Documentation quick fixes

arrays.qmd

@@ -20,28 +20,35 @@ library(dust2)
 Let's take the simple stochastic SIR model from @sec-stochastic-sir and add age structure to it by having two groups (children and adults), with heterogeneous mixing between the groups.
 
 ```{r}
-sir_2 <- odin({
+sir <- odin({
+
   # Equations for transitions between compartments by age group
   update(S[]) <- S[i] - n_SI[i]
   update(I[]) <- I[i] + n_SI[i] - n_IR[i]
   update(R[]) <- R[i] + n_IR[i]
 
   # Individual probabilities of transition:
+
   p_SI[] <- 1 - exp(-lambda[i] * dt) # S to I
   p_IR <- 1 - exp(-gamma * dt) # I to R
 
   # Calculate force of infection
-  ## age-structured contact matrix: m[i, j] is mean number of contacts an
-  ## individual in group i has with an individual in group j per time unit
+
+  # age-structured contact matrix: m[i, j] is mean number of contacts an
+  # individual in group i has with an individual in group j per time unit
+
   m <- parameter()
-  ## here s_ij[i, j] gives the mean number of contacts and individual in group
-  ## i will have with the currently infectious individuals of group j
+
+  # here s_ij[i, j] gives the mean number of contacts and individual in group
+  # i will have with the currently infectious individuals of group j
   s_ij[, ] <- m[i, j] * I[j]
-  ## lambda[i] is the total force of infection on an individual in group i 
+
+  # lambda[i] is the total force of infection on an individual in group i 
   lambda[] <- beta * (s_ij[i, 1] + s_ij[i, 2])
 
   # Draws from binomial distributions for numbers changing between
   # compartments:
+
   n_SI[] <- Binomial(S[i], p_SI[i])
   n_IR[] <- Binomial(I[i], p_IR)
 
@@ -96,12 +103,12 @@ dim(S) <- 2
 ```
 or can be generalised so that the dimensions themselves become parameters:
 ```r
-dim(S) <- N_age
-N_age <- parameter()
+dim(S) <- n_age
+n_age <- parameter()
 ```
 You may have array parameters, these can have dimensions explicitly declared
 ```r
-dim(m) <- c(N_age, N_age)
+dim(m) <- c(n_age, n_age)
 ```
 or they can also have their dimensions detected when the parameters are passed into the system - this still needs a `dim` equation where you must explicitly state the rank:
 ```r
@@ -146,7 +153,7 @@ The same syntax applies to other functions that reduce arrays: `min`, `max` and
 
 ## Example: a generalised age structured SIR model {#sec-stochastic-age}
 
-Let's now take the two group SIR model and generalise it to `N_age` age groups
+Let's now take the two group SIR model and generalise it to `n_age` age groups
 
 ```{r}
 sir_age <- odin({
@@ -185,18 +192,18 @@ sir_age <- odin({
   gamma <- parameter(0.1)
 
   # Dimensions of arrays
-  N_age <- parameter()
-  dim(S0) <- N_age
-  dim(I0) <- N_age
-  dim(S) <- N_age
-  dim(I) <- N_age
-  dim(R) <- N_age
-  dim(n_SI) <- N_age
-  dim(n_IR) <- N_age
-  dim(p_SI) <- N_age
-  dim(m) <- c(N_age, N_age)
-  dim(s_ij) <- c(N_age, N_age)
-  dim(lambda) <- N_age
+  n_age <- parameter()
+  dim(S0) <- n_age
+  dim(I0) <- n_age
+  dim(S) <- n_age
+  dim(I) <- n_age
+  dim(R) <- n_age
+  dim(n_SI) <- n_age
+  dim(n_IR) <- n_age
+  dim(p_SI) <- n_age
+  dim(m) <- c(n_age, n_age)
+  dim(s_ij) <- c(n_age, n_age)
+  dim(lambda) <- n_age
 })
 ```
 
@@ -216,7 +223,7 @@ We are aiming to support matrix multiplication in future to help simplify this c
 
 ## Indexing
 
-We have seen in the above examples uses of 1d and 2d arrays, making use of index variables `i` and `j`. Note that while we never use these index variables on the LHS, it is implicit that on the LHS we are indexing by `i` for 1d arrays and `i` and `j` for 2d arrays! Use of these index variables on the RHS corresponds to the indexing on the LHS.
+We have seen in the above examples uses of 1-dimensions and 2-dimensional arrays, making use of index variables `i` and `j`. Note that while we never use these index variables on the LHS, it is implicit that on the LHS we are indexing by `i` for 1-dimensional arrays and `i` and `j` for 2-dimensional arrays! Use of these index variables on the RHS corresponds to the indexing on the LHS.
 
 Of course you may want to use higher-dimensional arrays! We currently supporting up to 8 dimensions, with index variables `i`, `j`, `k`, `l`, `i5`, `i6`, `i7` and `i8`.
 
@@ -256,8 +263,8 @@ sir_age_vax <- odin({
   # Nested binomial draw for vaccination in S
   # Assume you cannot move vaccine class and get infected in same step
   n_S_vax[, ] <- Binomial(S[i, j] - n_SI[i, j], p_vax[i, j])
-  new_S[, 1] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, N_vax]
-  new_S[, 2:N_vax] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, j - 1]
+  new_S[, 1] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, n_vax]
+  new_S[, 2:n_vax] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, j - 1]
 
   initial(S[, ]) <- S0[i, j]
   initial(I[, ]) <- I0[i, j]
@@ -272,42 +279,42 @@ sir_age_vax <- odin({
   rel_susceptibility <- parameter()
 
   # Dimensions of arrays
-  N_age <- parameter()
-  N_vax <- parameter()
-  dim(S0) <- c(N_age, N_vax)
-  dim(I0) <- c(N_age, N_vax)
-  dim(S) <- c(N_age, N_vax)
-  dim(I) <- c(N_age, N_vax)
-  dim(R) <- c(N_age, N_vax)
-  dim(n_SI) <- c(N_age, N_vax)
-  dim(n_IR) <- c(N_age, N_vax)
-  dim(p_SI) <- c(N_age, N_vax)
-  dim(m) <- c(N_age, N_age)
-  dim(s_ij) <- c(N_age, N_age)
-  dim(lambda) <- N_age
-  dim(eta) <- c(N_age, N_vax)
-  dim(rel_susceptibility) <- c(N_vax)
-  dim(p_vax) <- c(N_age, N_vax)
-  dim(n_S_vax) <- c(N_age, N_vax)
-  dim(new_S) <- c(N_age, N_vax)
+  n_age <- parameter()
+  n_vax <- parameter()
+  dim(S0) <- c(n_age, n_vax)
+  dim(I0) <- c(n_age, n_vax)
+  dim(S) <- c(n_age, n_vax)
+  dim(I) <- c(n_age, n_vax)
+  dim(R) <- c(n_age, n_vax)
+  dim(n_SI) <- c(n_age, n_vax)
+  dim(n_IR) <- c(n_age, n_vax)
+  dim(p_SI) <- c(n_age, n_vax)
+  dim(m) <- c(n_age, n_age)
+  dim(s_ij) <- c(n_age, n_age)
+  dim(lambda) <- n_age
+  dim(eta) <- c(n_age, n_vax)
+  dim(rel_susceptibility) <- c(n_vax)
+  dim(p_vax) <- c(n_age, n_vax)
+  dim(n_S_vax) <- c(n_age, n_vax)
+  dim(new_S) <- c(n_age, n_vax)
 })
 ```
 
 We see we can use multiple lines to deal with boundary conditions:
 ```r
-new_S[, 1] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, N_vax]
-new_S[, 2:N_vax] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, j - 1]
+new_S[, 1] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, n_vax]
+new_S[, 2:n_vax] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] + n_S_vax[i, j - 1]
 ```
 which we could also write as
 ```r
 new_S[, ] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j]
-new_S[, 1] <- new_S[i, j] + n_S_vax[i, N_vax]
-new_S[, 2:N_vax] <- new_S[i, j] + n_S_vax[i, j - 1]
+new_S[, 1] <- new_S[i, j] + n_S_vax[i, n_vax]
+new_S[, 2:n_vax] <- new_S[i, j] + n_S_vax[i, j - 1]
 ```
 or another alternative way of writing this would be to use `if else`
 ```r
 new_S[, ] <- S[i, j] - n_SI[i, j] - n_S_vax[i, j] +
-    (if (j == 1) n_S_vax[i, N_vax] else n_S_vax[i, j - 1])
+    (if (j == 1) n_S_vax[i, n_vax] else n_S_vax[i, j - 1])
 ```
 Note that in odin, an `if` always requires an `else`!
 
@@ -341,8 +348,8 @@ n_IR[, ] <- Binomial(I[i, j], p_IR)
 update(I[, ]) <- I[i, j] + n_SI[i, j] - n_IR[i, j]
 update(R[, ]) <- R[i, j] + n_IR[i, j]
 
-dim(I) <- c(N_age, N_vax)
-dim(n_IR) <- c(N_age, N_vax)
+dim(I) <- c(n_age, n_vax)
+dim(n_IR) <- c(n_age, n_vax)
 
 ```
 
@@ -356,14 +363,14 @@ n_I_progress[, , ] <- Binomial(I[i, j, k], p_I_progress)
 update(I[, , ]) <- I[i, j, k] - n_I_progress[i, j, k] +
   (if (k == 1) n_SI[i, j] else n_I_progress[i, j, k - 1])
 update(R[, ]) <- R[i, j] + n_I_progress[i, j, k_I]
-dim(I) <- c(N_age, N_vax, k_I)
-dim(n_I_progress) <- c(N_age, N_vax, k_I)
+dim(I) <- c(n_age, n_vax, k_I)
+dim(n_I_progress) <- c(n_age, n_vax, k_I)
 ```
 
 and there would be some other bits we'd need to change to deal with the increased dimensionality of `I`:
 
 ```r
 s_ij[, ] <- m[i, j] * sum(I[j, ,])
 initial(I[, , ]) <- I0[i, j, k]
-dim(I0) <- c(N_age, N_vax, k_I)
+dim(I0) <- c(n_age, n_vax, k_I)
 ```

interpolation.qmd

@@ -260,7 +260,7 @@ sis <- odin({
 })
 ```
 
-When we create the system, we must pass in values for the components of `school`; there are no defaults for vector parameters.
+When we create the system, we must pass in values for the components of `school`; there are no defaults for vector parameters. The `constant = TRUE` argument when declaring the parameters specifies that these parameters cannot be changed after being set; this is necessary if we wanted to use these parameters to index into an array later, but moreover makes our intentions clear for the purposes of these parameters.
 
 ```{r}
 pars <- list(schools_time = schools_time, schools_open = schools_open)

packaging.qmd

@@ -42,7 +42,7 @@ usethis::create_package(
   path,
   fields = list(Package = "pkg",
                 Title = "My Odin Model",
-                Description = ""))
+                Description = "An example odin model"))
 usethis::proj_set(path)
 ```