Merge pull request #41 from R-Computing-Lab/dev_main

added bonus tests

Author: smasongarrison

NEWS.md

@@ -1,3 +1,6 @@
+# BGmisc 1.3.1.1
+* Added some more tests of identifyModel.R
+
 # BGmisc 1.3.1
 * Confirmed that all orcids are correct
 * Added gedcom importer

tests/testthat/test-identifyModel.R

@@ -1,3 +1,7 @@
+# Test cases for model identification and fitting functions
+
+# Test that a fully specified model with adequate parameters is correctly identified
+
 test_that("identified model is identified", {
   set.seed(5)
 
@@ -14,6 +18,8 @@ test_that("identified model is identified", {
   )$identified)
 })
 
+# Test for error when a model is underidentified due to insufficient parameters
+
 test_that("underidentified model is unidentified", {
   set.seed(5)
 
@@ -24,7 +30,9 @@ test_that("underidentified model is unidentified", {
   )$identified)
 })
 
+# Test that fitComponentModel returns expected coefficients with valid input data
 test_that("fitComponentModel work", {
+  # Initial example using commented out real data loading
   # data(twinData, package = "OpenMx")
   # selVars <- c("ht1", "ht2")
   # mzData <- subset(twinData, zyg %in% c(1), c(selVars, "zyg"))
@@ -51,3 +59,28 @@ test_that("fitComponentModel work", {
   )
   expect_equal(result$coefficients, c(compmA = 0.0036404, compmC = 0.0002317, compmE = 0.0006713), tolerance = 1e-4)
 })
+
+# Test for incorrect input types in comp2vech
+test_that("comp2vech handles incorrect input types", {
+  expect_error(comp2vech("some non-matrix input"), "x is neither a list nor a matrix")
+})
+
+# Ensure default names are correctly assigned
+test_that("default names are assigned correctly", {
+  result <- identifyComponentModel(matrix(1, 2, 2), matrix(1, 2, 2))
+  expect_equal(result$nidp, c("Comp1", "Comp2"))
+})
+
+
+
+# Test for list handling in comp2vech
+test_that("comp2vech handles lists correctly", {
+  list_input <- list(matrix(c(1, 0.5, 0.5, 1), 2, 2), matrix(1, 2, 2))
+  expect_length(comp2vech(list_input, include.zeros = TRUE), 10)  # Adjust based on expected vector length
+})
+
+
+# Test for incorrect input types in comp2vech
+test_that("comp2vech handles incorrect input types", {
+  expect_error(comp2vech("some non-matrix input"), "x is neither a list nor a matrix")
+})

vignettes/analyticrelatedness.Rmd

@@ -46,7 +46,7 @@ calculateRelatedness(generations = 1, full = FALSE)
 
 # Inferring Relatedness Coefficient
 
-The `inferRelatedness` function is designed to infer the relatedness coefficient between two groups based on the observed correlation between their additive genetic variance and shared environmental variance. This function leverages the ACE framework.
+The `inferRelatedness` function is designed to infer the relatedness coefficient between two groups based on the observed correlation between their additive genetic variance and shared environmental variance. This function leverages the `ACE` framework.
 
 ```{r}
 # Example usage:

vignettes/validation.Rmd

@@ -89,6 +89,7 @@ library(tidyverse)
 
 summarizeFamilies(df, famID = "newFamID", personID = "personID")$family_summary %>% glimpse()
 ```
+
 If we didn't know to look for duplicates, we might not notice the issue. Indeed, only of the duplicates was selected as are founder member. However, the `checkIDs` function can help us identify and repair these errors:
 
 ```{r}
@@ -106,6 +107,7 @@ df %>% filter(personID %in% result$non_unique_ids) %>%
   arrange(personID)
 
 ```
+
 Yep, these are  definitely the duplicates.
 
 ```{r}
@@ -155,7 +157,7 @@ results <- checkSex(potter, code_male = 1, code_female = 0, verbose = TRUE, repa
 print(results)
 ```
 
-In this example, the checkSex function checks the unique values in the sex column and identifies any inconsistencies in the sex coding of parents. The function returns a list containing validation results, such as the unique values found in the sex column and any inconsistencies in the sex coding of parents.
+In this example, the `checkSex` function checks the unique values in the sex column and identifies any inconsistencies in the sex coding of parents. The function returns a list containing validation results, such as the unique values found in the sex column and any inconsistencies in the sex coding of parents.
 
 If incorrect sex codes are found, you can attempt to repair them automatically using the repair argument: