increasing coverage

dev/dev.R

@@ -1,2 +1,8 @@
 # Development
 devtools::load_all("/Users/markolalovic/dev/latent2likert/")
+
+covr::codecov(
+  quiet = FALSE,
+  clean = FALSE,
+  token = Sys.getenv("CODECOV_TOKEN")
+)

tests/testthat/test_discretization.R

@@ -1,22 +1,21 @@
 context("Testing discretization")
 
 testthat::test_that("discretization of N(0,1) using 4 levels gives expected results", {
-    #print("TODO: test coverage")
-    res = discretize_density(density_fn = dnorm, n_levels = 4)
-    expected = list(
-        prob = c(0.16, 0.34, 0.34, 0.16), 
-        endp = c(-Inf, -0.98, 0, 0.98, Inf), 
-        repr = c(-1.51, -0.45, 0.45, 1.51), 
-        dist = 0.12)
-    testthat::expect_equal(res, expected, tolerance = 0.05)
+  res = discretize_density(density_fn = dnorm, n_levels = 4)
+  expected = list(
+      prob = c(0.16, 0.34, 0.34, 0.16), 
+      endp = c(-Inf, -0.98, 0, 0.98, Inf), 
+      repr = c(-1.51, -0.45, 0.45, 1.51), 
+      dist = 0.12)
+  testthat::expect_equal(res, expected, tolerance = 0.05)
 })
 
 testthat::test_that("discretization of N(0,1) using 5 levels gives expected results", {
-    res = discretize_density(density_fn = dnorm, n_levels = 5)
-    expected = list(
-        prob = c(0.11, 0.24, 0.30, .24, 0.11), 
-        endp = c(-Inf, -1.25, -0.38, 0.38, 1.25, Inf), 
-        repr = c(-1.73, -0.77, 0, 0.77, 1.73), 
-        dist = 0.08)
-    testthat::expect_equal(res, expected, tolerance = 0.05)
+  res = discretize_density(density_fn = dnorm, n_levels = 5)
+  expected = list(
+      prob = c(0.11, 0.24, 0.30, .24, 0.11), 
+      endp = c(-Inf, -1.25, -0.38, 0.38, 1.25, Inf), 
+      repr = c(-1.73, -0.77, 0, 0.77, 1.73), 
+      dist = 0.08)
+  testthat::expect_equal(res, expected, tolerance = 0.05)
 })

tests/testthat/test_estimation.R

@@ -1,13 +1,15 @@
 context("Testing estimation of parameters")
 
-testthat::test_that("`mean` and `sd` are well estimated given `pk` for normal case", {
+testthat::test_that("`mean` and `sd` are well estimated given `prob`
+  for normal case", {
   actual <- c(-1, 0.5) # actual mean and sd
   prob <- c("1" = 0.313, "2" = 0.579, "3" = 0.105, "4" = 0.003)
   estimates <- estimate_mean_and_sd(prob, 5)
   testthat::expect_equal(estimates, actual, tolerance = 0.1)
 })
 
-testthat::test_that("`mean` and `sd` are well estimated given `pk` for skew case", {
+testthat::test_that("`mean` and `sd` are well estimated given `prob`
+  for skew case", {
   actual <- c(0.146858, 1.084341) # actual mean and sd
   skew <- -0.4565873 # skewness
   prob <- c(
@@ -17,3 +19,85 @@ testthat::test_that("`mean` and `sd` are well estimated given `pk` for skew case
   estimates <- estimate_mean_and_sd(prob, 7, skew)
   testthat::expect_equal(estimates, actual, tolerance = 0.05)
 })
+
+testthat::test_that("estimate_params returns accurate estimates for 
+  latent parameters when data is a vector", {
+  set.seed(12345) # for reproducibility
+
+  # Generate test data for a single item (vector data)
+  data_vector <- rlikert(size = 1000,
+                         n_items = 1,
+                         n_levels = 5,
+                         mean = 0.5, 
+                         sd   = 1.2,
+                         skew = 0)
+
+  # Estimate parameters
+  estimates <- estimate_params(data_vector, n_levels = 5, skew = 0)
+
+  # Extract estimated means and standard deviations
+  estimated_mean <- as.numeric(estimates["mean"])
+  estimated_sd <- as.numeric(estimates["sd"])
+
+  # Actual means and standard deviations
+  actual_mean <- 0.5
+  actual_sd <- 1.2
+
+  # Check if estimated means are close to actual means
+  testthat::expect_equal(estimated_mean, actual_mean, tolerance = 0.1)
+
+  # Check if estimated standard deviations are close to actual standard deviations
+  testthat::expect_equal(estimated_sd, actual_sd, tolerance = 0.1)
+})
+
+testthat::test_that("estimate_params returns accurate estimates for 
+latent parameters", {
+  set.seed(12345) # for reproducibility
+
+  # Generate test data
+  corr <- matrix(c(1.00, -0.63, -0.39, 
+                   -0.63, 1.00, 0.41, 
+                   -0.39, 0.41, 1.00), nrow = 3)
+  data <- rlikert(size = 1000,
+                  n_items = 3,
+                  n_levels = c(4, 5, 6),
+                  mean = c(0, -1, 0), 
+                  sd   = c(0.8, 1, 1),
+                  corr = corr)
+
+  # Estimate parameters
+  estimates <- estimate_params(data, n_levels = c(4, 5, 6), skew = 0)
+
+  # Extract estimated means and standard deviations
+  estimated_means <- as.numeric(estimates["mean", ])
+  estimated_sds <- as.numeric(estimates["sd", ])
+
+  # Actual means and standard deviations
+  actual_means <- c(0, -1, 0)
+  actual_sds <- c(0.8, 1, 1)
+
+  # Check if estimated means are close to actual means
+  testthat::expect_equal(estimated_means, actual_means, tolerance = 0.1)
+
+  # Check if estimated sds are close to actual sds
+  testthat::expect_equal(estimated_sds, actual_sds, tolerance = 0.1)
+})
+
+testthat::test_that("plot_contour executes without errors", {
+  # Define a simple objective function for testing
+  test_fn <- function(x, endp, prob, cdf_X) {
+    u <- x[1]
+    v <- x[2]
+    y <- cdf_X(v * endp - u * v)
+    return(matrix(utils::tail(y, -1) - utils::head(y, -1) - prob))
+  }
+
+  # Example parameters for the test
+  endp <- c(-Inf, -1, 0, 1, Inf)
+  prob <- c(0.1, 0.2, 0.4, 0.3)
+  cdf_X <- stats::pnorm
+  trace <- matrix(c(rep(0, 100), rep(1, 100)), nrow = 2)
+
+  # Run the plotting function
+  testthat::expect_silent(plot_contour(test_fn, endp, prob, cdf_X, trace))
+})