Nothing
rob.fit <-
function(test_sol, initial_sol, outcome)
{
all_sols = list()
if ("list" %in% class(test_sol)){all_sols <- test_sol}
else{all_sols[[1]] <- test_sol}
all_sols[[length(all_sols)+1]] <- initial_sol
rcf <- rob.corefit(test_sol, initial_sol, outcome)
isf <- QCAfit(initial_sol, outcome, necessity = FALSE)
rob_cons <- isf["solution_formula",1]/rcf[1]
rob_cov <- rcf[2]/isf["solution_formula",2]
if ("list" %in% class(test_sol))
{
P2 <- pimdata(results = test_sol[[1]], outcome = outcome)
for (i in length(test_sol))
{
Pi <- pimdata(results = test_sol[[i]], outcome = outcome)
P2$solution_formula <- pmin(Pi$solution_formula, P2$solution_formula)
}
}
else {
P2 <- pimdata(results = test_sol, outcome = outcome)
}
if ("list" %in% class(test_sol))
{
P3 <- pimdata(results = test_sol[[1]], outcome = outcome)
for (j in length(test_sol))
{
Pj <- pimdata(results = test_sol[[j]], outcome = outcome)
P3$solution_formula <- pmax(Pj$solution_formula, P3$solution_formula)
}
}
else {
P3 <- pimdata(results = test_sol, outcome = outcome)
}
P1 <- pimdata(results = initial_sol, outcome = outcome)
rob_sc_min <- sum(pmin(P1$solution_formula, P2$solution_formula))/
sum(pmax(P1$solution_formula, P2$solution_formula))
rob_sc_max <- sum(pmin(P1$solution_formula, P3$solution_formula))/
sum(pmax(P1$solution_formula, P3$solution_formula))
ND <- rob.evaluation(test_sol = test_sol,
initial_sol = initial_sol,
outcome=outcome)
rank <- robust.rank(test_sol = test_sol,
initial_sol = initial_sol,
outcome=outcome)
#SSrel <- sum(ND$'S1*s2'>0.5)*sum(ND$'s1*S2' >0.5)
#SSrel <- ifelse(SSrel==0, TRUE, FALSE)
#rob <- data.frame("Robustness_Fit"= c(rob_cov, rob_cons, rob_sc, SSrel))
rob <- data.frame("Robustness_Fit"= c(rob_cov, rob_cons, rob_sc_min, rob_sc_max))
rob <- t(rob)
colnames(rob) <- c("RF_cov","RF_cons","RF_SC_minTS","RF_SC_maxTS")
rob[,1:4] <- round(rob[,1:4], digits = 3)
#rob[,4] <- as.logical(rob[,4])
return(rob)
}
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