Nathaniel Phillips, University of Basel
Department Presentation, University of Zurich, Department of Economics
Rotjan, R. D., Chabot, J. R., & Lewis, S. M. (2010). Social context of shell acquisition in Coenobita clypeatus hermit crabs. Behavioral Ecology, 21(3), 639–646.
Method | Availability | Documentation | Accuracy |
---|---|---|---|
Don't share | Low | Low | Low |
By request only | Medium | Low | Low |
Post data online | High | Medium | Medium |
Markdown + R package | High | High | High |
All data analyses, and data descriptions are stored in an R manuscript package called phillips2014rivals
available in a package phillips2014rivals_0.1.0.tar
at https://goo.gl/q6GvBk
# Install the phillips2014rivals R package
# Package file: phillips2014rivals_0.1.0.tar,
# Link to package file: https://goo.gl/q6GvBk
install.packages("https://goo.gl/q6GvBk",
repos = NULL,
type = "source")
Compensatory | Non-Compensatory | |
---|---|---|
Example | Weighted averaging: Expected utility, Tally, Bayes | Heuristics: Take the Best, Tit-for-tat |
Information Requirements | High | Low |
Search | Comprehensive | Sequential |
Speed | Slow | Fast |
When do people use? (Payne, Bettman, Johnson, 1993) |
Low time pressure, high processing capacity | High time-pressure, low processing capacity |
Neth et al. (2014). "Homo heuristicus in the financial world".
FFTrees
An easy-to-use R package to create, visualize, and implement fast and frugal decision trees.# Install FFTrees to R.
install.packages("FFTrees")
# Creating a regression decision model with glm()
patient.glm <- glm(formula = decision ~ .,
data = patient.data,
family = "binomial")
Df | F value | Pr(>F) | ||
---|---|---|---|---|
socsit | 1 | 6 | 8.604 | 0.000 |
schoolqual | 2 | 6 | 5.076 | 0.000 |
migration | 3 | 6 | 3.166 | 0.005 |
transfac | 4 | 14 | 2.539 | 0.002 |
withdr | 5 | 1 | 10.526 | 0.001 |
offense | 6 | 10 | 3.154 | 0.001 |
sentence | 7 | 1 | 10.880 | 0.001 |
prisonprior | 8 | 1 | 4.578 | 0.033 |
raext | 9 | 3 | 8.060 | 0.000 |
migration2 | 10 | 1 | 4.256 | 0.040 |
# Step 0: Install FFTrees
install.packages("FFTrees")
# Step 1: Load the package
library("FFTrees")
# Step 2: Create an fft decision model with FFTrees
patient.fft <- FFTrees(formula = decision ~.,
data = patient.data)
# Show the cue accuracies
plot(patient.fft, what = "cues", main = "Patient cues")
plot(patient.fft, main = "Release Decision FFT", stats = FALSE)
plot(patient.fft)
FFTrees
package can be used with any dataset with a binary criterion.heart.fft <- FFTrees(diagnosis ~ ., data = heartdisease)
mushrooms.fft <- FFTrees(poisonous ~ ., data = mushrooms)
FFTrees
R package, you can create descriptive or prescriptive fast and frugal trees and compare to compensatory models.
Joerg Rieskamp (University of Basel)
Ralph Hertwig (MPI for Human Development)
Yaakov Kareev (Hebrew University of Jerusalem)
Judith Avrahami (Hebrew University of Jerusalem)
Wolfgang Gaissmaier (University of Konstanz)
Hansjoerg Neth (University of Konstanz)
Jan Woike (MPI for Human Development)
install.packages("FFTrees")
install.packages("yarrr")
Full tutorial: http://rpubs.com/ndphillips/rpackagescience
/data
, /R
, /vignettes
, /inst
DESCRIPTION.txt
./data
and /inst
folders, all R code in /R
/data
/vignettes
build()
function.
phillips2014rivals_0.1.0.tar
FFTs are very cheap to implement
Heart disease data