = survival_model("exponential"),
threads = 4
)Arguments
- data
List with
encodedA,encodedB(the two encoded data sources, missing values as0),n_values,PIVs_configandsame_mistakes; seeStEM().- linksR
2-column matrix (1-indexed) of the currently linked (A, B) indices.
- truepivsA
Matrix of true PIV values, as returned by
simulateH().- truepivsB
Matrix of true PIV values, as returned by
simulateH().- gamma
Numeric, proportion of linked records as a fraction of the smaller file.
- eta
List (one per PIV) of the distribution of true values.
- alpha
List (one per PIV) of survival-model parameters (see
survival_model()).- phi
List (one per PIV) of registration-error parameters.
- model_dynamics
Object from
survival_model()giving the survival function of the structured PIVs.- threads
Integer, number of OpenMP threads for the C++ per-pair likelihood loops (default 4; identical results for any value; ignored if the package was built without OpenMP).
Value
A list (Dsample, Rcpp sampleD() output) with: links updated set
of links, sumRowD updated sumRowD, sumColD updated sumColD, loglik
updated value of the complete log likelihood, nlinkrec updated number of
linked records
Examples
PIVs_config <- list( V1 = list(dynamics = "stable",
bound_mistakes = c(0.10,0.10),
fix_mistakes = c(NA,NA)),
V2 = list(dynamics = "stable",
bound_mistakes = c(0.10,0.10),
fix_mistakes = c(NA,NA)),
V3 = list(dynamics = "flexible",
bound_mistakes = c(NA,NA),
fix_mistakes = c(NA,NA)),
V4 = list(dynamics = "structured",
bound_mistakes = c(NA,NA),
fix_mistakes = c(0.03,0.03),
cond_hazard_cov = list(cov1=c("Xe", "Xf"),
cov2=c())) )
n_values <- c( 5, 6, 7, 12 )
p_mistake <- list( V1 = c(0.02, 0.02), V2 = c(0.02, 0.02),
V3 = c(0.05, 0.05), V4 = c(0.02, 0.02) )
p_missing <- list( V1 = c(0.005, 0.005), V2 = c(0.005, 0.005),
V3 = c(0.005, 0.005), V4 = c(0.005, 0.005) )
cond_hazard_params <- list( V1 = c(), V2 = c(),
V3 = c(), V4 = log(c(0.7, 0.6, 0.5)) )
gen_data <- simulate_data( PIVs_config, n_values, c(150, 200), 100,
p_mistake, p_missing, cond_hazard_params, TRUE )
data_StEM <- prepare_data( gen_data$data1, gen_data$data2, "1", "2",
PIVs_config, TRUE, "entity_id", TRUE )
#> '2' is the larger source, saved as B; '1' saved as A.
PIVs_stable <- sapply(data_StEM$PIVs_config, function(x)
x$dynamics != "structured")
linksR = base::matrix(0,0,2)
linksCpp = linksR
sumRowD = rep(0, nrow(data_StEM$encodedA))
sumColD = rep(0, nrow(data_StEM$encodedB))
nlinkrec = 0
survivalpSameH = base::matrix(1, nrow(linksR), length(data_StEM$n_values))
gamma = 0.5
eta = lapply(data_StEM$n_values, function(x) rep(1/x,x))
phi = lapply(data_StEM$n_values, function(x) c(0.9,0.9,0.1,0.1))
n_coef_unstable = lapply( seq_along(PIVs_stable), function(idx)
if(PIVs_stable[idx]){ 0 }else{
ncol(data_StEM$encodedA[, data_StEM$PIVs_config[[idx]]$cond_hazard_cov$covA,
drop=FALSE]) +
ncol(data_StEM$encodedB[, data_StEM$PIVs_config[[idx]]$cond_hazard_cov$covB,
drop=FALSE]) + 1 } )
alpha = lapply( seq_along(PIVs_stable),
function(idx) if(PIVs_stable[idx]){ c(-Inf) }
else{ rep(log(0.05), n_coef_unstable[[idx]]) }
)
newTruePivs = simulateH(data=data_StEM, links=linksCpp,
survivalpSameH=survivalpSameH,
sumRowD=sumRowD, sumColD=sumColD, eta=eta, phi=phi)
truepivsA = newTruePivs$truepivsA
truepivsB = newTruePivs$truepivsB
Dsample = simulateD(data=data_StEM, linksR=linksR,
truepivsA=truepivsA, truepivsB=truepivsB,
gamma=gamma, eta=eta, alpha=alpha, phi=phi)
linksCpp = Dsample$links
linksR = linksCpp + 1