From 41ec57ed28806d9c683806f22ec767e4cf5caf37 Mon Sep 17 00:00:00 2001 From: James Balamuta Date: Tue, 25 Apr 2017 21:37:29 -0500 Subject: [PATCH] fourPNO - v1.0.3 --- DESCRIPTION | 4 ++-- man/Gibbs_2PNO.Rd | 2 +- man/Gibbs_4PNO.Rd | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index b137cfd..a9c9678 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -1,11 +1,11 @@ Package: fourPNO Type: Package Title: Bayesian 4 Parameter Item Response Model -Version: 1.0.2 +Version: 1.0.3 Date: 2015-11-12 Authors@R: c(person("Steven Andrew", "Culpepper", role = c("aut","cre"), email = "sculpepp@illinois.edu")) -Description: Estimate Lord & Barton's four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2015). +Description: Estimate Barton & Lord's four parameter IRT model with lower and upper asymptotes using Bayesian formulation described by Culpepper (2015). License: GPL (>= 2) Imports: Rcpp LinkingTo: Rcpp, RcppArmadillo diff --git a/man/Gibbs_2PNO.Rd b/man/Gibbs_2PNO.Rd index b2342a9..0b38f80 100644 --- a/man/Gibbs_2PNO.Rd +++ b/man/Gibbs_2PNO.Rd @@ -55,7 +55,7 @@ Sigma_xi_inv = solve(2*matrix(c(1,0,0,1),2,2)) burnin = 1000 #Execute Gibbs sampler. This should take about 15.5 minutes out_t <- Gibbs_4PNO(Y_t,mu_xi,Sigma_xi_inv,mu_theta,Sigma_theta_inv,alpha_c,beta_c,alpha_s, - beta_s,burnin,rep(1,J),rep(1,J),gwg_reps=5,chain_length=1000) + beta_s,burnin,rep(1,J),rep(1,J),gwg_reps=5,chain_length=burnin*2) #summarizing posterior distribution OUT = cbind(apply(out_t$AS[,-c(1:burnin)],1,mean),apply(out_t$BS[,-c(1:burnin)],1,mean), diff --git a/man/Gibbs_4PNO.Rd b/man/Gibbs_4PNO.Rd index 4a43337..b77e243 100644 --- a/man/Gibbs_4PNO.Rd +++ b/man/Gibbs_4PNO.Rd @@ -70,7 +70,7 @@ Sigma_xi_inv = solve(2*matrix(c(1,0,0,1),2,2)) burnin = 1000 #Execute Gibbs sampler out_t <- Gibbs_4PNO(Y_t,mu_xi,Sigma_xi_inv,mu_theta,Sigma_theta_inv,alpha_c,beta_c,alpha_s, - beta_s,burnin,rep(1,J),rep(1,J),gwg_reps=5,chain_length=1000) + beta_s,burnin,rep(1,J),rep(1,J),gwg_reps=5,chain_length=burnin*2) #summarizing posterior distribution OUT = cbind(apply(out_t$AS[,-c(1:burnin)],1,mean),apply(out_t$BS[,-c(1:burnin)],1,mean),