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Simulate the true PIV values underlying the registered records

Source: R/FlexRecordLinkage.r
simulateH.Rd

Draw the latent true values of each PIV given the currently registered (possibly mistaken or missing) values, the current linkage status, and the current parameters.

Usage

simulateH(data, links, survivalpSameH, sumRowD, sumColD, eta, phi)

Arguments

data

List with encodedA, encodedB (the two encoded data sources, missing values as 0), n_values, PIVs_config and same_mistakes; see StEM().

2-column matrix of (A, B) indices for the currently linked records.

survivalpSameH

Matrix (n links x n PIVs); 1 for stable PIVs, and the survival probability that the true value is unchanged for unstable PIVs.

sumRowD

Logical vector, one entry per record in A: does it form a link?

sumColD

Logical vector, one entry per record in B: does it form a link?

eta

List (one per PIV) of the distribution of true values.

phi

List (one per PIV) of length-4 vectors: agreement prob. in A, agreement prob. in B, missing prob. in A, missing prob. in B.

Value

List with truepivsA and truepivsB, matrices (same shape as the input data) of simulated true PIV values.

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 )
#> Warning: PIV 'V4': values out of the common support in data2 [5].
#> '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