Household Demography model

1. loading simulation data

endStates <- read.csv("output/3-householdDemography v.1.1 - kinship tabu for new couples exp-endstates-table.csv", skip = 6)

2. Detecting survival populations and factors

hist(endStates$totalIndividuals)

Initial household population

This parameter has slight positive effect on population survival:

summary(endStates$initialNumHouseholds[endStates$totalIndividuals == 0])

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    6.00   12.00   12.64   19.00   25.00

summary(endStates$initialNumHouseholds[endStates$totalIndividuals > 0])

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    8.00   14.00   13.81   20.00   25.00

boxplot(endStates$initialNumHouseholds ~ (endStates$totalIndividuals > 0))

Mortality factors

Coale-Demeny Life Tables Model level parameter has great effect:

summary(endStates$cdmlt.level[endStates$totalIndividuals == 0])

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00    5.00   10.00   11.19   17.00   25.00

summary(endStates$cdmlt.level[endStates$totalIndividuals > 0])

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
   1.00   13.00   18.00   17.05   22.00   25.00

boxplot(endStates$cdmlt.level ~ (endStates$totalIndividuals > 0))

but region parameter has a smaller effect. East tends to survive more often.

table(endStates$coale.demeny.region, endStates$totalIndividuals > 0)

       
        FALSE TRUE
  east    806  462
  north   852  416
  south   834  405
  west    822  403

boxplot(data = endStates, totalIndividuals ~ coale.demeny.region)

Fertility factors

All have a visible effect, but particularly c1.fert and sigma1.fert.

layout(matrix(1:4, nrow = 2, byrow = T))
boxplot(endStates$c1.fert ~ (endStates$totalIndividuals > 0), main = "c1.fert")
boxplot(endStates$mu.fert ~ (endStates$totalIndividuals > 0), main = "mu.fert")

boxplot(endStates$sigma1.fert ~ (endStates$totalIndividuals > 0), main = "sigma1.fert")
boxplot(endStates$sigma2.fert ~ (endStates$totalIndividuals > 0), main = "sigma2.fert")

Nuptiality factors

Residential rule showing no clear effect:

table(endStates$residence.rule, endStates$totalIndividuals > 0)

                        
                         FALSE TRUE
  matrilocal-matrilineal  1636  862
  patrilocal-patrilineal  1678  824

The effect of the acceptable kinship degree is also unclear:

boxplot(endStates$acceptable.kinship.degree.for.couples ~ (endStates$totalIndividuals > 0))

All nuptiality parameters for both men and women present a small, yet apparently consistent effect. c1 and sigma parameters have a positive effect while mu has a smaller negative effect.

layout(matrix(1:8, nrow = 2, byrow = T))
boxplot(endStates$c1.women ~ (endStates$totalIndividuals > 0), main = "c1.women")
boxplot(endStates$mu.women ~ (endStates$totalIndividuals > 0), main = "mu.women")

boxplot(endStates$sigma1.women ~ (endStates$totalIndividuals > 0), main = "sigma1.women")
boxplot(endStates$sigma2.women ~ (endStates$totalIndividuals > 0), main = "sigma2.women")

boxplot(endStates$c1.men ~ (endStates$totalIndividuals > 0), main = "c1.men")
boxplot(endStates$mu.men ~ (endStates$totalIndividuals > 0), main = "mu.men")

boxplot(endStates$sigma1.men ~ (endStates$totalIndividuals > 0), main = "sigma1.men")
boxplot(endStates$sigma2.men ~ (endStates$totalIndividuals > 0), main = "sigma2.men")

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