added tests for decisiongroups and latent class model

inst/extdata/spdesigns/twoattr.R

+
+
+# Taken from Erlend Sandorf
+# Example file for creating a simple MNL design
+#
+rm(list = ls(all = TRUE))
+library(spdesign)
+
+# Define the list of utility functions ----
+#' Specifying a utility function with 3 attributes and a constant for the
+#' SQ alternative. The design has 20 rows.
+utility <- list(
+  alt1 = "b_x1[0.1]  * x1[1:5] + b_x2[0.4] * x2[c(0, 1)] ",
+  alt2 = "b_x1       * x1      + b_x2      * x2          "
+)
+
+# Generate designs ----
+design <- generate_design(utility, rows = 20,
+                          model = "mnl", efficiency_criteria = "d-error",
+                          algorithm = "rsc", draws = "scrambled-sobol",
+                          control = list(
+                            max_iter = 2000,
+                            max_no_improve = 5000
+                          ))
+
+saveRDS(design,"inst/extdata/spdesigns/designs/twoattr.RDS")

tests/manual-tests/test_decisiongroups.R

+
+rm(list=ls())
+devtools::load_all()
+
+library(dplyr)
+designpath<- system.file("extdata","spdesigns","designs", package = "simulateDCE")
+
+destype = 'spdesign'
+resps =4000  # number of respondents
+nosim=2 # number of simulations to run (about 500 is minimum)
+
+#betacoefficients should not include "-"
+bcoeff <- list(
+  bx1A = 0.1, ## very high asc
+  bx1B = -0.1,
+  bx2A = 0.2,
+  bx2B = -0.2
+  )
+
+
+desisiongroups =c(0,0.3,1)
+
+ul<- list(uA=
+            list(
+              v1 = V.1 ~  bx1A*alt1.x1+ bx2A * alt1.x2,
+              v2 = V.2 ~  bx1A*alt2.x1+ bx2A * alt2.x2),
+          uB = list(
+              v1 = V.1 ~  bx1B*alt1.x1+ bx2B * alt1.x2,
+              v2 = V.2 ~  bx1B*alt2.x1+ bx2B * alt2.x2)
+
+)
+
+simplesim <- sim_all(nosim = nosim, resps = resps,designpath = designpath, bcoeff = bcoeff, u = ul,destype = "spdesign" , decisiongroups = desisiongroups)
+
+testdata = simplesim[["twoattr"]][[1]][["data"]] %>%
+  distinct(group, Choice_situation ,.keep_all = T) %>%
+  arrange(Choice_situation)
+
+## estimate models
+
+library(apollo)
+
+apollo_initialise()
+
+
+apollo_control = list(
+  modelName       = "Clogit_simpledesign",
+  modelDescr      = "Simple conditional logit model for group 1 ",
+  indivID         = "ID",
+  nCores          = 1,
+  outputDirectory = "/~"
+)
+
+
+## group 1
+
+database <- simplesim[["twoattr"]][[1]][["data"]]
+apollo_beta = c(
+
+  b_x1 = 0,
+  b_x2 = 0
+)
+
+apollo_fixed = c()
+
+
+
+
+
+# ################################################################# #
+#### GROUP AND VALIDATE INPUTS                                   ####
+# ################################################################# #
+
+apollo_inputs = apollo_validateInputs()
+
+# ################################################################# #
+#### DEFINE MODEL AND LIKELIHOOD FUNCTION                        ####
+# ################################################################# #
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+  ### Attach inputs and detach after function exit
+  apollo_attach(apollo_beta, apollo_inputs)
+  on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+  ### Create list of probabilities P
+  P = list()
+
+  ### Define settings for MNL model component that are generic across classes
+  mnl_settings = list(
+    alternatives = c(alt1=1, alt2=2),
+    avail        = list(alt1=1, alt2=1),
+    choiceVar    = CHOICE
+  )
+
+
+
+
+  ### Compute class-specific utilities
+  V=list()
+  V[["alt1"]] = b_x1*alt1_x1 + b_x2*alt1_x2
+  V[["alt2"]] = b_x1*alt2_x1 + b_x2*alt2_x2
+
+  mnl_settings$utilities = V
+  #mnl_settings$componentName = paste0("Class_",s)
+
+
+
+  ### Compute probabilities using MNL model
+  P[["model"]] = apollo_mnl(mnl_settings, functionality)
+
+  ### Take product across observation for same individual
+  P = apollo_panelProd(P, apollo_inputs, functionality)
+
+  ### Prepare and return outputs of function
+  P = apollo_prepareProb(P, apollo_inputs, functionality)
+  return(P)
+
+
+
+}
+
+# ################################################################# #
+#### MODEL ESTIMATION                                            ####
+# ################################################################# #
+
+### Optional starting values search
+# apollo_beta=apollo_searchStart(apollo_beta, apollo_fixed,apollo_probabilities, apollo_inputs)
+
+model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs ,estimate_settings =list(estimationRoutine="BFGS" , hessianRoutine= "numDeriv"))
+
+
+
+apollo_modelOutput(model)
+
+
+
+
+### latent class model
+
+apollo_control = list(
+  modelName       = "Latent Class logit_simpledesign",
+  modelDescr      = "Latent Class  logit model",
+  indivID         = "ID",
+  nCores          = 1,
+  outputDirectory = "/~"
+)
+
+
+## group 1
+
+database <- simplesim[["twoattr"]][[1]][["data"]]
+
+apollo_beta = c(
+  b_x1_a = 0,
+  b_x2_a = 0,
+  b_x1_b = 0,
+  b_x2_b = 0,
+  delta_a = 0,
+  delta_b = 0
+)
+
+### Vector with names (in quotes) of parameters to be kept fixed at their starting value in apollo_beta, use apollo_beta_fixed = c() if none
+apollo_fixed = c("delta_b")
+
+# ################################################################# #
+#### DEFINE LATENT CLASS COMPONENTS                              ####
+# ################################################################# #
+
+apollo_lcPars=function(apollo_beta, apollo_inputs){
+  lcpars = list()
+
+  lcpars[["b_x1"]] = list(b_x1_a, b_x1_b)
+  lcpars[["b_x2"]] = list(b_x2_a, b_x2_b)
+
+  V=list()
+  V[["class_a"]] = delta_a
+  V[["class_b"]] = delta_b
+
+
+  classAlloc_settings = list(
+    classes      = c(class_a = 1, class_b = 2),
+    utilities    = V
+  )
+
+  lcpars[["pi_values"]] = apollo_classAlloc(classAlloc_settings)
+
+  return(lcpars)
+}
+
+
+
+# ################################################################# #
+#### GROUP AND VALIDATE INPUTS                                   ####
+# ################################################################# #
+
+apollo_inputs = apollo_validateInputs()
+
+# ################################################################# #
+#### DEFINE MODEL AND LIKELIHOOD FUNCTION                        ####
+# ################################################################# #
+
+
+# ################################################################# #
+#### DEFINE MODEL AND LIKELIHOOD FUNCTION                        ####
+# ################################################################# #
+
+apollo_probabilities=function(apollo_beta, apollo_inputs, functionality="estimate"){
+
+  ### Attach inputs and detach after function exit
+  apollo_attach(apollo_beta, apollo_inputs)
+  on.exit(apollo_detach(apollo_beta, apollo_inputs))
+
+  ### Create list of probabilities P
+  P = list()
+
+  ### Define settings for MNL model component that are generic across classes
+  mnl_settings = list(
+    alternatives = c(alt1=1, alt2=2),
+    avail        = list(alt1=1, alt2=1),
+    choiceVar    = CHOICE
+  )
+
+  ### Loop over classes
+  for(s in 1:2){
+
+    ### Compute class-specific utilities
+    V=list()
+    V=list()
+    V[["alt1"]] = b_x1[[s]]*alt1_x1 + b_x2[[s]]*alt1_x2
+    V[["alt2"]] = b_x1[[s]]*alt2_x1 + b_x2[[s]]*alt2_x2
+
+
+
+    mnl_settings$utilities = V
+    #mnl_settings$componentName = paste0("Class_",s)
+
+    ### Compute within-class choice probabilities using MNL model
+    P[[paste0("Class_",s)]] = apollo_mnl(mnl_settings, functionality)
+
+    ### Take product across observation for same individual
+    P[[paste0("Class_",s)]] = apollo_panelProd(P[[paste0("Class_",s)]], apollo_inputs ,functionality)
+  }
+
+  ### Compute latent class model probabilities
+  lc_settings   = list(inClassProb = P, classProb=pi_values)
+  P[["model"]] = apollo_lc(lc_settings, apollo_inputs, functionality)
+
+  ### Prepare and return outputs of function
+  P = apollo_prepareProb(P, apollo_inputs, functionality)
+  return(P)
+}
+
+# ################################################################# #
+#### MODEL ESTIMATION                                            ####
+# ################################################################# #
+
+### Optional starting values search
+# apollo_beta=apollo_searchStart(apollo_beta, apollo_fixed,apollo_probabilities, apollo_inputs)
+
+model = apollo_estimate(apollo_beta, apollo_fixed, apollo_probabilities, apollo_inputs ,estimate_settings =list(estimationRoutine="bhhh" , hessianRoutine= "maxLik"))
+
+# ################################################################# #
+#### MODEL OUTPUTS                                               ####
+# ################################################################# #
+
+# ----------------------------------------------------------------- #
+#---- FORMATTED OUTPUT (TO SCREEN)                               ----
+# ----------------------------------------------------------------- #
+
+apollo_modelOutput(model)
+
+