Merge branch 'master' of https://github.com/andrewcparnell/simms_course

aj-content/practicals/siber-comparing-populations.Rmd

@@ -267,7 +267,23 @@ bayes.overlap.G2.G3 <- bayesianOverlap("1.3", "1.4", ellipses.posterior,
                                        n = 360)
 print(bayes.overlap.G2.G3)
 
+
+```
+
+And summarise the credible intervals of the Bayesian overlap output. Note that this code does not work well on the small number of posterior draws we are using for this basic example - for one it returns negative values which is not possible, but is arising as the smoother has not got enough information to stay close to or within the positive number range.
+
+```{r}
+# and we can calculate the corresponding credible intervals using
+# our code from above again
+# call to hdrcde:hdr using lapply()
+overlap.credibles <- lapply(
+  as.data.frame(bayes.overlap.G2.G3), 
+  function(x,...){tmp<-hdrcde::hdr(x)$hdr},
+  prob = cr.p)
+
+print(overlap.credibles)
 ```
 
 
 
+

aj-content/practicals/source-aggregation.Rmd

@@ -10,9 +10,9 @@ knitr::opts_chunk$set(echo = TRUE)
 ```
 
 ## Create some simulated data for us to work with
-This is essentially the example used in Fry, B. 2013. Alternative approaches for solving underdetermined isotope mixing problems. *MEPS*.
+This is essentially the example used in Fry, B. 2013. Alternative approaches for solving under-determined isotope mixing problems. *MEPS*.
 
-Here we use the package simmr to explore the two alternatives to aggregating sources in mixing models. 
+Here we use the package `simmr` to explore the two alternatives to aggregating sources in mixing models. 
 
 ```{r simulate-data}
 library(simmr, quietly = TRUE)
@@ -38,8 +38,8 @@ S_sds = cbind(c(1,1,1,1), c(1,1,1,1))
 
 # speficy the consumer data at the origin
 # Ten consumers for this example around 0 with small sd of error.
-consumers <- cbind(dC = rnorm(10, 0, 0.1),
-                   dN = rnorm(10, 0, 0.1) )
+consumers <- cbind(dC = rnorm(n = 10, mean = 0, sd = 0.1),
+                   dN = rnorm(n = 10, mean = 0, sd = 0.1) )
 
 # and create the simmr object
 # here we have no TDFs or concentration values

aj-content/siar-dos-and-donts.Rmd

@@ -88,6 +88,7 @@ p <- c(0.2, 0.4, 0.4)
 
 consumer <- p %*% sources
 
+
 plot(sources[,1], sources[,2], pch = c(15,15,15), col = c(1,6,4),
      xlab = "", ylab = "", bty="L", cex = 3, xlim = c(-12,6),
      ylim = c(-5, 10))