Change the way stress_tox_ca is calculated

Change the way stress_tox_ca is calculated to make the steps more logical. Does not change the results. Instead of averaging the effects and converting to stress, first calculate the stresses and then average them.

Also add a check that ensures that p is the same for both models and q is the same for both models. Otherwise the mixture prediction would get more complicated.

R/predict_mixture.R

@@ -44,7 +44,9 @@ predict_mixture <- function(model_1,
         length(concentration_2) == 1,
         !is.na(concentration_2),
         proportion_ca >= 0,
-        proportion_ca <= 1
+        proportion_ca <= 1,
+        model_1$args$p == model_2$args$p,
+        model_1$args$q == model_2$args$q
     )
 
     predicted_model_1 <- predict_ecxsys(model_1, concentration_1)
@@ -67,6 +69,7 @@ predict_mixture <- function(model_1,
         model_1$effect_tox_mod,
         data.frame(concentration = concentration_1 + concentration_2_equivalent)
     )
+    stress_tox_ca_1 <- effect_to_stress(effect_tox_ca_1)
 
     response_level_1 <- 100 - predicted_model_1$effect_tox / model_1$args$effect_max * 100
     response_level_1 <- clamp(response_level_1, 1e-10, 100 - 1e-10)
@@ -79,8 +82,9 @@ predict_mixture <- function(model_1,
         model_2$effect_tox_mod,
         data.frame(concentration = concentration_2 + concentration_1_equivalent)
     )
+    stress_tox_ca_2 <- effect_to_stress(effect_tox_ca_2)
 
-    stress_tox_ca <- effect_to_stress(effect_tox_ca_1 * 0.5 + effect_tox_ca_2 * 0.5)
+    stress_tox_ca <- (stress_tox_ca_1 + stress_tox_ca_2) / 2
 
     # sys -----------------------------------------------------------------
     sys_1 <- predict(model_1$sys_tox_mod, data.frame(stress_tox = stress_tox_ca))