From 3eeb6820cd7bf13a18930671438679ab74a26e69 Mon Sep 17 00:00:00 2001
From: Julian Sagebiel <julian.sagebiel@idiv.de>
Date: Fri, 21 Jun 2024 23:49:29 +0200
Subject: [PATCH] added tests for decisiongroups and latent class model
---
inst/extdata/spdesigns/designs/twoattr.RDS | Bin 0 -> 631 bytes
inst/extdata/spdesigns/twoattr.R | 26 ++
tests/manual-tests/test_decisiongroups.R | 273 +++++++++++++++++++++
3 files changed, 299 insertions(+)
create mode 100644 inst/extdata/spdesigns/designs/twoattr.RDS
create mode 100644 inst/extdata/spdesigns/twoattr.R
create mode 100644 tests/manual-tests/test_decisiongroups.R
diff --git a/inst/extdata/spdesigns/designs/twoattr.RDS b/inst/extdata/spdesigns/designs/twoattr.RDS
new file mode 100644
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diff --git a/inst/extdata/spdesigns/twoattr.R b/inst/extdata/spdesigns/twoattr.R
new file mode 100644
index 0000000..ea90f0f
--- /dev/null
+++ b/inst/extdata/spdesigns/twoattr.R
@@ -0,0 +1,26 @@
+
+
+# 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")
diff --git a/tests/manual-tests/test_decisiongroups.R b/tests/manual-tests/test_decisiongroups.R
new file mode 100644
index 0000000..76c3511
--- /dev/null
+++ b/tests/manual-tests/test_decisiongroups.R
@@ -0,0 +1,273 @@
+
+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)
+
+
--
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