skipping tests on Cran

.Rhistory

@@ -1,5 +1,3 @@
-"detections among replicates.")
-errMsg2 <- 'See the eDNAjoint guide for data formatting help: '
 errMsg3 <- paste0('https://bookdown.org/abigailkeller/eDNAjoint_vignette',
 '/usecase1.html#prepare-the-data')
 errMsg <- paste(errMsg1,errMsg2,errMsg3, sep = "\n")
@@ -510,3 +508,5 @@ print('no')
 }
 roxygen2::roxygenise()
 codemetar::write_codemeta()
+codemetar::write_codemeta()
+roxygen2::roxygenise()

R/globals.R

@@ -1 +1 @@
-utils::globalVariables(c("L","median","survey_type","inits"))
+utils::globalVariables(c("L","median","survey_type","inits","value"))

tests/testthat/test-detectionCalculate.R

@@ -1,64 +1,65 @@
-test_that("detectionCalculate input checks work", {
-  #' @srrstats {G5.2,G5.2b} Tests the assure function input checks are behaving
-  #'   as expected.
-  # run joint model to do tests with
-  model1 <- suppressWarnings({jointModel(data = gobyData,
-                                         cov = c('Filter_time','Salinity'),
-                                         n.chain = 1,n.iter.burn = 25,
-                                         n.iter.sample = 75,multicore = FALSE)})
-
-  model2 <- suppressWarnings({jointModel(data = greencrabData,family = 'negbin',
-                                         n.chain = 1,n.iter.burn = 25,
-                                         n.iter.sample = 75,multicore = FALSE)})
-
-  #1. make sure model fit is of class stanfit
-  expect_error(detectionCalculate(as.matrix(model1$model), mu = c(0.1, 0.5),
-                                  cov.val = c(0,0)),
-               "modelfit must be of class 'stanfit'.")
-
-  #2. make sure ci is valid
-  expect_error(detectionCalculate(model1$model, mu = c('0', 0.5),
-                                  cov.val = c(0,0)),
-               "mu must be a numeric vector of positive values")
-
-  #3. make sure mu is a numeric vector of positive values
-  expect_error(detectionCalculate(model1$model, mu = c(0, 0.5),
-                                  cov.val = c(0,0)),
-               "mu must be a numeric vector of positive values")
-
-  #4. make sure probability is a numeric value between 0 and 1
-  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
-                                  cov.val = c(0,0), probability = 1.05),
-               "probability must be a numeric value between 0 and 1")
-
-  #5. cov.val is numeric, if provided
-  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
-                                  cov.val = c('0',0)),
-               "cov.val must be a numeric vector")
-
-  #6. Only include input cov.val if covariates are included in model
-  expect_error(detectionCalculate(model2$model, mu = c(0.1, 0.5),
-                                  cov.val = c(0,0)),
-               paste0("cov.val must be NULL if the model does not ",
-                      "contain site-level covariates."))
-
-  #7. Input cov.val is the same length as the number of estimated covariates.
-  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
-                                  cov.val = c(0,0,0)),
-               paste0("cov.val must be of the same length as the number of ",
-                      "non-intercept site-level coefficients in the model."))
-
-  #8. If covariates are in model, cov.val must be provided
-  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5)),
-               paste0("cov.val must be provided if the model contains ",
-                      "site-level covariates."))
-
-  #9. qPCR.N must be an integer
-  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
-                                  cov.val = c(0,0), qPCR.N = 6.8),
-               "qPCR.N should be an integer.")
-})
-
-
-
-
+test_that("detectionCalculate input checks work", {
+  testthat::skip_on_cran()
+  #' @srrstats {G5.2,G5.2b} Tests the assure function input checks are behaving
+  #'   as expected.
+  # run joint model to do tests with
+  model1 <- suppressWarnings({jointModel(data = gobyData,
+                                         cov = c('Filter_time','Salinity'),
+                                         n.chain = 1,n.iter.burn = 25,
+                                         n.iter.sample = 75,multicore = FALSE)})
+
+  model2 <- suppressWarnings({jointModel(data = greencrabData,family = 'negbin',
+                                         n.chain = 1,n.iter.burn = 25,
+                                         n.iter.sample = 75,multicore = FALSE)})
+
+  #1. make sure model fit is of class stanfit
+  expect_error(detectionCalculate(as.matrix(model1$model), mu = c(0.1, 0.5),
+                                  cov.val = c(0,0)),
+               "modelfit must be of class 'stanfit'.")
+
+  #2. make sure ci is valid
+  expect_error(detectionCalculate(model1$model, mu = c('0', 0.5),
+                                  cov.val = c(0,0)),
+               "mu must be a numeric vector of positive values")
+
+  #3. make sure mu is a numeric vector of positive values
+  expect_error(detectionCalculate(model1$model, mu = c(0, 0.5),
+                                  cov.val = c(0,0)),
+               "mu must be a numeric vector of positive values")
+
+  #4. make sure probability is a numeric value between 0 and 1
+  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
+                                  cov.val = c(0,0), probability = 1.05),
+               "probability must be a numeric value between 0 and 1")
+
+  #5. cov.val is numeric, if provided
+  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
+                                  cov.val = c('0',0)),
+               "cov.val must be a numeric vector")
+
+  #6. Only include input cov.val if covariates are included in model
+  expect_error(detectionCalculate(model2$model, mu = c(0.1, 0.5),
+                                  cov.val = c(0,0)),
+               paste0("cov.val must be NULL if the model does not ",
+                      "contain site-level covariates."))
+
+  #7. Input cov.val is the same length as the number of estimated covariates.
+  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
+                                  cov.val = c(0,0,0)),
+               paste0("cov.val must be of the same length as the number of ",
+                      "non-intercept site-level coefficients in the model."))
+
+  #8. If covariates are in model, cov.val must be provided
+  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5)),
+               paste0("cov.val must be provided if the model contains ",
+                      "site-level covariates."))
+
+  #9. qPCR.N must be an integer
+  expect_error(detectionCalculate(model1$model, mu = c(0.1, 0.5),
+                                  cov.val = c(0,0), qPCR.N = 6.8),
+               "qPCR.N should be an integer.")
+})
+
+
+
+

tests/testthat/test-detectionPlot.R

@@ -1,74 +1,75 @@
-test_that("detectionPlot input checks work", {
-  #' @srrstats {G5.2,G5.2b} Tests the assure function input checks are behaving
-  #'   as expected.
-  # run joint model to do tests with
-  model1 <- suppressWarnings({jointModel(data = gobyData,
-                                         cov = c('Filter_time','Salinity'),
-                                         n.chain = 1,n.iter.burn = 25,
-                                         n.iter.sample = 75,
-                                         multicore = FALSE)})
-
-  model2 <- suppressWarnings({jointModel(data = greencrabData,
-                                         family = 'negbin',
-                                         n.chain = 1,n.iter.burn = 25,
-                                         n.iter.sample = 75,multicore = FALSE)})
-
-  #1. make sure model fit is of class stanfit
-  expect_error(detectionPlot(as.matrix(model1$model), mu.min = 0.1,
-                             mu.max = 1, cov.val = c(0,0)),
-               "modelfit must be of class 'stanfit'.")
-
-  #2. make sure mu.min is a numeric value
-  expect_error(detectionPlot(model1$model, mu.min = '0.1', mu.max = 1,
-                             cov.val = c(0,0)),
-               "mu.min must be a numeric value greater than 0")
-
-  #3. make sure mu.min is a numeric value
-  expect_error(detectionPlot(model1$model, mu.min = 0, mu.max = 1,
-                             cov.val = c(0,0)),
-               "mu.min must be a numeric value greater than 0")
-
-  #4. make sure mu.max is a numeric value
-  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = '1',
-                             cov.val = c(0,0)),
-               "mu.max must be a numeric value greater than mu.min")
-
-  #5. make sure mu.max is a numeric value
-  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 0.1,
-                             cov.val = c(0,0)),
-               "mu.max must be a numeric value greater than mu.min")
-
-  #6. make sure mu.max is a numeric value
-  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
-                             cov.val = c(0,0), probability = 1.1),
-               "probability must be a numeric value between 0 and 1")
-
-  #7. cov.val is numeric, if provided
-  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
-                             cov.val = c(0,'0')),
-               "cov.val must be a numeric vector")
-
-  #8. Only include input cov.val if covariates are included in model
-  expect_error(detectionPlot(model2$model, mu.min = 0.1, mu.max = 1,
-                             cov.val = c(0,0)),
-               paste0("cov.val must be NULL if the model does not contain ",
-                      "site-level covariates."))
-
-  #9. Input cov.val is the same length as the number of estimated covariates.
-  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
-                             cov.val = c(0,0,0)),
-               paste0("cov.val must be of the same length as the number of ",
-                      "non-intercept site-level coefficients in the model."))
-
-  #10. If covariates are in model, cov.val must be provided
-  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1),
-               paste0("cov.val must be provided if the model contains ",
-                      "site-level covariates."))
-
-  #11. qPCR.N must be an integer
-  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
-                             cov.val = c(0,0), qPCR.N = 6.8),
-               "qPCR.N should be an integer.")
-})
-
-
+test_that("detectionPlot input checks work", {
+  testthat::skip_on_cran()
+  #' @srrstats {G5.2,G5.2b} Tests the assure function input checks are behaving
+  #'   as expected.
+  # run joint model to do tests with
+  model1 <- suppressWarnings({jointModel(data = gobyData,
+                                         cov = c('Filter_time','Salinity'),
+                                         n.chain = 1,n.iter.burn = 25,
+                                         n.iter.sample = 75,
+                                         multicore = FALSE)})
+
+  model2 <- suppressWarnings({jointModel(data = greencrabData,
+                                         family = 'negbin',
+                                         n.chain = 1,n.iter.burn = 25,
+                                         n.iter.sample = 75,multicore = FALSE)})
+
+  #1. make sure model fit is of class stanfit
+  expect_error(detectionPlot(as.matrix(model1$model), mu.min = 0.1,
+                             mu.max = 1, cov.val = c(0,0)),
+               "modelfit must be of class 'stanfit'.")
+
+  #2. make sure mu.min is a numeric value
+  expect_error(detectionPlot(model1$model, mu.min = '0.1', mu.max = 1,
+                             cov.val = c(0,0)),
+               "mu.min must be a numeric value greater than 0")
+
+  #3. make sure mu.min is a numeric value
+  expect_error(detectionPlot(model1$model, mu.min = 0, mu.max = 1,
+                             cov.val = c(0,0)),
+               "mu.min must be a numeric value greater than 0")
+
+  #4. make sure mu.max is a numeric value
+  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = '1',
+                             cov.val = c(0,0)),
+               "mu.max must be a numeric value greater than mu.min")
+
+  #5. make sure mu.max is a numeric value
+  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 0.1,
+                             cov.val = c(0,0)),
+               "mu.max must be a numeric value greater than mu.min")
+
+  #6. make sure mu.max is a numeric value
+  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
+                             cov.val = c(0,0), probability = 1.1),
+               "probability must be a numeric value between 0 and 1")
+
+  #7. cov.val is numeric, if provided
+  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
+                             cov.val = c(0,'0')),
+               "cov.val must be a numeric vector")
+
+  #8. Only include input cov.val if covariates are included in model
+  expect_error(detectionPlot(model2$model, mu.min = 0.1, mu.max = 1,
+                             cov.val = c(0,0)),
+               paste0("cov.val must be NULL if the model does not contain ",
+                      "site-level covariates."))
+
+  #9. Input cov.val is the same length as the number of estimated covariates.
+  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
+                             cov.val = c(0,0,0)),
+               paste0("cov.val must be of the same length as the number of ",
+                      "non-intercept site-level coefficients in the model."))
+
+  #10. If covariates are in model, cov.val must be provided
+  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1),
+               paste0("cov.val must be provided if the model contains ",
+                      "site-level covariates."))
+
+  #11. qPCR.N must be an integer
+  expect_error(detectionPlot(model1$model, mu.min = 0.1, mu.max = 1,
+                             cov.val = c(0,0), qPCR.N = 6.8),
+               "qPCR.N should be an integer.")
+})
+
+

tests/testthat/test-jointModel.R

@@ -1,4 +1,5 @@
 test_that("jointModel input checks work", {
+  testthat::skip_on_cran()
   #' @srrstats {G5.2,G5.2b,BS2.15} Tests the assure function input checks are
   #'   behaving as expected.
   #1. input tags are valid, q = FALSE, cov = FALSE
@@ -849,6 +850,7 @@ test_that("jointModel input checks work", {
 #'   values are within the 95% credibility interval of the parameters'
 #'   posteriors in the function output.
 test_that("jointModel parameter recovery tests work",{
+  testthat::skip_on_cran()
 
   ################################
   # model run 1: smaller dataset #
@@ -1146,6 +1148,7 @@ test_that("jointModel parameter recovery tests work",{
 
 
 test_that("jointModel probability distribution tests work",{
+  testthat::skip_on_cran()
 
   #' @srrstats {PD4.2} This package fits models with fixed distributions to
   #'   data. Users can choose the distribution used to represent the
@@ -1234,6 +1237,7 @@ test_that("jointModel probability distribution tests work",{
 })
 
 test_that("semi-paired model works", {
+  testthat::skip_on_cran()
 
   ## 1.
   # model includes 'p10','q','phi','beta'