EIFA Urban Ipixuna: fitting

Load required libraries and data

rm(list = ls())
library(day2day)
library(dplyr)

#> 
#> Attaching package: 'dplyr'

#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag

#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union

library(mirt)

#> Loading required package: stats4

#> Loading required package: lattice

library(sf)

#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUE

path_main <- git_path()
path_data <- file.path(path_main, "data")
path_raw <- file.path(path_data, "raw")
path_processed <- file.path(path_data, "processed")
path_modelled <- file.path(path_data, "modelled")

fidata <- file.path(path_processed, "fi-items-ipixuna-urban.gpkg") |>
    st_read(as_tibble = TRUE)

#> Reading layer `fi-items-ipixuna-urban' from data source 
#>   `/home/rstudio/documents/projects/food-insecurity-mapping/data/processed/fi-items-ipixuna-urban.gpkg' 
#>   using driver `GPKG'
#> Simple feature collection with 200 features and 36 fields
#> Geometry type: POINT
#> Dimension:     XY
#> Bounding box:  xmin: -71.70038 ymin: -7.06058 xmax: -71.68109 ymax: -7.03724
#> Geodetic CRS:  WGS 84

Exploratory items factor analysis

First, we filter only the items

items_data <- fidata |>
    st_set_geometry(NULL) |>
    dplyr::select(matches("^item_[0-9]+_[A-D]"))

Run the exploratory item factor analysis with 1 to 6 dimensions.

eifa_dim <- function(x) {
    mirt(items_data, x, method = "MHRM", technical = list(NCYCLES = 20000),
         verbose = FALSE)
}

mirt_eifa <- tibble(ndim = 1:6) |>
    mutate(model = purrr::map(ndim, eifa_dim))

Save output

Save output as a tibble containing number of dimensions and associated eifa models.

saveRDS(mirt_eifa, file = file.path(path_modelled, "eifa-ipixuna-urban.rds"))

Time to execute the task

Only useful when executed with Rscript.

proc.time()

#>    user  system elapsed 
#> 462.376 756.077 311.912