README.md

In JanaJarecki/cognitiveutils: Utility functions for cognitive science and cognitive modeling

cogsciutils

An R-package with helper functions for social scientists and cognitive psychologists. Note, the package is currently being developed (bleeding edge).

What you can do

• Goodness of fit gof() is general function to calculate different goodness-of-fit measures, e.g.
• Loglikelihood, see also Loglikelihood()
• Squared error measures, see also MSE(), SSE()
• Percent accuracy, see also Accuracy()
• and more.
• Choice rules choicerule() is a general function to apply choice rules (action selection rules) to predictions, e.g.,
• Soft maximum, see alsocr_softmax()
• Luce's rule, see also cr_luce()
• Argmax, see also cr_argmax()
• Epsilon greedy, see also cr_epsilon()
• and more.
• Demographics participants() is a function to nicely print demographic summary sections of your data

Installation

First time installation

library(devtools) #maybe install.packages('devtools')
devtools::install_github('janajarecki/cogsciutils')

Usage

library(cogsciutils)

Participants

The paricipants() function summarizes demographic data and prints a text like this

participants(mydata, id = "id", age = "age", gender = "fem", excl = 0)

In total ten participants completed the study (zero were excluded), 6 males and 4 females (60% and 40%, respectively), mean age 50 years (med = 51, sd = 18, range 21-73 years).

Details of participants()

# Let's make sample data
set.seed(232)
N <- 10
mydata <- data.frame(id = 1:N,
                    age = sample(18:75, N),
                    fem = sample(c("m","f"), N, T))
head(mydata, 3)
#   id age fem
# 1  1  73   f
# 2  2  71   m
# 3  3  23   f
participants(mydata, id = "id", age = "age", gender = "fem", excl = 0)

Further usage

• Save the ouput: file = "filename.txt" R participants(mydata, "id", "age", "fem", excl = 0, file = "test.txt")
• Did you exclude participants? excl = 2 R participants(mydata, "id", "age", "fem", excl = 2) # Or specify, for each participant, the reasons and NA if included mydata$exclude_because <- c("no variance on scales", "inattentiveness", rep(NA, 8)) participants(mydata, "id", "age", "fem", excl = "exclude_because")
• Print location(s): collectedat = "..." R participants(mydata, "id", "age", "fem", excl = 0, collectedat = "the University of Basel") participants(mydata, "id", "age", "fem", excl = 0, collectedat = "online")
• Add ethics approval: approvedby = "..." R participants(mydata, "id", "age", "fem", excl = 0, approvedby = "the University of Basel ethics comittee")
• Add the time of data collection: date = "variable_name" R mydata$date <- Sys.Date() + 1:N # make a mock date variable participants(mydata, "id", "age", "fem", excl = 0, date = "date")
• Add a payoff summary: compensation = "variable_name" R mydata$payout <- runif(N) * 5 # Payout participants(mydata, "id", "age", "fem", excl = 0, compensation = "payout") participants(mydata, "id", "age", "fem", excl = 0, compensation = "payout", currency = "EUR")
• Add further variables to be summarized: more = list(x = c(...)) R mydata$duration_min <- sample(20:30,N) # study duration mydata$income_eur <- c(sample(1200:4000,N-1), NA) # we have one missing value! mydata$income_categ <- c(sample(c("1000-2000", "2000-3000", "> 3000"), N, T)) # use the more argument # The format is a list of vectors c("what to print" = c("variable_name", "unit")) participants(mydata, "id", "age", "fem", excl = 0, more = list("completion duration was" = c("duration_min", "minute"))) participants(mydata, "id", "age", "fem", excl = 0, more = list("completion duration was" = c("duration_min", "minute"), "income was" = c("income_eur", "EUR"))) participants(mydata, "id", "age", "fem", excl = 0, more = list("completion duration was" = c("duration_min", "minute"), "income categories were" = c("income_categ", "EUR")))
• Alltogether R participants(mydata, "id", "age", "fem", excl = "exclude_because", date ="date", compensation = "payout", currency = "EUR", recruitedfrom ="Amazon MTurk", collectedat ="online", approvedby = "the IRB at the University of Basel", more = list("completion duration was" = c("duration_min", "minute"), "income categories were" = c("income_categ", "EUR")))

  In total ten participants recruited from Amazon MTurk completed an online study, two were excluded (for no variance on scales and inattentiveness), leaving a final sample of N = 8; 5 males and 3 females (62% and 38%, respectively), mean age 53 years (med = 50, sd = 13, range 35-73 years), mean remuneration was 3.2 EUR (med = 3.2, sd = 0.9, range 1.7-4.3 EUR), data were collected in March 2019, the study was approved by the IRB at the University of Basel. Mean completion duration was 25.1 minutes (med = 25.5, sd = 3.0, range 20.0-29.0 minutes); income categories were n = 4 > 3000 and 4 2000-3000 (50% and 50%, respectively).

Choice rules

Binary data: from a vector

Calculate Luce choice rule

library(cogsciutils)
# Binary predictions
binaryPredictions <- c(.22, .5, .73)
choicerule(binaryPredictions, "softmax", tau = 2)
choicerule(binaryPredictions, "argmax")
choicerule(binaryPredictions, "epsilon", eps = .2)

Predictions for three or more variables: from a matrix

# Make some predictions for three variables A, B, C
predictions <- cbind(A = c(.1,.5,.4), B = c(.3,.1,.4), C = c(.6, .4, .2))
choicerule(predictions, "luce")
choicerule(predictions, "argmax")
choicerule(predictions, "epsilon", eps = .2)

Goodness of fit

Fit from raw observed data

Calculate the log likelihood for some observatinos

library(cogsciutils)

# N = 100 observations
set.seed(121)
x <- sample(0:1, 100, rep = T) # observations of 0s and 1s
y <- rep(0.55, 100) # predicting to observe 1 with probability 55 %
gof(obs = x, pred = y, type = "loglik") # log likelihood
gof(obs = x, pred = y, type = "mse") # mean squared error, average squared deviations
gof(obs = x, pred = y, type = "sse") # sum of squared errors, sum of squared deviations
gof(obs = x, pred = y, type = "loglik", saturated = TRUE) # saturated log lik

# Ignore first 5 observations
gof(obs = x, pred = y, type = "loglik", discount = 5)
gof(obs = x, pred = y, type = "loglik", saturated = TRUE, discount = 5) # saturated log lik

Fit from proportions observed of responses

Calculate the fit from the aggregated relative frequencies of responses. In this case you can also calculate the weighted sum of squares wsse and weighted mean squared error wmse. Weighting means predictions close to 0.5 are discounted in the fit, and predictions closer to 0 and 1 are more important. Weighting is done by dividing through the variance (var = p * (1-p)) of each unique prediction.

library(cogsciutils)

# Sum of squared errors (SSE) between predictions and observations
x <- c(.96, .78) # observations as proportions of responses (aggregated)
y <- c(.85, .95) # predictions for the response

# How is the goodness of fit?
gof(obs = x, pred = y, type = "loglik") # log likelihood
gof(obs = x, pred = y, type = "mse") # mean squared deviations
gof(obs = x, pred = y, type = "sse") # sum of squared deviations

# If you want the weighted SSE the function needs to know
# how many data points underly the observed proportions
n <- c(100, 100)
gof(obs = x, pred = y, type = "wmse", n = n) # mean squared deviations
gof(obs = x, pred = y, type = "wsse", n = n) # sum of squared deviations

JanaJarecki/cognitiveutils documentation built on Sept. 9, 2020, 9:11 a.m.