MyNixOS website logo
Description

Estimate the "Gremlins in the Data" Model for Conjoint Studies.

The tools and utilities to estimate the model described in "Gremlin's in the Data: Identifying the Information Content of Research Subjects" (Howell et al. (2021) <doi:10.1177/0022243720965930>) using conjoint analysis data such as that collected in Sawtooth Software's 'Lighthouse' or 'Discover' products. Additional utilities are included for formatting the input data.

RGremlinsConjoint

R-CMD-check R-CMD-check

The gremlins package provides the tools and utilities to estimate a the model described in “Gremlins in the Data: Identifying the Information Content of Research Subjects” ([https://doi.org/10.1177/0022243720965930]) using conjoint analysis data such as that collected in Sawtooth Software’s Lighthouse or Discover Products. The packages also contains utility functions for formatting the input data and extracting the relevant results.

Installation

You can install the development version from GitHub with:

# install.packages("devtools")
devtools::install_github("statuser/RGremlinsConjoint")

Example

The package exposes basically one function You can use it like:

library(RGremlinsConjoint)

# Read in the data
truck_design_file <- system.file("extdata", "simTruckDesign.csv", package = "RGremlinsConjoint")
truck_data_file <- system.file("extdata", "simTruckData.csv", package = "RGremlinsConjoint")
truckDesign <- read.csv(truck_design_file)
truckData <- read.csv(truck_data_file)

# Covert the design file to be dummy coded is necessary
# The simulated data is already coded
# codedTruck <- code_sawtooth_design(truckDesign, c(4:9), include_none_option=TRUE)

outputSimData_burn <- estimateGremlinsModel(truckData,
                                            truckDesign,
                                            R = 10,
                                            keepEvery = 1,
                                            num_lambda_segments = 2)
#> Finding Starting Values
#> Beginning MCMC Routine
#> Completing iteration :  1 
#> Accept rate slopes:  0 
#> Accept rate lambda:  0 
#> Mu_adapt lambda:     50 
#> Gamma_adapt lambda:  10 
#> metstd lambda:       10 
#> current lambda:      50
Metadata

Version

0.9.1

License

Unknown

Platforms (75)

    Darwin
    FreeBSD
    Genode
    GHCJS
    Linux
    MMIXware
    NetBSD
    none
    OpenBSD
    Redox
    Solaris
    WASI
    Windows
Show all
  • aarch64-darwin
  • aarch64-genode
  • aarch64-linux
  • aarch64-netbsd
  • aarch64-none
  • aarch64_be-none
  • arm-none
  • armv5tel-linux
  • armv6l-linux
  • armv6l-netbsd
  • armv6l-none
  • armv7a-darwin
  • armv7a-linux
  • armv7a-netbsd
  • armv7l-linux
  • armv7l-netbsd
  • avr-none
  • i686-cygwin
  • i686-darwin
  • i686-freebsd
  • i686-genode
  • i686-linux
  • i686-netbsd
  • i686-none
  • i686-openbsd
  • i686-windows
  • javascript-ghcjs
  • loongarch64-linux
  • m68k-linux
  • m68k-netbsd
  • m68k-none
  • microblaze-linux
  • microblaze-none
  • microblazeel-linux
  • microblazeel-none
  • mips-linux
  • mips-none
  • mips64-linux
  • mips64-none
  • mips64el-linux
  • mipsel-linux
  • mipsel-netbsd
  • mmix-mmixware
  • msp430-none
  • or1k-none
  • powerpc-netbsd
  • powerpc-none
  • powerpc64-linux
  • powerpc64le-linux
  • powerpcle-none
  • riscv32-linux
  • riscv32-netbsd
  • riscv32-none
  • riscv64-linux
  • riscv64-netbsd
  • riscv64-none
  • rx-none
  • s390-linux
  • s390-none
  • s390x-linux
  • s390x-none
  • vc4-none
  • wasm32-wasi
  • wasm64-wasi
  • x86_64-cygwin
  • x86_64-darwin
  • x86_64-freebsd
  • x86_64-genode
  • x86_64-linux
  • x86_64-netbsd
  • x86_64-none
  • x86_64-openbsd
  • x86_64-redox
  • x86_64-solaris
  • x86_64-windows