to_rtmpt_data: Transform data to be used in RT-MPT model fitting

In rtmpt: Fitting RT-MPT Models

Transform data to be used in RT-MPT model fitting

Description

Transform data, such that it can be used in fit_rtmpt. This implies changing each value/label in "subj", "group", "tree", and "cat" to numbers such that it starts from zero (e.g. data$tree = c(1,1,3,3,2,2,...) will be changed to data$tree = c(0,0,2,2,1,1,...)) and the columns will be ordered in the right way. "rt" must be provided in milliseconds. If it has decimal places it will be rounded to a whole number. fit_rtmpt will automatically call this function if its input is not already an rtmpt_data list, but it is advised to use it anyway because it provides information about the transformations of the data.

Usage

to_rtmpt_data(raw_data, model)

Arguments

raw_data	data.frame or path to data containing columns "subj", "group", "tree", "cat", and "rt". If not provided in this order it will be reordered and unused variables will be moved to the end of the new data frame.
model	A list of the class rtmpt_model.

Value

A list of the class rtmpt_data containing transformed data and information about the transformation that has been done.

Author(s)

Raphael Hartmann

Examples

####################################################################################
# Detect-Guess variant of the Two-High Threshold model.
# The encoding and motor execution times are assumed to be equal for each response.
####################################################################################

eqn_2HTM <- "
# CORE MPT EQN
# tree ; cat  ; mpt
target ; hit  ; do
target ; hit  ; (1-do)*g
target ; miss ; (1-do)*(1-g)
       
  lure ;  f_a ; (1-dn)*g
  lure ;  c_r ; dn
  lure ;  c_r ; (1-dn)*(1-g)
"

model <- to_rtmpt_model(eqn_file = eqn_2HTM)

data_file <- system.file("extdata/labeled_data.txt", package="rtmpt")
data <- read.table(file = data_file, header = TRUE)

data_list <- to_rtmpt_data(raw_data = data, model = model)
data_list 

rtmpt documentation built on April 10, 2022, 5:05 p.m.