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@@ -75,7 +75,7 @@
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#'                     
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#' # Summarize marginal skill distribution using posterior samples for latent
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#' # class proportions
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#' marg_PIs = t(As_1984) \%*\% outchain_1984$PIs
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#' marg_PIs = t(As_1984) %*% outchain_1984$PIs
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#' PI_Est = apply(marg_PIs[, chain_samples], 1, mean)
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#' PI_Sd = apply(marg_PIs[, chain_samples], 1, sd)
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#' PIoutput = cbind(PI_Est, PI_Sd)
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#' # Setting item parameters and generating attribute profiles
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#' ss = gs = rep(.2, J)
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#' PIs = rep(1/(2^K), 2^K)
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#' CLs = c(1:(2^K)) \%*\% rmultinom(n = N, size = 1, prob = PIs) )
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#' CLs = c(1:(2^K)) %*% rmultinom(n = N, size = 1, prob = PIs)
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#'         
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#' # Defining matrix of possible attribute profiles
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#' As = rep(0,K)
Files Coverage
R/dina-est.R 75.00%
src/dina-estimation-routine.cpp 100.00%
Project Totals (2 files) 98.95%
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