minor changes to plots

Author: gregorkastner

vignettes/Chapter07.Rmd

@@ -44,10 +44,10 @@ Now we can plot the data and its empirical autocorrelation function.
 
 ```{r, echo = -c(1:2)}
 if (pdfplots) {
-  pdf("7-2_1.pdf", width = 8, height = 5)
-  par(mar = c(2.5, 1.5, 1.5, .1), mgp = c(1.5, .5, 0), lwd = 2)
+  pdf("7-2_1.pdf", width = 12, height = 5)
+  par(mar = c(2.6, 1.5, 1.5, .1), mgp = c(1.5, .5, 0), lwd = 1.5)
 }
-par(mfrow = c(1,2))
+par(mfrow = c(1, 2))
 ts.plot(logret, main = "U.S. GDP log returns")
 acf(logret, lag = 8, main = "")
 title("Empirical autocorrelation function")
@@ -184,7 +184,7 @@ corresponding to the highest lag in each of the models.
 if (pdfplots) {
   pdf("7-2_2.pdf", width = 8, height = 5)
 }
-par(mfrow = c(2, 2), mar = c(2.5, 1.5, 1.5, .1), mgp = c(1.5, .5, 0))
+par(mfrow = c(2, 2), mar = c(2.5, 1.5, 1.5, .1), mgp = c(1.5, .5, 0), lwd = 1.5)
 for (p in 1:4) {
   hist(res[[p]]$betas[, p + 1], freq = FALSE, main = bquote(AR(.(p))),
        xlab = bquote(phi[.(p)]), ylab = "", breaks = seq(-.75, .75, .02))
@@ -222,7 +222,7 @@ We now visualize the posterior of the intercept under all those priors.
 
 ```{r, echo = -c(1:2)}
 if (pdfplots) {
-  pdf("7-2_3.pdf", width = 8, height = 5)
+  pdf("7-2_3.pdf", width = 12, height = 5, lwd = 1.5)
 }
 par(mfrow = c(1, 2), mar = c(2.5, 1.5, 1.5, .1), mgp = c(1.5, .5, 0))
 dens_improper <- density(res_improper$betas[, 1], bw = "SJ", adj = 2)
@@ -254,9 +254,9 @@ variance.
 
 ```{r, echo = -c(1:2)}
 if (pdfplots) {
-  pdf("7-2_4.pdf", width = 8, height = 5)
+  pdf("7-2_4.pdf", width = 9, height = 5)
 }
-par(mfrow = c(4, 2), mar = c(2.5, 1.5, 1.5, .1), mgp = c(1.5, .5, 0))
+par(mfrow = c(4, 2), mar = c(2.5, 1.5, 1.5, .1), mgp = c(1.5, .5, 0), lwd = 1.5)
 for (p in 1:3) {
   dens_improper <- density(res_improper$betas[, p + 1], bw = "SJ", adj = 2)
   dens_semi_1 <- density(res_semi_1$betas[, p + 1], bw = "SJ", adj = 2)
@@ -317,7 +317,7 @@ stationarity for $p = 1, 2, 3$.
 if (pdfplots) {
   pdf("7-2_5.pdf", width = 9, height = 3)
 }
-par(mfrow = c(1, 3), mar = c(2.5, 2.5, 1.2, .1), mgp = c(1.5, .5, 0))
+par(mfrow = c(1, 3), mar = c(2.5, 2.5, 1.2, .1), mgp = c(1.5, .5, 0), lwd = 1.5)
 hist(res[[1]]$betas[,2], breaks = 20, freq = FALSE, main = "AR(1)",
      xlab = bquote(phi), ylab = "")
 
@@ -349,8 +349,8 @@ First, we plot the data and its empirical autocorrelation function.
 
 ```{r, echo = -c(1:2)}
 if (pdfplots) {
-  pdf("7-2_6.pdf", width = 8, height = 5)
-  par(mar = c(2.5, 1.5, 1.5, .1), mgp = c(1.5, .5, 0))
+  pdf("7-2_6.pdf", width = 12, height = 5)
+  par(mar = c(2.6, 1.5, 1.5, .1), mgp = c(1.5, .5, 0), lwd = 1.5)
 }
 par(mfrow = c(1,2))
 ts.plot(inflation, main = "EU Inflation")
@@ -376,7 +376,7 @@ for (p in 1:3) {
 if (pdfplots) {
   pdf("7-2_7.pdf", width = 9, height = 3)
 }
-par(mfrow = c(1, 3), mar = c(2.5, 2.5, 1.2, .1), mgp = c(1.5, .5, 0))
+par(mfrow = c(1, 3), mar = c(2.5, 2.5, 1.2, .1), mgp = c(1.5, .5, 0), lwd = 1.5)
 hist(res[[1]]$betas[,2], breaks = 20, freq = FALSE, main = "AR(1)",
      xlab = bquote(phi), ylab = "")
 abline(v = 1, col = 2)