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.
# latest version on CRAN
install.packages("frscore")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 norm.score
# 1 R A+B+F*g<->R 1.0000000 1.0000000 4 TRUE 38 2 1.00000000
# 2 R A+B<->R 1.0000000 0.9230769 2 TRUE 28 12 0.73684211
# 3 R A+B+F<->R 0.9285714 1.0000000 3 TRUE 24 2 0.63157895
# .
# .
# .
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 norm.score
# 1 A+B+F*g<->R 38 2 1.00000000
# 2 A+B<->R 28 12 0.73684211
# .
# .