SPIFA Urban Ipixuna Season: model fitting
Perform spatial item factor analysis (SPIFA) by season using the results obtained from the exploratory item factor analysis (EIFA) with 3 latent dimensions.
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, unionlibrary(mirt)#> Loading required package: stats4#> Loading required package: latticelibrary(spifa)
library(sf)#> Linking to GEOS 3.10.2, GDAL 3.4.1, PROJ 8.2.1; sf_use_s2() is TRUElibrary(purrr)
library(ggplot2)
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 84eifa_data <- readRDS(file.path(path_modelled, "eifa-ipixuna-urban.rds"))Get items data and eifa model
# items
items_data <- fidata |>
dplyr::select(matches("^item_[0-9]+_[A-D]"), season)
items_data_dry <- items_data |>
filter(season == "dry")
items_data_wet <- items_data |>
filter(season == "wet")
# discrimination parameters with rotation
eifa_discrimiation <- summary(eifa_data$model[[3]], rotate = "varimax", suppres = 0.5)#>
#> Rotation: varimax
#>
#> Rotated factor loadings:
#>
#> F1 F2 F3 h2
#> item_1_A_worried_that_food_ends NA -0.639 NA 0.742
#> item_2_A_run_out_of_food NA NA -0.529 0.689
#> item_3_A_ate_few_food_types -0.761 NA NA 0.831
#> item_4_B_skipped_a_meal -0.764 NA -0.506 0.906
#> item_5_B_ate_less_than_required -0.630 NA -0.653 0.910
#> item_6_B_hungry_but_did_not_eat -0.683 NA -0.634 0.993
#> item_7_B_at_most_one_meal_per_day -0.736 NA NA 0.817
#> item_8_C_ate_few_food_types -0.836 NA NA 0.825
#> item_9_C_ate_less_than_required -0.858 NA NA 0.880
#> item_10_C_decreased_food_quantity -0.875 NA NA 0.926
#> item_11_C_skipped_a_meal -0.935 NA NA 0.985
#> item_12_C_hungry_but_did_not_eat -0.881 NA NA 0.949
#> item_13_C_at_most_one_meal_per_day -0.946 NA NA 0.994
#> item_14_D_food_just_with_farinha -0.568 NA -0.640 0.797
#> item_15_D_credit_for_eating NA -0.753 NA 0.668
#> item_16_D_borrowed_food NA -0.892 NA 0.990
#> item_17_D_had_meals_at_neighbors NA -0.552 NA 0.581
#> item_18_D_reduced_meat_or_fish -0.541 NA NA 0.634
#>
#> Rotated SS loadings: 8.571 3.31 3.235
#>
#> Factor correlations:
#>
#> F1 F2 F3
#> F1 1 0 0
#> F2 0 1 0
#> F3 0 0 1Dimensions, restrictions and priors
Let’s first define the dimensions and restrictions.
# general dimensions
q <- ncol(items_data)-2 # number of items
m <- 3 # number of latent factors
# restrictions on the discrimination parameters A for confirmatory analysis
L_a <- (abs(eifa_discrimiation$rotF) > 0.5) * 1
# prior for the discrimination parameters A
A_mean <- matrix(0, q, m)
A_mean[c(11, 13), 1] <- 1
A_mean[c(16), 2] <- 1
A_mean[c(14), 3] <- 1
A_sd <- matrix(1, q, m)
A_sd[A_mean == 1] <- 0.25
# model iterations
iter <- 1.5 * 10 ^ 6
thin <- 200Spatial item factor analysis 1GP
set.seed(123)
# Gaussian process parameters
ngp <- 1
W_gp <- diag(m)[, c(1), drop = FALSE]
phi_mean <- c(160)
phi_sd <- c(0.3)
system.time({
samples1_dry <- spifa(
response = item_1_A_worried_that_food_ends:item_18_D_reduced_meat_or_fish,
coords = geom,
data = items_data_dry, nfactors = 3, ngp = ngp, niter = iter, thin = thin,
constrains = list(A = L_a, W = W_gp, V_sd = 1),
A_opt = list(initial = A_mean, prior_mean = A_mean, prior_sd = A_sd),
R_opt = list(prior_eta = 3),
sigmas_gp_opt = list(initial = 0.4, prior_mean = 0.4, prior_sd = 0.4),
phi_gp_opt = list(initial = phi_mean , prior_mean = phi_mean, prior_sd = phi_sd)
)
})#> Standardixing#> user system elapsed
#> 43425.01 36964.10 10049.45saveRDS(samples1_dry, file = file.path(path_modelled, "spifa-ipixuna-urban-dry-1gp.rds"))
set.seed(123)
system.time({
samples1_wet <- spifa(
response = item_1_A_worried_that_food_ends:item_18_D_reduced_meat_or_fish,
coords = geom,
data = items_data_wet, nfactors = 3, ngp = ngp, niter = iter, thin = thin,
constrains = list(A = L_a, W = W_gp, V_sd = 1),
A_opt = list(initial = A_mean, prior_mean = A_mean, prior_sd = A_sd),
R_opt = list(prior_eta = 3),
sigmas_gp_opt = list(initial = 0.4, prior_mean = 0.4, prior_sd = 0.4),
phi_gp_opt = list(initial = phi_mean , prior_mean = phi_mean, prior_sd = phi_sd)
)
})#> Standardixing#> user system elapsed
#> 34745.66 29311.38 8007.70saveRDS(samples1_wet, file = file.path(path_modelled, "spifa-ipixuna-urban-wet-1gp.rds"))Spatial item factor analysis 2GP
set.seed(123)
# Gaussian process parameters
ngp <- 2
W_gp <- diag(m)[, c(1,3)]
phi_mean <- c(160, 80)
phi_sd <- c(0.3, 0.3)
system.time({
samples2_dry <- spifa(
response = item_1_A_worried_that_food_ends:item_18_D_reduced_meat_or_fish,
coords = geom,
data = items_data_dry, nfactors = 3, ngp = ngp, niter = iter, thin = thin,
constrains = list(A = L_a, W = W_gp, V_sd = 1),
A_opt = list(initial = A_mean, prior_mean = A_mean, prior_sd = A_sd),
R_opt = list(prior_eta = 3),
sigmas_gp_opt = list(initial = 0.4, prior_mean = 0.4, prior_sd = 0.4),
phi_gp_opt = list(initial = phi_mean , prior_mean = phi_mean, prior_sd = phi_sd)
)
})#> Standardixing#> user system elapsed
#> 42143.788 36760.506 9863.456saveRDS(samples2_dry, file = file.path(path_modelled, "spifa-ipixuna-urban-dry-2gp.rds"))
set.seed(123)
system.time({
samples2_wet <- spifa(
response = item_1_A_worried_that_food_ends:item_18_D_reduced_meat_or_fish,
coords = geom,
data = items_data_wet, nfactors = 3, ngp = ngp, niter = iter, thin = thin,
constrains = list(A = L_a, W = W_gp, V_sd = 1),
A_opt = list(initial = A_mean, prior_mean = A_mean, prior_sd = A_sd),
R_opt = list(prior_eta = 3),
sigmas_gp_opt = list(initial = 0.4, prior_mean = 0.4, prior_sd = 0.4),
phi_gp_opt = list(initial = phi_mean , prior_mean = phi_mean, prior_sd = phi_sd)
)
})#> Standardixing#> user system elapsed
#> 42280.957 37641.450 9990.836saveRDS(samples2_wet, file = file.path(path_modelled, "spifa-ipixuna-urban-wet-2gp.rds"))Spatial item factor analysis 3GP
set.seed(123)
# Gaussian process parameters
ngp <- 3
W_gp <- diag(m)
phi_mean <- c(160, 80, 80)
phi_sd <- c(0.3, 0.3, 0.3)
# hist(rlnorm(10000, log(160), 0.3), 100)
system.time({
samples3_dry <- spifa(
response = item_1_A_worried_that_food_ends:item_18_D_reduced_meat_or_fish,
coords = geom,
data = items_data_dry, nfactors = 3, ngp = ngp, niter = iter, thin = thin,
constrains = list(A = L_a, W = W_gp, V_sd = 1),
A_opt = list(initial = A_mean, prior_mean = A_mean, prior_sd = A_sd),
R_opt = list(prior_eta = 3),
sigmas_gp_opt = list(initial = 0.4, prior_mean = c(0.4, 0.2, 0.4), prior_sd = 0.4),
phi_gp_opt = list(initial = phi_mean , prior_mean = phi_mean, prior_sd = phi_sd)
)
})#> Standardixing#> user system elapsed
#> 29912.577 25265.346 6897.388saveRDS(samples3_dry, file = file.path(path_modelled, "spifa-ipixuna-urban-dry-3gp.rds"))
set.seed(123)
system.time({
samples3_wet <- spifa(
response = item_1_A_worried_that_food_ends:item_18_D_reduced_meat_or_fish,
coords = geom,
data = items_data_wet, nfactors = 3, ngp = ngp, niter = iter, thin = thin,
constrains = list(A = L_a, W = W_gp, V_sd = 1),
A_opt = list(initial = A_mean, prior_mean = A_mean, prior_sd = A_sd),
R_opt = list(prior_eta = 3),
sigmas_gp_opt = list(initial = 0.4, prior_mean = c(0.4, 0.2, 0.4), prior_sd = 0.4),
phi_gp_opt = list(initial = phi_mean , prior_mean = phi_mean, prior_sd = phi_sd)
)
})#> Standardixing#> user system elapsed
#> 29402.870 24327.251 6716.392saveRDS(samples3_wet, file = file.path(path_modelled, "spifa-ipixuna-urban-wet-3gp.rds"))Check models settings
Print the settings defined for the models.
attr(samples1_dry, "model_info")[-c(1:4)]#> $nobs
#> [1] 100
#>
#> $nitems
#> [1] 18
#>
#> $nfactors
#> [1] 3
#>
#> $ngp
#> [1] 1
#>
#> $niter
#> [1] 1500000
#>
#> $thin
#> [1] 200
#>
#> $standardize
#> [1] TRUE
#>
#> $constrain_L
#> F1 F2 F3
#> item_1_A_worried_that_food_ends 0 1 0
#> item_2_A_run_out_of_food 0 0 1
#> item_3_A_ate_few_food_types 1 0 0
#> item_4_B_skipped_a_meal 1 0 1
#> item_5_B_ate_less_than_required 1 0 1
#> item_6_B_hungry_but_did_not_eat 1 0 1
#> item_7_B_at_most_one_meal_per_day 1 0 0
#> item_8_C_ate_few_food_types 1 0 0
#> item_9_C_ate_less_than_required 1 0 0
#> item_10_C_decreased_food_quantity 1 0 0
#> item_11_C_skipped_a_meal 1 0 0
#> item_12_C_hungry_but_did_not_eat 1 0 0
#> item_13_C_at_most_one_meal_per_day 1 0 0
#> item_14_D_food_just_with_farinha 1 0 1
#> item_15_D_credit_for_eating 0 1 0
#> item_16_D_borrowed_food 0 1 0
#> item_17_D_had_meals_at_neighbors 0 1 0
#> item_18_D_reduced_meat_or_fish 1 0 0
#>
#> $constrain_T
#> [,1]
#> [1,] 1
#> [2,] 0
#> [3,] 0
#>
#> $constrain_V_sd
#> [,1]
#> [1,] 0.7856643
#> [2,] 0.9185840
#> [3,] 0.9075315
#>
#> $adap_Sigma
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.001 0.000 0.000 0.000 0.000
#> [2,] 0.000 0.001 0.000 0.000 0.000
#> [3,] 0.000 0.000 0.001 0.000 0.000
#> [4,] 0.000 0.000 0.000 0.001 0.000
#> [5,] 0.000 0.000 0.000 0.000 0.001
#>
#> $adap_scale
#> [1] 1
#>
#> $adap_C
#> [1] 0.7
#>
#> $adap_alpha
#> [1] 0.8
#>
#> $adap_accep_prob
#> [1] 0.234
#>
#> $c_initial
#> [1] -0.56047565 -0.23017749 1.55870831 0.07050839 0.12928774 1.71506499 0.46091621 -1.26506123
#> [9] -0.68685285 -0.44566197 1.22408180 0.35981383 0.40077145 0.11068272 -0.55584113 1.78691314
#> [17] 0.49785048 -1.96661716
#>
#> $c_prior_mean
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $c_prior_sd
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $A_initial
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_mean
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_sd
#> [,1] [,2] [,3]
#> [1,] 1.00 1.00 1.00
#> [2,] 1.00 1.00 1.00
#> [3,] 1.00 1.00 1.00
#> [4,] 1.00 1.00 1.00
#> [5,] 1.00 1.00 1.00
#> [6,] 1.00 1.00 1.00
#> [7,] 1.00 1.00 1.00
#> [8,] 1.00 1.00 1.00
#> [9,] 1.00 1.00 1.00
#> [10,] 1.00 1.00 1.00
#> [11,] 0.25 1.00 1.00
#> [12,] 1.00 1.00 1.00
#> [13,] 0.25 1.00 1.00
#> [14,] 1.00 1.00 0.25
#> [15,] 1.00 1.00 1.00
#> [16,] 1.00 0.25 1.00
#> [17,] 1.00 1.00 1.00
#> [18,] 1.00 1.00 1.00
#>
#> $R_initial
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $R_prior_eta
#> [1] 3
#>
#> $B_initial
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_mean
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_sd
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $sigmas_gp_initial
#> [1] 0.4
#>
#> $sigmas_gp_mean
#> [1] 0.4
#>
#> $sigmas_gp_sd
#> [1] 0.4
#>
#> $phi_gp_initial
#> [1] 160
#>
#> $phi_gp_mean
#> [1] 160
#>
#> $phi_gp_sd
#> [1] 0.3
#>
#> $model_type
#> [1] "spifa"attr(samples1_wet, "model_info")[-c(1:4)]#> $nobs
#> [1] 100
#>
#> $nitems
#> [1] 18
#>
#> $nfactors
#> [1] 3
#>
#> $ngp
#> [1] 1
#>
#> $niter
#> [1] 1500000
#>
#> $thin
#> [1] 200
#>
#> $standardize
#> [1] TRUE
#>
#> $constrain_L
#> F1 F2 F3
#> item_1_A_worried_that_food_ends 0 1 0
#> item_2_A_run_out_of_food 0 0 1
#> item_3_A_ate_few_food_types 1 0 0
#> item_4_B_skipped_a_meal 1 0 1
#> item_5_B_ate_less_than_required 1 0 1
#> item_6_B_hungry_but_did_not_eat 1 0 1
#> item_7_B_at_most_one_meal_per_day 1 0 0
#> item_8_C_ate_few_food_types 1 0 0
#> item_9_C_ate_less_than_required 1 0 0
#> item_10_C_decreased_food_quantity 1 0 0
#> item_11_C_skipped_a_meal 1 0 0
#> item_12_C_hungry_but_did_not_eat 1 0 0
#> item_13_C_at_most_one_meal_per_day 1 0 0
#> item_14_D_food_just_with_farinha 1 0 1
#> item_15_D_credit_for_eating 0 1 0
#> item_16_D_borrowed_food 0 1 0
#> item_17_D_had_meals_at_neighbors 0 1 0
#> item_18_D_reduced_meat_or_fish 1 0 0
#>
#> $constrain_T
#> [,1]
#> [1,] 1
#> [2,] 0
#> [3,] 0
#>
#> $constrain_V_sd
#> [,1]
#> [1,] 0.6610242
#> [2,] 0.8814925
#> [3,] 0.8815031
#>
#> $adap_Sigma
#> [,1] [,2] [,3] [,4] [,5]
#> [1,] 0.001 0.000 0.000 0.000 0.000
#> [2,] 0.000 0.001 0.000 0.000 0.000
#> [3,] 0.000 0.000 0.001 0.000 0.000
#> [4,] 0.000 0.000 0.000 0.001 0.000
#> [5,] 0.000 0.000 0.000 0.000 0.001
#>
#> $adap_scale
#> [1] 1
#>
#> $adap_C
#> [1] 0.7
#>
#> $adap_alpha
#> [1] 0.8
#>
#> $adap_accep_prob
#> [1] 0.234
#>
#> $c_initial
#> [1] -0.56047565 -0.23017749 1.55870831 0.07050839 0.12928774 1.71506499 0.46091621 -1.26506123
#> [9] -0.68685285 -0.44566197 1.22408180 0.35981383 0.40077145 0.11068272 -0.55584113 1.78691314
#> [17] 0.49785048 -1.96661716
#>
#> $c_prior_mean
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $c_prior_sd
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $A_initial
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_mean
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_sd
#> [,1] [,2] [,3]
#> [1,] 1.00 1.00 1.00
#> [2,] 1.00 1.00 1.00
#> [3,] 1.00 1.00 1.00
#> [4,] 1.00 1.00 1.00
#> [5,] 1.00 1.00 1.00
#> [6,] 1.00 1.00 1.00
#> [7,] 1.00 1.00 1.00
#> [8,] 1.00 1.00 1.00
#> [9,] 1.00 1.00 1.00
#> [10,] 1.00 1.00 1.00
#> [11,] 0.25 1.00 1.00
#> [12,] 1.00 1.00 1.00
#> [13,] 0.25 1.00 1.00
#> [14,] 1.00 1.00 0.25
#> [15,] 1.00 1.00 1.00
#> [16,] 1.00 0.25 1.00
#> [17,] 1.00 1.00 1.00
#> [18,] 1.00 1.00 1.00
#>
#> $R_initial
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $R_prior_eta
#> [1] 3
#>
#> $B_initial
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_mean
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_sd
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $sigmas_gp_initial
#> [1] 0.4
#>
#> $sigmas_gp_mean
#> [1] 0.4
#>
#> $sigmas_gp_sd
#> [1] 0.4
#>
#> $phi_gp_initial
#> [1] 160
#>
#> $phi_gp_mean
#> [1] 160
#>
#> $phi_gp_sd
#> [1] 0.3
#>
#> $model_type
#> [1] "spifa"attr(samples2_dry, "model_info")[-c(1:4)]#> $nobs
#> [1] 100
#>
#> $nitems
#> [1] 18
#>
#> $nfactors
#> [1] 3
#>
#> $ngp
#> [1] 2
#>
#> $niter
#> [1] 1500000
#>
#> $thin
#> [1] 200
#>
#> $standardize
#> [1] TRUE
#>
#> $constrain_L
#> F1 F2 F3
#> item_1_A_worried_that_food_ends 0 1 0
#> item_2_A_run_out_of_food 0 0 1
#> item_3_A_ate_few_food_types 1 0 0
#> item_4_B_skipped_a_meal 1 0 1
#> item_5_B_ate_less_than_required 1 0 1
#> item_6_B_hungry_but_did_not_eat 1 0 1
#> item_7_B_at_most_one_meal_per_day 1 0 0
#> item_8_C_ate_few_food_types 1 0 0
#> item_9_C_ate_less_than_required 1 0 0
#> item_10_C_decreased_food_quantity 1 0 0
#> item_11_C_skipped_a_meal 1 0 0
#> item_12_C_hungry_but_did_not_eat 1 0 0
#> item_13_C_at_most_one_meal_per_day 1 0 0
#> item_14_D_food_just_with_farinha 1 0 1
#> item_15_D_credit_for_eating 0 1 0
#> item_16_D_borrowed_food 0 1 0
#> item_17_D_had_meals_at_neighbors 0 1 0
#> item_18_D_reduced_meat_or_fish 1 0 0
#>
#> $constrain_T
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 0 0
#> [3,] 0 1
#>
#> $constrain_V_sd
#> [,1]
#> [1,] 0.7846918
#> [2,] 0.9179651
#> [3,] 0.8659754
#>
#> $adap_Sigma
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 0.001 0.000 0.000 0.000 0.000 0.000 0.000
#> [2,] 0.000 0.001 0.000 0.000 0.000 0.000 0.000
#> [3,] 0.000 0.000 0.001 0.000 0.000 0.000 0.000
#> [4,] 0.000 0.000 0.000 0.001 0.000 0.000 0.000
#> [5,] 0.000 0.000 0.000 0.000 0.001 0.000 0.000
#> [6,] 0.000 0.000 0.000 0.000 0.000 0.001 0.000
#> [7,] 0.000 0.000 0.000 0.000 0.000 0.000 0.001
#>
#> $adap_scale
#> [1] 1
#>
#> $adap_C
#> [1] 0.7
#>
#> $adap_alpha
#> [1] 0.8
#>
#> $adap_accep_prob
#> [1] 0.234
#>
#> $c_initial
#> [1] -0.56047565 -0.23017749 1.55870831 0.07050839 0.12928774 1.71506499 0.46091621 -1.26506123
#> [9] -0.68685285 -0.44566197 1.22408180 0.35981383 0.40077145 0.11068272 -0.55584113 1.78691314
#> [17] 0.49785048 -1.96661716
#>
#> $c_prior_mean
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $c_prior_sd
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $A_initial
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_mean
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_sd
#> [,1] [,2] [,3]
#> [1,] 1.00 1.00 1.00
#> [2,] 1.00 1.00 1.00
#> [3,] 1.00 1.00 1.00
#> [4,] 1.00 1.00 1.00
#> [5,] 1.00 1.00 1.00
#> [6,] 1.00 1.00 1.00
#> [7,] 1.00 1.00 1.00
#> [8,] 1.00 1.00 1.00
#> [9,] 1.00 1.00 1.00
#> [10,] 1.00 1.00 1.00
#> [11,] 0.25 1.00 1.00
#> [12,] 1.00 1.00 1.00
#> [13,] 0.25 1.00 1.00
#> [14,] 1.00 1.00 0.25
#> [15,] 1.00 1.00 1.00
#> [16,] 1.00 0.25 1.00
#> [17,] 1.00 1.00 1.00
#> [18,] 1.00 1.00 1.00
#>
#> $R_initial
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $R_prior_eta
#> [1] 3
#>
#> $B_initial
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_mean
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_sd
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $sigmas_gp_initial
#> [1] 0.4 0.4
#>
#> $sigmas_gp_mean
#> [1] 0.4 0.4
#>
#> $sigmas_gp_sd
#> [1] 0.4 0.4
#>
#> $phi_gp_initial
#> [1] 160 80
#>
#> $phi_gp_mean
#> [1] 160 80
#>
#> $phi_gp_sd
#> [1] 0.3 0.3
#>
#> $model_type
#> [1] "spifa"attr(samples2_wet, "model_info")[-c(1:4)]#> $nobs
#> [1] 100
#>
#> $nitems
#> [1] 18
#>
#> $nfactors
#> [1] 3
#>
#> $ngp
#> [1] 2
#>
#> $niter
#> [1] 1500000
#>
#> $thin
#> [1] 200
#>
#> $standardize
#> [1] TRUE
#>
#> $constrain_L
#> F1 F2 F3
#> item_1_A_worried_that_food_ends 0 1 0
#> item_2_A_run_out_of_food 0 0 1
#> item_3_A_ate_few_food_types 1 0 0
#> item_4_B_skipped_a_meal 1 0 1
#> item_5_B_ate_less_than_required 1 0 1
#> item_6_B_hungry_but_did_not_eat 1 0 1
#> item_7_B_at_most_one_meal_per_day 1 0 0
#> item_8_C_ate_few_food_types 1 0 0
#> item_9_C_ate_less_than_required 1 0 0
#> item_10_C_decreased_food_quantity 1 0 0
#> item_11_C_skipped_a_meal 1 0 0
#> item_12_C_hungry_but_did_not_eat 1 0 0
#> item_13_C_at_most_one_meal_per_day 1 0 0
#> item_14_D_food_just_with_farinha 1 0 1
#> item_15_D_credit_for_eating 0 1 0
#> item_16_D_borrowed_food 0 1 0
#> item_17_D_had_meals_at_neighbors 0 1 0
#> item_18_D_reduced_meat_or_fish 1 0 0
#>
#> $constrain_T
#> [,1] [,2]
#> [1,] 1 0
#> [2,] 0 0
#> [3,] 0 1
#>
#> $constrain_V_sd
#> [,1]
#> [1,] 0.6576590
#> [2,] 0.8821168
#> [3,] 0.8235346
#>
#> $adap_Sigma
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7]
#> [1,] 0.001 0.000 0.000 0.000 0.000 0.000 0.000
#> [2,] 0.000 0.001 0.000 0.000 0.000 0.000 0.000
#> [3,] 0.000 0.000 0.001 0.000 0.000 0.000 0.000
#> [4,] 0.000 0.000 0.000 0.001 0.000 0.000 0.000
#> [5,] 0.000 0.000 0.000 0.000 0.001 0.000 0.000
#> [6,] 0.000 0.000 0.000 0.000 0.000 0.001 0.000
#> [7,] 0.000 0.000 0.000 0.000 0.000 0.000 0.001
#>
#> $adap_scale
#> [1] 1
#>
#> $adap_C
#> [1] 0.7
#>
#> $adap_alpha
#> [1] 0.8
#>
#> $adap_accep_prob
#> [1] 0.234
#>
#> $c_initial
#> [1] -0.56047565 -0.23017749 1.55870831 0.07050839 0.12928774 1.71506499 0.46091621 -1.26506123
#> [9] -0.68685285 -0.44566197 1.22408180 0.35981383 0.40077145 0.11068272 -0.55584113 1.78691314
#> [17] 0.49785048 -1.96661716
#>
#> $c_prior_mean
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $c_prior_sd
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $A_initial
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_mean
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_sd
#> [,1] [,2] [,3]
#> [1,] 1.00 1.00 1.00
#> [2,] 1.00 1.00 1.00
#> [3,] 1.00 1.00 1.00
#> [4,] 1.00 1.00 1.00
#> [5,] 1.00 1.00 1.00
#> [6,] 1.00 1.00 1.00
#> [7,] 1.00 1.00 1.00
#> [8,] 1.00 1.00 1.00
#> [9,] 1.00 1.00 1.00
#> [10,] 1.00 1.00 1.00
#> [11,] 0.25 1.00 1.00
#> [12,] 1.00 1.00 1.00
#> [13,] 0.25 1.00 1.00
#> [14,] 1.00 1.00 0.25
#> [15,] 1.00 1.00 1.00
#> [16,] 1.00 0.25 1.00
#> [17,] 1.00 1.00 1.00
#> [18,] 1.00 1.00 1.00
#>
#> $R_initial
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $R_prior_eta
#> [1] 3
#>
#> $B_initial
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_mean
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_sd
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $sigmas_gp_initial
#> [1] 0.4 0.4
#>
#> $sigmas_gp_mean
#> [1] 0.4 0.4
#>
#> $sigmas_gp_sd
#> [1] 0.4 0.4
#>
#> $phi_gp_initial
#> [1] 160 80
#>
#> $phi_gp_mean
#> [1] 160 80
#>
#> $phi_gp_sd
#> [1] 0.3 0.3
#>
#> $model_type
#> [1] "spifa"attr(samples3_dry, "model_info")[-c(1:4)]#> $nobs
#> [1] 100
#>
#> $nitems
#> [1] 18
#>
#> $nfactors
#> [1] 3
#>
#> $ngp
#> [1] 3
#>
#> $niter
#> [1] 1500000
#>
#> $thin
#> [1] 200
#>
#> $standardize
#> [1] TRUE
#>
#> $constrain_L
#> F1 F2 F3
#> item_1_A_worried_that_food_ends 0 1 0
#> item_2_A_run_out_of_food 0 0 1
#> item_3_A_ate_few_food_types 1 0 0
#> item_4_B_skipped_a_meal 1 0 1
#> item_5_B_ate_less_than_required 1 0 1
#> item_6_B_hungry_but_did_not_eat 1 0 1
#> item_7_B_at_most_one_meal_per_day 1 0 0
#> item_8_C_ate_few_food_types 1 0 0
#> item_9_C_ate_less_than_required 1 0 0
#> item_10_C_decreased_food_quantity 1 0 0
#> item_11_C_skipped_a_meal 1 0 0
#> item_12_C_hungry_but_did_not_eat 1 0 0
#> item_13_C_at_most_one_meal_per_day 1 0 0
#> item_14_D_food_just_with_farinha 1 0 1
#> item_15_D_credit_for_eating 0 1 0
#> item_16_D_borrowed_food 0 1 0
#> item_17_D_had_meals_at_neighbors 0 1 0
#> item_18_D_reduced_meat_or_fish 1 0 0
#>
#> $constrain_T
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $constrain_V_sd
#> [,1]
#> [1,] 0.7840831
#> [2,] 0.8916548
#> [3,] 0.8633814
#>
#> $adap_Sigma
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> [1,] 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> [2,] 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> [3,] 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000
#> [4,] 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000
#> [5,] 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000
#> [6,] 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000
#> [7,] 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000
#> [8,] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000
#> [9,] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001
#>
#> $adap_scale
#> [1] 1
#>
#> $adap_C
#> [1] 0.7
#>
#> $adap_alpha
#> [1] 0.8
#>
#> $adap_accep_prob
#> [1] 0.234
#>
#> $c_initial
#> [1] -0.56047565 -0.23017749 1.55870831 0.07050839 0.12928774 1.71506499 0.46091621 -1.26506123
#> [9] -0.68685285 -0.44566197 1.22408180 0.35981383 0.40077145 0.11068272 -0.55584113 1.78691314
#> [17] 0.49785048 -1.96661716
#>
#> $c_prior_mean
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $c_prior_sd
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $A_initial
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_mean
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_sd
#> [,1] [,2] [,3]
#> [1,] 1.00 1.00 1.00
#> [2,] 1.00 1.00 1.00
#> [3,] 1.00 1.00 1.00
#> [4,] 1.00 1.00 1.00
#> [5,] 1.00 1.00 1.00
#> [6,] 1.00 1.00 1.00
#> [7,] 1.00 1.00 1.00
#> [8,] 1.00 1.00 1.00
#> [9,] 1.00 1.00 1.00
#> [10,] 1.00 1.00 1.00
#> [11,] 0.25 1.00 1.00
#> [12,] 1.00 1.00 1.00
#> [13,] 0.25 1.00 1.00
#> [14,] 1.00 1.00 0.25
#> [15,] 1.00 1.00 1.00
#> [16,] 1.00 0.25 1.00
#> [17,] 1.00 1.00 1.00
#> [18,] 1.00 1.00 1.00
#>
#> $R_initial
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $R_prior_eta
#> [1] 3
#>
#> $B_initial
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_mean
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_sd
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $sigmas_gp_initial
#> [1] 0.4 0.4 0.4
#>
#> $sigmas_gp_mean
#> [1] 0.4 0.2 0.4
#>
#> $sigmas_gp_sd
#> [1] 0.4 0.4 0.4
#>
#> $phi_gp_initial
#> [1] 160 80 80
#>
#> $phi_gp_mean
#> [1] 160 80 80
#>
#> $phi_gp_sd
#> [1] 0.3 0.3 0.3
#>
#> $model_type
#> [1] "spifa"attr(samples3_wet, "model_info")[-c(1:4)]#> $nobs
#> [1] 100
#>
#> $nitems
#> [1] 18
#>
#> $nfactors
#> [1] 3
#>
#> $ngp
#> [1] 3
#>
#> $niter
#> [1] 1500000
#>
#> $thin
#> [1] 200
#>
#> $standardize
#> [1] TRUE
#>
#> $constrain_L
#> F1 F2 F3
#> item_1_A_worried_that_food_ends 0 1 0
#> item_2_A_run_out_of_food 0 0 1
#> item_3_A_ate_few_food_types 1 0 0
#> item_4_B_skipped_a_meal 1 0 1
#> item_5_B_ate_less_than_required 1 0 1
#> item_6_B_hungry_but_did_not_eat 1 0 1
#> item_7_B_at_most_one_meal_per_day 1 0 0
#> item_8_C_ate_few_food_types 1 0 0
#> item_9_C_ate_less_than_required 1 0 0
#> item_10_C_decreased_food_quantity 1 0 0
#> item_11_C_skipped_a_meal 1 0 0
#> item_12_C_hungry_but_did_not_eat 1 0 0
#> item_13_C_at_most_one_meal_per_day 1 0 0
#> item_14_D_food_just_with_farinha 1 0 1
#> item_15_D_credit_for_eating 0 1 0
#> item_16_D_borrowed_food 0 1 0
#> item_17_D_had_meals_at_neighbors 0 1 0
#> item_18_D_reduced_meat_or_fish 1 0 0
#>
#> $constrain_T
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $constrain_V_sd
#> [,1]
#> [1,] 0.6779650
#> [2,] 0.8632087
#> [3,] 0.8352685
#>
#> $adap_Sigma
#> [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9]
#> [1,] 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> [2,] 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000 0.000
#> [3,] 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000 0.000
#> [4,] 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000 0.000
#> [5,] 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000 0.000
#> [6,] 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000 0.000
#> [7,] 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000 0.000
#> [8,] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001 0.000
#> [9,] 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.001
#>
#> $adap_scale
#> [1] 1
#>
#> $adap_C
#> [1] 0.7
#>
#> $adap_alpha
#> [1] 0.8
#>
#> $adap_accep_prob
#> [1] 0.234
#>
#> $c_initial
#> [1] -0.56047565 -0.23017749 1.55870831 0.07050839 0.12928774 1.71506499 0.46091621 -1.26506123
#> [9] -0.68685285 -0.44566197 1.22408180 0.35981383 0.40077145 0.11068272 -0.55584113 1.78691314
#> [17] 0.49785048 -1.96661716
#>
#> $c_prior_mean
#> [1] 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
#>
#> $c_prior_sd
#> [1] 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
#>
#> $A_initial
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_mean
#> [,1] [,2] [,3]
#> [1,] 0 0 0
#> [2,] 0 0 0
#> [3,] 0 0 0
#> [4,] 0 0 0
#> [5,] 0 0 0
#> [6,] 0 0 0
#> [7,] 0 0 0
#> [8,] 0 0 0
#> [9,] 0 0 0
#> [10,] 0 0 0
#> [11,] 1 0 0
#> [12,] 0 0 0
#> [13,] 1 0 0
#> [14,] 0 0 1
#> [15,] 0 0 0
#> [16,] 0 1 0
#> [17,] 0 0 0
#> [18,] 0 0 0
#>
#> $A_prior_sd
#> [,1] [,2] [,3]
#> [1,] 1.00 1.00 1.00
#> [2,] 1.00 1.00 1.00
#> [3,] 1.00 1.00 1.00
#> [4,] 1.00 1.00 1.00
#> [5,] 1.00 1.00 1.00
#> [6,] 1.00 1.00 1.00
#> [7,] 1.00 1.00 1.00
#> [8,] 1.00 1.00 1.00
#> [9,] 1.00 1.00 1.00
#> [10,] 1.00 1.00 1.00
#> [11,] 0.25 1.00 1.00
#> [12,] 1.00 1.00 1.00
#> [13,] 0.25 1.00 1.00
#> [14,] 1.00 1.00 0.25
#> [15,] 1.00 1.00 1.00
#> [16,] 1.00 0.25 1.00
#> [17,] 1.00 1.00 1.00
#> [18,] 1.00 1.00 1.00
#>
#> $R_initial
#> [,1] [,2] [,3]
#> [1,] 1 0 0
#> [2,] 0 1 0
#> [3,] 0 0 1
#>
#> $R_prior_eta
#> [1] 3
#>
#> $B_initial
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_mean
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $B_prior_sd
#> [,1] [,2] [,3]
#> [1,] NA NA NA
#>
#> $sigmas_gp_initial
#> [1] 0.4 0.4 0.4
#>
#> $sigmas_gp_mean
#> [1] 0.4 0.2 0.4
#>
#> $sigmas_gp_sd
#> [1] 0.4 0.4 0.4
#>
#> $phi_gp_initial
#> [1] 160 80 80
#>
#> $phi_gp_mean
#> [1] 160 80 80
#>
#> $phi_gp_sd
#> [1] 0.3 0.3 0.3
#>
#> $model_type
#> [1] "spifa"Time to execute the task
Only useful when executed with Rscript.
proc.time()#> user system elapsed
#> 221956.06 190272.90 51599.85