Vuong, Quan-Hoang

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Dr. Quan-Hoang Vuong (v19-13) ©2018 SSHPA ∫∫ Vuong & Associates 1  R Codes for Preliminary Checks (Bayesian Method)  SSHPA / AISDL 2018  ## column names (variables): scid, bmattitude, bmuse, freqstillgood, capab, bmdiff,  ## theodiff, moddiff, resdiff, codediff, plan, trysoft, learntime ## the following codes run well with the hypothetical data set btestdata.csv ## bmdat <- read.table("c://dr.vuong/BMSurvey/btestdata.csv", sep=",", header=T)  m1 <- map(  alist(  bmuse ~ dbinom(1,p) ,  logit(p) <- a + bfreq*freqstillgood,  a ~ dnorm(0,10),  bfreq ~ dnorm(0,10)  ), data=bmdat)  m3 <- map(  alist(  bmuse ~ dbinom(1,p) ,  logit(p) <- a + bfreq*freqstillgood + bcap*capab,  a ~ dnorm(0,10),  bfreq ~ dnorm(0,10),  bcap ~ dnorm(0,10)  ), data=bmdat)  ## Note for m1: a model with the suspicion that when one considers the old way of ## analysis is still good, one is hesitant to adop the Bayesian method  m2 <- map(  alist(  bmattitude ~ dbinom(1,p) ,  logit(p) <- a + bfreq*freqstillgood + bcap*capab,  a ~ dnorm(0,10),  bfreq ~ dnorm(0,10),  bcap ~ dnorm(0,10)  ), data=bmdat)  ## Note for m2: a model with the suspicion that when one considers the old way  ## of analysis is inadequate and one's capability is good enough,  ## one will tend to learn and use Bayesian method  > compare(m1,m2,m3)    WAIC pWAIC dWAIC weight    SE   dSE m3 28.6   8.6   0.0   0.90 13.79    NA m1 33.1   2.4   4.5   0.09  9.99 10.31 m2 42.3   9.2  13.7   0.00 14.75  8.33  > precis(m3) Dr. Quan-Hoang Vuong (v19-13) ©2018 SSHPA ∫∫ Vuong & Associates 2         Mean StdDev   5.5% 94.5% a     -1.71   6.60 -12.26  8.84 bfreq -2.01   6.59 -12.54  8.53 bcap   7.43   3.58   1.71 13.16  > logistic(-1.71) [1] 0.1531637 > logistic(-1.71+7.43) [1] 0.996731  > compare(m3b,m3b.assoc,m3c)           WAIC pWAIC dWAIC weight    SE   dSE m3b       15.3   1.9   0.0   0.61  7.86    NA m3b.assoc 16.2   2.3   0.9   0.39  7.80  0.45 m3c       30.5   9.4  15.1   0.00 14.44 10.58  > plot(compare(m3b,m3b.assoc,m3c))    > precis(m3b.assoc)        Mean StdDev  5.5% 94.5% a     -3.50   0.85 -4.85 -2.14 bfreq  1.85   3.34 -3.49  7.19 bcap   5.32   2.42  1.46  9.18  # HMC Stan m3b.assoc.stan <- map2stan(m3b.assoc, data=bmdat, iter=10000, warmup=1000) # check Gaussian pairs(m3b.assoc.stan) Dr. Quan-Hoang Vuong (v19-13) ©2018 SSHPA ∫∫ Vuong & Associates 3   > precis(m3b.assoc.stan)        Mean StdDev lower 0.89 upper 0.89 n_eff Rhat a     -3.78   0.94      -5.24      -2.31  4728    1 bfreq  3.02   3.89      -2.91       9.54  4114    1 bcap   7.11   2.98       2.36      11.77  3652    1 > post <- extract.samples(m3b.assoc.stan) > str(post) List of 3  $ a    : num [1:9000(1d)] -4.42 -4.6 -2.95 -3.94 -2.11 ...  $ bfreq: num [1:9000(1d)] 0.298 4.228 4.957 5.496 3.666 ...  $ bcap : num [1:9000(1d)] 6.41 5.34 3.91 3.98 3.93 ... > dens(post$a)   Dr. Quan-Hoang Vuong (v19-13) ©2018 SSHPA ∫∫ Vuong & Associates 4  > dens(post$bcap)   # MCMC Stan for m3b model > m3b.stan <- map2stan(m3b, data=bmdat, iter=10000, warmup=1000) > precis(m3b.stan)        Mean StdDev lower 0.89 upper 0.89 n_eff Rhat a     -1.33   3.21      -6.24       4.01  2489    1 bfreq -2.55   3.20      -7.88       2.29  2480    1 bcap   7.78   2.61       3.55      11.61  2511    1  > pairs(m3b.stan)   > post2 <- extract.samples(m3b.stan) > dens(post2$a) Dr. Quan-Hoang Vuong (v19-13) ©2018 SSHPA ∫∫ Vuong & Associates 5    > dens(post$bfreq)    Stan MCMC performs quite well.   References  McElreath R. (2018). Statistical Rethinking: A Bayesian Course with Examples in R and Stan. New York: Chapman and Hall/CRC.  Vuong QH. (2018). The (ir)rational consideration of the cost of science in transition economies. Nature Human Behaviour, 2(1), 5.  Vuong QH, La VP, Vuong TT,... & Ho MT. (2018). Cultural additivity: Behavioural insights from the interaction of Confucianism, Buddhism, and Taoism in folktales. Palgrave Communications, 4(1), 143.  

R Code for Preliminary Checks (Bayesian Method)

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R Codes for Preliminary Checks (Bayesian Method)

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