frscore

R-CMD-check

Overview

Functions for automatically performing a reanalysis series on a data set using CNA, and for calculating the fit-robustness of the resulting models, as described in Parkkinen and Baumgartner (2021): https://journals.sagepub.com/doi/full/10.1177/0049124120986200.

In the most common use case, one wants to obtain a set of models and their respective fit-robustness scores given a range of consistency and coverage values that determine a reanalysis series of the data set of interest. The function frscored_cna() runs the reanalysis series on a data set and calculates the fit-robustness scores of the recovered models in one go. If one only wishes to repeatedly analyze a data set with different consistency and coverage thresholds in a given range, rean_cna() automates this. If one wishes to calculate the fit-robustness scores for an existing set of models, frscore() does this.

Installation

# latest version on CRAN

install.packages("frscore")

Usage

library(frscore)

frsc <- frscored_cna(ct2df(selectCases("A+B+F*g<->R")))
frsc

# FR-scored reanalysis series with fit range 1 to 0.7 with granularity 0.1 
# Score type: full || score normalization: truemax 
# maxsols set to 50 -- 0 model types excluded from scoring 
# 
# ----- 
#  
# Model types: 
#  
#    outcome   condition consistency  coverage complexity inus      score tokens
# 1  R       A+B+F*g<->R   1.0000000 1.0000000          4 TRUE 1.00000000      2
# 2  R           A+B<->R   1.0000000 0.9230769          2 TRUE 0.73684211     12
# 3  R         A+B+F<->R   0.9285714 1.0000000          3 TRUE 0.63157895      2
# .
# .
# .


rean_cna(ct2df(selectCases("A+B+F*g<->R")), attempt = seq(1, 0.7, -0.1))
# [[1]]
#   outcome         condition consistency coverage complexity inus cnacon cnacov
# 1 R       A + B + F*g <-> R           1        1          4 TRUE      1      1
# 
# [[2]]
#   outcome         condition consistency coverage complexity inus cnacon cnacov
# 1 R       A + B + F*g <-> R           1        1          4 TRUE    0.9      1
# .
# .


res <- rean_cna(selectCases("A+B+F*g<->R"), attempt = seq(1, 0.7, -0.1))
res <- do.call(rbind, res)

fr <- frscore(res[,2])
fr

# FRscore, score type: full || score normalization: truemax 
# 
# maxsols set to 50 -- 0 solution types excluded from scoring 
# 
# -----
#  
# Model types: 
# 
#          model      score tokens
# 1  A+B+F*g<->R 1.00000000      2
# 2      A+B<->R 0.73684211     12
# .
# .