Testing a Three Way IPF with combined margins and separate ones (using the mipfp library)

ThreeWayIPF.R

#Testing a three way IPF with combined and individual marginals.

library(mipfp)
library(dplyr)

pers <- data_frame(
  gender = sample(c("m","f"),25,replace = T),
  age = rep(1:5,5),
  fpl = rep((5:1)/0.5,5),
  w = rep(1,25)
)

pers <- pers %>%
  mutate(gender = factor(gender)) %>%
  mutate(ageCat = standardBreakCut(age,c(-Inf,2,4,Inf),offsetLabels = F)) %>%
  mutate(fplCat = standardBreakCut(fpl,c(-Inf,5,Inf),offsetLabels = T)) 

persCells <- xtabs(data = pers, w ~ gender + ageCat + fplCat)

probs <- data_frame(
  gender = c("m","m","m","f","f","f"),
  ageCat = c("<2","3-4","5+","<2","3-4","5+"),
  fplCat = c("<5","5+","<5","5+","<5","5+"),
  w = (1:6) * 3
)

gendProbs <- xtabs(data = probs, w ~ gender)
ageProbs <- xtabs(data = probs, w ~ ageCat)
fplProbs <- xtabs(data = probs, w ~ fplCat)

probCells <- xtabs(data = probs, w ~ gender + ageCat + fplCat)

dimnames(persCells)
dimnames(probCells)

# Using individual marginal distributions ----------------------------------------------------------------
targetDistributions <- list(gendProbs, ageProbs, fplProbs)
targetDistributionVariableOrder <- list(1,2,3)
set.seed(pi)
#CAUTION: TAKE CARE TO ENSURE THAT THE VARIABLES USED HERE ARE IN THE CORRECT ORDER
result <- Estimate(seed = persCells,
                   target = targetDistributions,
                   target.list = targetDistributionVariableOrder,
                   print = T)

print(summary.mipfp(result))

# Using joint/combined marginal distributions ----------------------------------------------------------------
targetDistributions <- list(probCells)
targetDistributionVariableOrder <- list(c(1:3))
set.seed(pi)
#CAUTION: TAKE CARE TO ENSURE THAT THE VARIABLES USED HERE ARE IN THE CORRECT ORDER
result <- Estimate(seed = persCells,
                   target = targetDistributions,
                   target.list = targetDistributionVariableOrder,
                   print = T)

summary(result) #Works in this example, but with more complicated examples, it fails with a weird error.