switch nlg models in tests

tests/testthat/test_post_correct.R

@@ -46,22 +46,39 @@ test_that("Test post correction for AR1 model", {
 test_that("Test post correction for non-linear model", {
    skip_on_cran()
    set.seed(1)
-   n <- 10
-   x <- y <- numeric(n)
-   y[1] <- rnorm(1, exp(x[1]), 0.1)
-   for(i in 1:(n-1)) {
-      x[i+1] <- rnorm(1, sin(x[i]), 0.1)
-      y[i+1] <- rnorm(1, exp(x[i+1]), 0.1)
-   }
-   y[2:3] <- NA
-   pntrs <- cpp_example_model("nlg_sin_exp")
 
-   expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, 
+   p1 <- 50 # population size at t = 1
+   K <- 500 # carrying capacity
+   H <- 1 # standard deviation of obs noise
+   R_1 <- 0.05 # standard deviation of the noise on logit-growth
+   R_2 <- 1 # standard deviation of the noise in population level
+   #sample time
+   dT <- .1
+
+   #observation times
+   t <- seq(0.1, 10, dT)
+   n <- length(t)
+   r <- plogis(cumsum(c(-1.5, rnorm(n - 1, sd = R_1))))
+   p <- numeric(n)
+   p[1] <- p1
+   for(i in 2:n)
+      p[i] <- rnorm(1, K * p[i-1] * exp(r[i-1] * dT) / (K + p[i-1] * (exp(r[i-1] * dT) - 1)), R_2)
+   # observations
+   y <- p + rnorm(n, 0, H)
+   y[2:15] <- NA
+   pntrs <- cpp_example_model("nlg_growth")
+
+   initial_theta <- c(log_H = 0, log_R1 = log(0.05), log_R2 = 0)
+
+   # dT, K, a1 and the prior variances of first and second state (logit r and and p)
+   known_params <- c(dT = dT, K = K, a11 = -1, a12 = 50, P11 = 1, P12 = 100)
+
+   expect_error(model <- ssm_nlg(y = y, a1=pntrs$a1, P1 = pntrs$P1, 
       Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, 
       Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn,
-      theta = c(log_H = log(0.1), log_R = log(0.1)), 
-      log_prior_pdf = pntrs$log_prior_pdf,
-      n_states = 1, n_etas = 1, state_names = "state"), NA)
+      theta = initial_theta, log_prior_pdf = pntrs$log_prior_pdf,
+      known_params = known_params, known_tv_params = matrix(1),
+      n_states = 2, n_etas = 2, state_names = c("logit_r", "p")), NA)
 
 
    expect_error(out_approx <- run_mcmc(model, mcmc_type = "approx", 
@@ -80,5 +97,4 @@ test_that("Test post correction for non-linear model", {
    expect_lt(sum(out_is2$Vt), Inf)
    expect_lt(max(out_is2$weights), Inf)
    expect_gt(max(out_is2$weights), 0)
-
 })

tests/testthat/test_predict.R

@@ -149,16 +149,16 @@ test_that("Predictions for nlg_ssm work", {
   x <- y <- numeric(n)
   y[1] <- rnorm(1, exp(x[1]), 0.1)
   for(i in 1:(n-1)) {
-    x[i+1] <- rnorm(1, sin(x[i]), 0.1)
+    x[i+1] <- rnorm(1, 0.9 * x[i], 0.1)
     y[i+1] <- rnorm(1, exp(x[i+1]), 0.1)
   }
 
-  pntrs <- cpp_example_model("nlg_sin_exp")
+  pntrs <- cpp_example_model("nlg_ar_exp")
 
   expect_error(model <- ssm_nlg(y = y, a1 = pntrs$a1, P1 = pntrs$P1, 
     Z = pntrs$Z_fn, H = pntrs$H_fn, T = pntrs$T_fn, R = pntrs$R_fn, 
     Z_gn = pntrs$Z_gn, T_gn = pntrs$T_gn,
-    theta = c(log_H = log(0.1), log_R = log(0.1)), 
+    theta = c(mu = 0, rho = 0.9, log_R = log(0.1), log_H = log(0.1)), 
     log_prior_pdf = pntrs$log_prior_pdf,
     n_states = 1, n_etas = 1, state_names = "state"), NA)