The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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.
You can install the development version from GitHub with:
# install.packages("devtools")
devtools::install_github("statuser/RGremlinsConjoint")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: 50These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.