change Figure 1

LeoEgidi · Jun 12, 2024 · eeec6ae · eeec6ae
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diff --git a/paper/fish_hist.pdf b/paper/fish_hist.pdf
diff --git a/paper/fish_hist.png b/paper/fish_hist.png
diff --git a/paper/paper.md b/paper/paper.md
@@ -142,7 +142,7 @@ cat(res_stan$model)
 ```
 
 
-![Histograms of the Fishery data. The blue line represents the estimated kernel density. \label{fig:example1}](fish_hist.png)
+![Histograms of the Fishery data. The blue line represents the estimated kernel density. \label{fig:example1}](fish_hist.pdf){width=70%}
 
 
 \autoref{fig:example2} displays the traceplots for the parameters $(\mu, \sigma, \eta)$. From the first row 
@@ -151,7 +151,7 @@ and reorder the means $\mu_j$ and the weights $\eta_j$, for $j=1,\ldots,k$, as e
 
 ![Fishery dataset: traceplots of the parameters $(\mu, \sigma, \eta)$ obtained via the `rjags` option for the
  `piv_MCMC` function (Gibbs sampling, 15000 MCMC iterations). Top row: Raw MCMC outputs. 
-Bottom row: relabelled MCMC samples. \label{fig:example2}](fish_chains.pdf){width=70%}
+Bottom row: relabelled MCMC samples. \label{fig:example2}](fish_chains.pdf){width=80%}
 
 
 

diff --git a/paper/rcode/reproducible_code.R b/paper/rcode/reproducible_code.R
@@ -15,6 +15,16 @@ y <- fish[,1]
 k <- 5
 nMC <- 15000
 
+## Figure 1
+
+pdf(file="fishery_hist.pdf", width=10, height=6.5)
+par( mar=c(5,4,2,1))
+hist(y, breaks=40, prob = TRUE, cex.lab=1.6,
+     main ="Fishery data", cex.main =1.7,
+     col="navajowhite1", border="navajowhite1")
+lines(density(y), lty=1, lwd=3, col="blue")
+dev.off()
+
 #fit the mixture model for univariate data and find the pivots
 res <- piv_MCMC(y = y, k = k, nMC = nMC, burn = 0.5*nMC, software = "rjags")