updated chapter 84

winterwang · Jan 8, 2020 · 58f0532 · 58f0532
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diff --git a/12-Bayesian-stats.Rmd b/12-Bayesian-stats.Rmd
@@ -2879,7 +2879,17 @@ gelman.plot(Simulated)
 
 
 
-```{r OpenBUGSPractical04110000, cache=TRUE, message=FALSE, warning=FALSE, echo=FALSE}
+```{r OpenBUGSPractical04110000, cache=FALSE, message=FALSE, warning=FALSE, echo=TRUE, eval=FALSE}
+Dat <- list(
+     y = c(1, 3, 6, 8, 11, 15, 17, 19), 
+     n = c(20, 20, 20, 20, 20, 20, 20, 20), 
+     x = c(30, 32, 34, 36, 38, 40, 42, 44), 
+     N = 8 
+)
+inits <- function (){
+  list (beta0=rnorm(1), beta1=runif(1) )
+}
+
 post.jags <- jags(
   data = Dat,
   inits = inits,
@@ -2951,7 +2961,33 @@ print(post.jags)
 # samplesHistory("beta1", mfrow = c(1,1), ask = FALSE)
 ```
 
+```
+Compiling model graph
+   Resolving undeclared variables
+   Allocating nodes
+Graph information:
+   Observed stochastic nodes: 8
+   Unobserved stochastic nodes: 2
+   Total graph size: 53
+
+Initializing model
 
+Inference for Bugs model at "/home/ccwang/Documents/LSHTMlearningnote/backupfiles/logistic-reg-model.txt", fit using jags,
+ 2 chains, each with 502000 iterations (first 2000 discarded)
+ n.sims = 1000000 iterations saved
+         mu.vect sd.vect    2.5%     25%     50%     75%   97.5%  Rhat n.eff
+beta0    -13.892   2.090 -18.257 -15.237 -13.788 -12.449 -10.042 1.001  3900
+beta1      0.375   0.056   0.272   0.337   0.373   0.412   0.493 1.001  3100
+theta[6]   0.752   0.048   0.652   0.720   0.754   0.786   0.841 1.001 10000
+deviance  25.722   2.020  23.760  24.283  25.095  26.499  31.199 1.001  9600
+
+For each parameter, n.eff is a crude measure of effective sample size,
+and Rhat is the potential scale reduction factor (at convergence, Rhat=1).
+
+DIC info (using the rule, pD = var(deviance)/2)
+pD = 2.0 and DIC = 27.8
+DIC is an estimate of expected predictive error (lower deviance is better).
+```
 
 
 <!-- ```{r OpenBUGSPractical0413, cache=TRUE, message=TRUE, warning=FALSE, echo=TRUE} -->
@@ -3285,7 +3321,7 @@ print(post.jags)
 
 所以，藥物每增加劑量1mg，有療效的比值比是OR = 1.46 (95%CrI: 1.31, 1.63)。能夠達到95%患者都有療效的劑量是 45.02 mg (95% CrI: 42.7, 48.2 mg)。如果給予患者藥物劑量爲 35 mg，患者的疼痛能夠得到緩解(有療效)的概率是 32.3% (95% CrI: 22.7%, 42.3%)。跟着看到這裏的你是不是也覺得貝葉斯的結果和過程能夠更加豐富地回答我們想知道的問題呢？ 
 
-```{r R-OpenBUGSPractical0425, cache=TRUE, fig.width=3.5, fig.height=3, fig.cap='Posterior density plots of ED95, OR, and P35.', fig.align='center', out.width='80%', message=TRUE, warning=FALSE}
+```{r R-OpenBUGSPractical0425, cache=TRUE, fig.width=3.5, fig.height=5, fig.cap='Posterior density plots of ED95, OR, and P35.', fig.align='center', out.width='80%', message=TRUE, warning=FALSE}
 #### PLOT THE DENSITY and HISTOGRAMS OF THE SAMPLED VALUES
 par(mfrow=c(3,1))
 Simulated <- coda::as.mcmc(post.jags)

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diff --git a/packages.bib b/packages.bib
@@ -396,6 +396,13 @@ @Manual{R-Matrix
   note = {R package version 1.2-18},
   url = {https://CRAN.R-project.org/package=Matrix},
 }
+@Manual{R-mcmcplots,
+  title = {mcmcplots: Create Plots from MCMC Output},
+  author = {S. McKay Curtis},
+  year = {2018},
+  note = {R package version 0.4.3},
+  url = {https://CRAN.R-project.org/package=mcmcplots},
+}
 @Manual{R-mstate,
   title = {mstate: Data Preparation, Estimation and Prediction in Multi-State
 Models},