Move grid behind lines

ch08.r

@@ -43,9 +43,9 @@ L_df = data.frame(L)
 colnames(L_df) = S
 
 plot(theta, L_df$"12", type="n")
+grid()
 lines(theta, L_df$"12", lwd=6)
 title(expression(paste("L(", theta, " | S = 12)")))
-grid()
 
 ## ML estimation for single-photon imaging
 
@@ -83,20 +83,20 @@ theta = seq(0.1, 0.9, (0.1+0.9)/1000)
 L = S * log(theta) + (N-S) * log(1-theta)
 plot(theta, L, type="n", xlab=expression(theta), ylab=expression(paste("Log L(", theta, "|S = 20)")))
 title("Bernoulli")
-lines(theta, L, lwd=6, col="#8080BF")
 grid()
+lines(theta, L, lwd=6, col="#8080BF")
 
 h_theta = -log(1-theta)
 plot(theta, h_theta, type="n", xlab=expression(theta), ylab=expression(paste(eta, " = h(", theta, ")")))
-lines(theta, h_theta, lwd=6)
 grid()
+lines(theta, h_theta, lwd=6)
 
 theta = seq(0.1, 2.5, (0.1+2.5)/1000)
 L = S * log(1-exp(-theta)) - theta * (N-S)
 plot(theta, L, type="n")
 title("Truncated Poisson")
-lines(theta, L, lwd=6, col="#0000BF")
 grid()
+lines(theta, L, lwd=6, col="#0000BF")
 
 #############
 # Chapter 8.3 Maximum-a-Posteriori Estimation
@@ -127,11 +127,11 @@ prior = dnorm(t, mu0, sigma0)/10
 plot(t, p1, type="n")
 title("N = 5")
 legend(-2, 0.9, legend=c("Likelihood", "Posterior", "Prior", "Data"), col=c("blue", "orange", "green", "purple"), lty=1:1)
+grid()
 lines(t, p0, lwd = 4, col="blue")
 lines(t, p1, lwd = 4, col="orange")
 lines(t, prior, lwd = 4, col="green")
 lines(t, q, lwd = 4, col="purple")
-grid()
 
 ## Conjugate priors
 
@@ -158,6 +158,7 @@ for (i in 1:7) {
 }
 
 plot(t, posterior[[7]], type="n", xlab="", ylab="")
+grid()
 lines(t, posterior[[1]], lwd=3, col="red")
 lines(t, posterior[[2]], lwd=3, col="orange")
 lines(t, posterior[[3]], lwd=3, col="yellow")
@@ -167,6 +168,5 @@ lines(t, posterior[[6]], lwd=3, col="blue")
 lines(t, posterior[[7]], lwd=3, col="purple")
 
 legend(-0.9, 7, legend=c("N = 0", "N = 1", "N = 2", "N = 5", "N = 8", "N = 12", "N = 20"), col=c("red", "orange", "yellow", "green", "turquoise", "blue", "purple"), lty=1:1, lwd=3)
-grid()
 
 #############