some minor fixes to articles

poissonconsulting · May 1, 2024 · 349130d · 349130d
1 parent 20b0cc1
commit 349130d
Show file tree

Hide file tree

Showing 2 changed files with 3 additions and 3 deletions.
diff --git a/vignettes/articles/methods.Rmd b/vignettes/articles/methods.Rmd
@@ -199,7 +199,7 @@ sAnnual ~  Exponential(1)
 for(i in 1:nAnnual)  bAnnual[i] ~ Normal(0, sAnnual)
 
 for(i in 1:nAnnual) {
-  FemaleYearlings[i] ~ Binomial(0.5, Yearlings[i])
+  FemaleYearlings[i] ~ Binomial(sex_ratio, Yearlings[i])
   Cows[i] ~ Binomial(adult_female_proportion, CowsBulls[i])
   OtherAdultsFemales[i] ~ Binomial(adult_female_proportion, UnknownAdults[i])
   log(eRecruitment[i]) <- b0 + bAnnual[Annual[i]] + bYear * Year[i]
@@ -222,7 +222,7 @@ for(i in 1:nAnnual)  bAnnual[i] ~ Normal(0, sAnnual)
 
 for(i in 1:nAnnual) {
   Cows[i] ~ Binomial(adult_female_proportion, CowsBulls[i])
-  FemaleYearlings[i] <- round(0.5 * Yearlings[i])
+  FemaleYearlings[i] <- round(sex_ratio * Yearlings[i])
   OtherAdultsFemales[i] <- round(adult_female_proportion * UnknownAdults[i])
   logit(eRecruitment[i]) <- b0 + bAnnual[Annual[i]] + bYear * Year[i]
   AdultsFemales[i] <- max(FemaleYearlings[i] + Cows[i] + OtherAdultsFemales[i], 1)

diff --git a/vignettes/articles/priors.Rmd b/vignettes/articles/priors.Rmd
@@ -136,7 +136,7 @@ ggplot(data = df) +
   scale_color_discrete("")
 ```
 
-The Bayesian model with vague priors (default) has annual survival estimates that closely match the Maximum Likelihood estimates, whereas the the Bayesian model with a tighter prior on the year fixed effect standard deviation results in estimates that are pulled in toward the mean.
+The Bayesian model with vague priors (default) has annual survival estimates that closely match the Maximum Likelihood estimates, whereas the Bayesian model with a tighter prior on the year fixed effect standard deviation results in estimates that are pulled in toward the mean.
 If this behaviour is desirable (i.e., to place less trust in extreme estimates), a model with a year random effect can be fit.
 
 As another example, the user might strongly believe (i.e., based on literature or biological understanding) that the true adult female proportion is 65%.