Calculate the Fit-Robustness of CNA-Solutions

Automatically perform a reanalysis series on a data set using CNA, and calculate the fit-robustness of the resulting models, as described in Parkkinen and Baumgartner (2021) .

frscore

Overview

Functions for automatically performing a reanalysis series on a data set using cna::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, or simply count (causal) sub- and supermodel relations in a set of models for any reason, frscore() does this. causal_submodel() is a generalization of cna::is.submodel() that checks whether all causal relevance ascriptions, rather than only ascriptions of direct causation, made by one model are contained in another model. causal_submodel() is used by default in frscored_cna() and frscore() to calculate fr-scores, but the user can change this to cna::is.submodel() to obtain a moderate speed improvement if needed.

Have a look at the NEWS for information about recent changes and developments.

Installation

# latest version on CRAN

install.packages("frscore")

Usage

library(frscore)

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

rean_cna(ct2df(selectCases("A+B+F*g<->R")), attempt = seq(1, 0.7, -0.1))

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

target <- "(A+B<->C)*(C+D<->E)"
candidate <- "A+B<->E"
causal_submodel(candidate, target)