Add curves_concentration_max argument to ecxsys(),

closes #29

NEWS.md

 # stressaddition (development version)
 
 * `ec()` raises an error if the curve does not cross the desired response level.
+* `ecxsys()` gains a new argument `curves_concentration_max` which allows setting the maximum concentration of the predicted curves.
 
 # stressaddition 2.1.1
 

R/ecxsys.R

@@ -31,6 +31,9 @@
 #'   environmental stress. Must be between 0 and \code{effect_max}.
 #' @param effect_max The maximum value the effect could possibly reach. For
 #'   survival data in percent this should be 100 (the default).
+#' @param curves_concentration_max The maximum concentration of the predicted
+#'   curves. This might be useful if for example your highest observed
+#'   concentration is 30 but you would like to know the predicted values at 100.
 #' @param p,q The shape parameters of the beta distribution. Default is 3.2.
 #'
 #' @return A list (of class ecxsys) containing many different objects of which
@@ -86,6 +89,7 @@ ecxsys <- function(concentration,
                    effect_tox_observed,
                    effect_tox_env_observed = NULL,
                    effect_max = 100,
+                   curves_concentration_max = NULL,
                    p = 3.2,
                    q = 3.2) {
     output <- list(args = as.list(environment()))
@@ -268,23 +272,32 @@ ecxsys <- function(concentration,
     }
 
 
-    # smooth curves -------------------------------------------------------
+    # curves -------------------------------------------------------
     # In order to generate a broken x-axis the concentration vector must
     # also be broken in two. The left part of the axis is supposed to be at
     # 0 but because it's a log axis I have to make the values just really
     # small. The concentrations in the gap won't be used for plotting later.
-    n_smooth <- 1000  #  number of points to approximate the curves
+    len_curves <- 1000  #  number of points to approximate the curves
     conc_adjust_factor <- 10^-5
     output$conc_adjust_factor <- conc_adjust_factor
-    concentration_smooth <- 10 ^ seq(
+    if (is.null(curves_concentration_max)) {
+        curves_concentration_max <- max(concentration)
+    } else if (curves_concentration_max <  max(concentration)) {
+        if (curves_concentration_max < min(concentration[concentration > 0])) {
+            stop("'curves_concentration_max' is too low.")
+        }
+        warning("Your chosen value for 'curves_concentration_max' is less ",
+                "than the maximum observed concentration.")
+    }
+    curves_concentration <- 10 ^ seq(
         log10(min_conc * conc_adjust_factor),
-        log10(max(concentration)),
-        length.out = n_smooth
+        log10(curves_concentration_max),
+        length.out = len_curves
     )
-    output$curves <- predict_ecxsys(output, concentration_smooth)
+    output$curves <- predict_ecxsys(output, curves_concentration)
     output$curves$use_for_plotting <-
-        concentration_smooth < min_conc * conc_adjust_factor * 1.5 |
-        concentration_smooth > min_conc * 1.5
+        curves_concentration < min_conc * conc_adjust_factor * 1.5 |
+        curves_concentration > min_conc * 1.5
     output
 }
 

man/ecxsys.Rd

@@ -10,6 +10,7 @@ ecxsys(
   effect_tox_observed,
   effect_tox_env_observed = NULL,
   effect_max = 100,
+  curves_concentration_max = NULL,
   p = 3.2,
   q = 3.2
 )
@@ -31,6 +32,10 @@ environmental stress. Must be between 0 and \code{effect_max}.}
 \item{effect_max}{The maximum value the effect could possibly reach. For
 survival data in percent this should be 100 (the default).}
 
+\item{curves_concentration_max}{The maximum concentration of the predicted
+curves. This might be useful if for example your highest observed
+concentration is 30 but you would like to know the predicted values at 100.}
+
 \item{p, q}{The shape parameters of the beta distribution. Default is 3.2.}
 }
 \value{

tests/testthat/test-ecxsys.R

@@ -164,6 +164,7 @@ test_that("function arguments are returned unchanged", {
         effect_tox_observed = c(90, 81, 92, 28, 0),
         effect_tox_env_observed = c(29, 27, 33, 5, 0),
         effect_max = 100,
+        curves_concentration_max = NULL,
         p = 3.2,
         q = 3.2
     )