added a few new tests on test that output

tests/manual-tests/agora.R

@@ -15,7 +15,7 @@ bcoeff <- list(
   basc = -1.2,
   basc2 = -1.4,
   baction = 0.1,
-  badvisory = 0.4,
+  badvisory =0.4,
   bpartner = 0.3,
   bcomp = 0.02)
 

tests/manual-tests/feedadditives.R

+rm(list=ls())
+devtools::load_all()
+
+designpath<- system.file("extdata","feedadditives" ,package = "simulateDCE")
+
+#notes <- "This design consists of different heuristics. One group did not attend the methan attribute, another group only decided based on the payment"
+
+notes <- "Three heuristics"
+
+resps =300  # number of respondents
+nosim=2 # number of simulations to run (about 500 is minimum)
+
+#betacoefficients should not include "-"
+basc = 0.2
+bcow = 0.3
+badv = 0.3
+bvet = 0.3
+bfar = 0.3
+bmet = 0.3
+bbon = 0.3
+bbon2 = 1.9
+basc2 =2
+
+decisiongroups=c(0,0.3,0.6,0.8,1)
+
+#place your utility functions here
+u<- list(u1= list(
+  v1 =V.1 ~  bcow*alt1.cow + badv * alt1.adv + bvet * alt1.vet + bfar * alt1.far + bmet*alt1.met + bbon * alt1.bon,
+  v2 =V.2 ~  bcow*alt2.cow + badv * alt2.adv + bvet * alt2.vet + bfar * alt2.far + bmet*alt2.met + bbon * alt2.bon,
+  v3 =V.3 ~ basc)
+  ,
+  u2 = list(
+    v1 =V.1 ~  bcow*alt1.cow + badv * alt1.adv + bvet * alt1.vet + bfar * alt1.far + bbon * alt1.bon,
+    v2 =V.2 ~  bcow*alt2.cow + badv * alt2.adv + bvet * alt2.vet + bfar * alt2.far + bbon * alt2.bon,
+    v3 =V.3 ~ basc),
+  u3 = list(
+    v1 =V.1 ~  bbon2 * alt1.bon,
+    v2 =V.2 ~  bbon2 * alt2.bon,
+    v3 =V.3 ~ basc),
+
+  u4 = list(v1 =V.1 ~ basc2 + bcow*alt1.cow + badv * alt1.adv + bvet * alt1.vet + bfar * alt1.far + bmet*alt1.met + bbon * alt1.bon,
+            v2 =V.2 ~  bcow*alt2.cow + badv * alt2.adv + bvet * alt2.vet + bfar * alt2.far + bmet*alt2.met + bbon * alt2.bon,
+            v3 =V.3 ~ basc)
+
+
+
+)

tests/testthat/test-sim_all.R

@@ -153,7 +153,7 @@ test_that("Simulation results are reasonable", {
   result1 <- sim_all(nosim = nosim, resps = resps, destype = destype, designpath = designpath, u = ul, bcoeff = bcoeff)
 
   ## The function below is intended to find a dataframe called $coef in the output
-  ## regardless of the output data structure which can vary
+  ## regardless of the output data structure, which can vary
   find_dataframe <- function(list_object, dataframe_name) {
     # Check if the current object is a list
     if (is.list(list_object)) {
@@ -182,12 +182,25 @@ test_that("Simulation results are reasonable", {
 
   for (variable in names(bcoeff)){
     ### Compare singular input value (hypothesis) with the average value of all iterations. ###
-    ### This could be made more rigorous by testing each iteration or by changing the       ###
+    ### This could be made more rigorous by testing each iteration rather than the mean or by changing the ###
     ### tolerance around the input value considered valid.
     input_value <- bcoeff[[variable]]
     mean_output_value <- mean(coeffNestedOutput[[paste0("est_", variable)]]) ## access the mean value of each iteration
 
+
+
     ##change this depending on how rigorous you want to be
+    # Check if variable exists in coeffNestedOutput
+    expect_true(paste0("est_", variable) %in% names(coeffNestedOutput),
+                sprintf("Variable est_%s does not exist in coeffNestedOutput", variable))
+
+    # Check if variable is numeric
+    expect_type(coeffNestedOutput[[paste0("est_", variable)]], "numeric",
+              sprintf("Variable est_%s in coeffNestedOutput is not numeric", variable))
+
+    # Check if each entry in the variable column is numeric
+    expect_true(all(sapply(coeffNestedOutput[[paste0("est_", variable)]], is.numeric)),
+                sprintf("Variable est_%s in coeffNestedOutput contains non-numeric values", variable))
     expect_gt(mean_output_value, input_value - 1)
     expect_lt(mean_output_value, input_value + 1)
   }