Updated examples.

examples/mnl-design-bayesian-priors.R

@@ -5,15 +5,18 @@ rm(list = ls(all = TRUE))
 # library(spdesign)
 
 # Define the list of utility functions ----
+#' Generating a design with Bayesian priors.
 utility <- list(
-  alt1 = "b_x1[0.1] * x_1[2:5] + b_x2[uniform_p(-1, 1)] * x_2[c(0, 1)] + b_x3[normal_p(0, 1)] * x_3[seq(0, 1, 0.25)]",
-  alt2 = "b_x1      * x_1      + b_x2      * x_2          + b_x3          * x_3"
+  alt1 = "b_x1[0.1] * x1[2:5] + b_x2[uniform_p(-1, 1)] * x2[c(0, 1)] + b_x3[normal_p(0, 1)] * x3[seq(0, 1, 0.25)]",
+  alt2 = "b_x1      * x1      + b_x2                   * x2          + b_x3                 * x3"
 )
 
 # Generate designs ----
-design <- generate_design(utility, rows = 6,
+design <- generate_design(utility, rows = 20,
                           model = "mnl", efficiency_criteria = "d-error",
-                          algorithm = "federov", draws = "scrambled-sobol",
+                          algorithm = "rsc", draws = "scrambled-sobol",
                           control = list(
-                            max_iter = 100
+                            max_iter = 10000
                           ))
+
+summary(design)

examples/mnl-design-dummy-coding-bayesian-priors-and-level-occurrrence-restrictions.R

@@ -0,0 +1,26 @@
+#
+# Example file for creating a simple MNL design with Bayesian priors
+#
+rm(list = ls(all = TRUE))
+# library(spdesign)
+
+# Define the list of utility functions ----
+#' Generating a design with Bayesian priors using dummy coding and level occurrences
+#' NOTE: This design may take a long time. It has to first find a candidate that
+#' meets the restrictions and then evaluate whether it is better than the
+#' current best. Little information is provided about the process along the way.
+utility <- list(
+  alt1 = "b_x1_dummy[c(uniform_p(-1, 1), uniform_p(-1, 1))] * x1[c(1, 2, 3)](6:10, 4:14, 6:10) + b_x2[0.4] * x2[c(0, 1)](9:11) + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
+  alt2 = "b_x1_dummy                                        * x1                               + b_x2      * x2                + b_x3       * x3"
+)
+
+
+# Generate designs ----
+design <- generate_design(utility, rows = 20,
+                          model = "mnl", efficiency_criteria = "d-error",
+                          algorithm = "random", draws = "scrambled-sobol",
+                          control = list(
+                            max_iter = 10000
+                          ))
+
+summary(design)

examples/mnl-design-dummy-coding.R

@@ -18,3 +18,6 @@ design <- generate_design(utility, rows = 20,
 
 # Add a blocking variable to the design with 2 blocks.
 design <- block(design, 2)
+
+
+summary(design)

examples/mnl-design-random-with-specified-level-occurrence.R

@@ -6,8 +6,8 @@ rm(list = ls(all = TRUE))
 
 # Define the list of utility functions ----
 utility <- list(
-  alt1 = "b_x1_dummy[c(0.1, 0.2)] * x_1[c(1, 3, 5)](6:10) + b_x2[0.4] * x_2[c(0, 1)](9:11) + b_x3[-0.2] * x_3[seq(0, 1, 0.25)]",
-  alt2 = "b_x1_dummy      * x_1      + b_x2      * x_2          + b_x3          * x_3"
+  alt1 = "b_x1_dummy[c(0.1, 0.2)] * x1[c(1, 2, 3)](4:14) + b_x2[0.4] * x2[c(0, 1)](9:11) + b_x3[-0.2] * x3[seq(0, 1, 0.25)]",
+  alt2 = "b_x1_dummy              * x1                   + b_x2      * x2                + b_x3       * x3"
 )
 
 
@@ -17,3 +17,5 @@ design <- generate_design(utility, rows = 20,
                           algorithm = "federov", draws = "scrambled-sobol")
 
 design <- block(design, 4)
+
+summary(design)

examples/mnl-design-with-interactions.R

@@ -6,11 +6,14 @@ rm(list = ls(all = TRUE))
 
 # Define the list of utility functions ----
 utility <- list(
-  alt1 = "b_x1[0.1] * x_1[1:5] + b_x2[0.4] * x_2[c(0, 1)] + b_x3[-0.2] * x_3[seq(0, 1, 0.25)] + b_x1x2[-0.1] * I(x_1 * x_2)",
-  alt2 = "b_x1      * x_1      + b_x2      * x_2          + b_x3          * x_3"
+  alt1 = "b_x1[0.1] * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)] + b_x1x2[-0.1] * I(x1 * x2)",
+  alt2 = "b_x1      * x1      + b_x2      * x2          + b_x3       * x3"
 )
 
 # Generate designs ----
 design <- generate_design(utility, rows = 20,
                           model = "mnl", efficiency_criteria = "d-error",
-                          algorithm = "federov", draws = "scrambled-sobol")
+                          algorithm = "federov", draws = "scrambled-sobol",
+                          dudx = "b_x3")
+
+summary(design)

examples/mnl-design-with-multiway-interactions.R

@@ -6,11 +6,14 @@ rm(list = ls(all = TRUE))
 
 # Define the list of utility functions ----
 utility <- list(
-  alt1 = "b_x1[0.1] * x_1[1:5] + b_x2[0.4] * x_2[c(0, 1)] + b_x3[-0.2] * x_3[seq(0, 1, 0.25)] + b_x1x2[-0.1] * I(x_1 * x_2)",
-  alt2 = "b_x1      * x_1      + b_x2      * x_2          + b_x3          * x_3 + b_x1x2x3[0.1] * I(x_1 * x_2 * x_3)"
+  alt1 = "b_x1[0.1] * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] + b_x3[-0.2] * x3[seq(0, 1, 0.25)] + b_x1x2[-0.1] * I(x1 * x2)",
+  alt2 = "b_x1      * x1      + b_x2      * x2          + b_x3       * x3 + b_x1x2x3[0.1] * I(x1 * x2 * x3)"
 )
 
 # Generate designs ----
 design <- generate_design(utility, rows = 20,
                           model = "mnl", efficiency_criteria = "d-error",
-                          algorithm = "federov", draws = "scrambled-sobol")
+                          algorithm = "rsc", draws = "scrambled-sobol",
+                          dudx = "b_x3")
+
+summary(design)

examples/mnl-design-with-supplied-candidate-set.R

@@ -13,10 +13,10 @@ utility <- list(
 # Use the full factorial as the candidate set
 candidate_set <- full_factorial(
   list(
-    alt1_x1 = 1:5,
+    alt1_x1 = 2:5,
     alt1_x2 = c(0, 1),
     alt1_x3 = seq(0, 1, 0.25),
-    alt2_x1 = 1:5,
+    alt2_x1 = 2:5,
     alt2_x2 = c(0, 1),
     alt2_x3 = seq(0, 1, 0.25)
   )
@@ -30,3 +30,6 @@ design <- generate_design(utility, rows = 20,
                           model = "mnl", efficiency_criteria = "d-error",
                           algorithm = "federov", draws = "scrambled-sobol",
                           candidate_set = candidate_set)
+
+
+summary(design)

examples/mnl-design.R

@@ -23,3 +23,6 @@ design <- generate_design(utility, rows = 20,
 
 # Add a blocking variable to the design with 4 blocks.
 design <- block(design, 4)
+
+
+summary(design)