Nothing
## ---- include = FALSE---------------------------------------------------------
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
## ----setup--------------------------------------------------------------------
suppressPackageStartupMessages({
library(ipfr)
library(dplyr)
})
## ---- warning=TRUE------------------------------------------------------------
hh_seed <- tibble(
geo_region = 1,
id = c(1:8),
hhsiz = c(1, 1, 1, 2, 2, 2, 2, 2),
hhveh = c(0, 2, 1, 1, 1, 2, 1, 0)
)
hh_targets <- list()
hh_targets$hhsiz <- tibble(
geo_region = 1,
`1` = 35,
`2` = 65
)
hh_targets$hhveh <- tibble(
geo_region = 1,
`0` = 100,
`1` = 100,
`2` = 100
)
result <- ipu(hh_seed, hh_targets, max_iterations = 30, verbose = TRUE)
## -----------------------------------------------------------------------------
result$primary_comp
## ----balance example inputs---------------------------------------------------
result <- setup_arizona()
hh_seed <- result$hh_seed
hh_targets <- result$hh_targets
per_seed <- result$per_seed
per_targets <- result$per_targets
avg_hh_weight <- (rowSums(hh_targets$hhtype) - 1) / nrow(hh_seed)
avg_per_weight <- (rowSums(per_targets$pertype) - 1) / nrow(per_seed)
## -----------------------------------------------------------------------------
new_per_targets <- per_targets
new_per_targets$pertype <- per_targets$pertype %>%
mutate_at(
.vars = vars(`1`, `2`, `3`),
.funs = list(~. * 2)
)
result <- ipu(hh_seed, hh_targets, per_seed, new_per_targets, max_iterations = 30)
## -----------------------------------------------------------------------------
result$weight_dist
## -----------------------------------------------------------------------------
result_80 <- ipu(
hh_seed, hh_targets, per_seed, new_per_targets,
max_iterations = 30,
secondary_importance = .80
)
result_80$weight_dist
result_80$primary_comp
result_80$secondary_comp
## -----------------------------------------------------------------------------
result_20 <- ipu(
hh_seed, hh_targets, per_seed, new_per_targets,
max_iterations = 30,
secondary_importance = .20
)
result_20$weight_dist
result_20$primary_comp
result_20$secondary_comp
## -----------------------------------------------------------------------------
hh_seed <- tibble(
id = c(1, 2, 3, 4),
siz = c(1, 2, 2, 1),
weight = c(1, 1, 1, 1),
geo_cluster = c(1, 1, 2, 2)
)
hh_targets <- list()
hh_targets$siz <- tibble(
geo_cluster = c(1, 2),
`1` = c(75, 100),
`2` = c(25, 150)
)
result <- ipu(hh_seed, hh_targets, max_iterations = 10,
max_ratio = 1.2, min_ratio = .8)
## -----------------------------------------------------------------------------
result$weight_dist
## -----------------------------------------------------------------------------
result$primary_comp
## -----------------------------------------------------------------------------
hh_targets <- list()
hh_targets$siz <- tibble(
geo_cluster = c(1, 2),
`1` = c(100000, 100),
`2` = c(10, 150)
)
result <- ipu(hh_seed, hh_targets, max_iterations = 10,
max_ratio = 5, min_ratio = .2)
result$weight_tbl
result$primary_comp
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