Update Optimalgo.Rmd

vignettes/Optimalgo.Rmd

@@ -200,7 +200,7 @@ grlnlbeta <- fitdistrplus:::grlnlbeta
 n <- 200
 x <- rbeta(n, 3, 3/4)
 grlnlbeta(c(3, 4), x) #test
-hist(x, prob=TRUE)
+hist(x, prob=TRUE, xlim=0:1)
 lines(density(x), col="red")
 curve(dbeta(x, 3, 3/4), col="green", add=TRUE)
 legend("topleft", lty=1, col=c("red","green"), legend=c("empirical", "theoretical"), bty="n")
@@ -275,9 +275,9 @@ kable(expopt[, grep("G-", colnames(expopt))], digits=3,
 
 Using `llsurface`, we plot the log-likehood surface around the true value (green) and the fitted parameters (red).
 ```{r, fig.width=4, fig.height=4}
-llsurface(min.arg=c(0.1, 0.1), max.arg=c(7, 3), 
+llsurface(min.arg=c(0.1, 0.1), max.arg=c(7, 3), xlim=c(.1,7), 
           plot.arg=c("shape1", "shape2"), nlev=25,
-          plot.np=50, data=x, distr="beta", back.col = FALSE)
+          lseq=50, data=x, distr="beta", back.col = FALSE)
 points(unconstropt[1,"BFGS"], unconstropt[2,"BFGS"], pch="+", col="red")
 points(3, 3/4, pch="x", col="green")
 ```
@@ -353,7 +353,7 @@ n <- 200
 trueval <- c("size"=10, "prob"=3/4, "mu"=10/3)
 x <- rnbinom(n, trueval["size"], trueval["prob"])
 
-hist(x, prob=TRUE, ylim=c(0, .3))
+hist(x, prob=TRUE, ylim=c(0, .3), xlim=c(0, 10))
 lines(density(x), col="red")
 points(min(x):max(x), dnbinom(min(x):max(x), trueval["size"], trueval["prob"]), 
        col = "green")
@@ -432,9 +432,9 @@ kable(expopt[, grep("G-", colnames(expopt))], digits=3,
 
 Using `llsurface`, we plot the log-likehood surface around the true value (green) and the fitted parameters (red).
 ```{r, fig.width=4, fig.height=4}
-llsurface(min.arg = c(5, 0.3), max.arg = c(15, 1), 
+llsurface(min.arg = c(5, 0.3), max.arg = c(15, 1), xlim=c(5, 15),
           plot.arg = c("size", "prob"), nlev = 25,
-          plot.np = 50, data = x, distr = "nbinom", back.col = FALSE)
+          lseq = 50, data = x, distr = "nbinom", back.col = FALSE)
 points(unconstropt["fitted size", "BFGS"], unconstropt["fitted prob", "BFGS"], 
        pch = "+", col = "red")
 points(trueval["size"], trueval["prob"], pch = "x", col = "green")
@@ -445,7 +445,7 @@ We can simulate bootstrap replicates using the `bootdist` function.
 b1 <- bootdist(fitdist(x, "nbinom", method = "mle", optim.method = "BFGS"), 
                niter = 100, parallel = "snow", ncpus = 2)
 summary(b1)
-plot(b1, trueval=trueval)
+plot(b1, trueval=trueval) 
 ```