GDP problems
06 July, 2023
Init
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## cor2covtheme_set(theme_bw())
options(
digits = 3
)
#how much data to impute
imp_max_miss = .10Functions
log10_fix = function(x) {
log10(x) %>% mapvalues(from = -Inf, to = NA, warn_missing = F)
}Data
#GDP per capita, world bank
#https://ourworldindata.org/grapher/gdp-per-capita-worldbank
gdp_pc_ppp_wb = read_csv("data/gdp-per-capita-worldbank.csv") %>%
df_legalize_names() %>%
rename(ISO = Code)## Rows: 6166 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Entity, Code
## dbl (2): Year, GDP per capita, PPP (constant 2017 international $)
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#GNI, world bank
#https://ourworldindata.org/grapher/gross-national-income-per-capita
gni_pc = read_csv("data/gross-national-income-per-capita.csv") %>%
df_legalize_names() %>%
rename(ISO = Code)## Rows: 4333 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Entity, Code
## dbl (2): Year, GNI per capita, PPP (constant 2017 international $)
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#median income
#https://worldpopulationreview.com/country-rankings/median-income-by-country
median_income = read_csv("data/median-income-by-country-2023.csv") %>%
df_legalize_names() %>%
mutate(ISO = pu_translate(country))## Rows: 162 Columns: 5
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (1): country
## dbl (4): medianIncome, meanIncome, gdpPerCapitaPPP, pop2023
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#HDI
#https://ourworldindata.org/grapher/human-development-index
hdi = read_csv("data/human-development-index.csv") %>%
df_legalize_names() %>%
rename(ISO = Code)## Rows: 5923 Columns: 4
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr (2): Entity, Code
## dbl (2): Year, Human Development Index
##
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.#SPI
#https://www.socialprogress.org/
spi = read_excel("data/2022_social_progress_index_dataset-1663250377.xlsx", sheet = 2, skip = 2) %>%
df_legalize_names() %>%
rename(ISO = SPI_country_code)
#becker IQ data
becker = read_excel("data/NIQ-DATA (V1.3.3).xlsx", sheet = "FAV", range = "A2:N205") %>%
df_legalize_names()
#regional dummies
smart_fraction_data = read_rds("data/smart_fraction_data_out.rds")
#join the datasets for year 2019, pre-covid
d = full_join(
gdp_pc_ppp_wb %>% filter(Year == 2019, !is.na(ISO)) %>% select(ISO, GDP_per_capita_PPP_constant_2017_international) %>% rename(GDPpc_PPP_2019 = GDP_per_capita_PPP_constant_2017_international),
gni_pc %>% filter(Year == 2019, !is.na(ISO)) %>% select(ISO, GNI_per_capita_PPP_constant_2017_international) %>% rename(GNIpc_PPP_2019 = GNI_per_capita_PPP_constant_2017_international)
) %>%
full_join(
median_income %>% filter(!is.na(ISO)) %>% select(ISO, medianIncome, meanIncome)
) %>%
full_join(
hdi %>% filter(Year == 2019, !is.na(ISO)) %>% select(ISO, Human_Development_Index) %>% rename(HDI_2019 = Human_Development_Index)
) %>%
full_join(
spi %>% filter(SPI_year == 2019, !is.na(ISO)) %>% select(ISO, Social_Progress_Index:Quality_weighted_universities_points) %>% rename(SPI_2019 = Social_Progress_Index)
) %>%
full_join(
becker %>% select(-Country)
) %>%
left_join(
smart_fraction_data %>% select(ISO, starts_with("UN_"))
) %>% mutate(
nice_name = pu_translate(ISO, reverse = T)
)## Joining with `by = join_by(ISO)`
## Joining with `by = join_by(ISO)`
## Joining with `by = join_by(ISO)`
## Joining with `by = join_by(ISO)`
## Joining with `by = join_by(ISO)`
## Joining with `by = join_by(ISO)`
## No match: OWID_KOS
## No match: OWID_WRL
## No match: WWW
## No match: WBG
## No match: KNA.assert_that(!any(is.na(d$ISO)))## [1] TRUERecoding
#log10 variants
d %<>% mutate(
medianIncome_log10 = log10_fix(medianIncome),
meanIncome_log10 = log10_fix(meanIncome),
GNIpc_PPP_2019_log10 = log10_fix(GNIpc_PPP_2019),
GDPpc_PPP_2019_log10 = log10_fix(GDPpc_PPP_2019)
)
#list of variables
d_vars = d %>% df_var_table()
#get all the data plus all econs
d_spi_all = d %>% select(ISO, Child_stunting_0_low_risk_100_high_risk:Quality_weighted_universities_points, medianIncome_log10, meanIncome_log10, GDPpc_PPP_2019_log10, GNIpc_PPP_2019_log10) %>%
miss_filter(missing = imp_max_miss)
d_spi_all_imp = miss_impute(d_spi_all, leave_out = "ISO")
d_spi_all_imp_z = df_standardize(d_spi_all_imp, exclude_range_01 = F)## Skipped ISO because it is a character (string)Analysis
Simple
#GDP vs. GNI
d %>%
GG_scatter("GDPpc_PPP_2019", "GNIpc_PPP_2019", case_names = "nice_name") +
geom_abline(slope = 1, linetype = "dotted") +
scale_y_continuous("GNI per capita, PPP, 2019") +
scale_x_continuous("GDP per capita, PPP, 2019") +
ggtitle("GDP vs. GNI per capita, PPP, 2019")## `geom_smooth()` using formula = 'y ~ x'
GG_save("figs/GNI_GDP.png")## `geom_smooth()` using formula = 'y ~ x'#GNI vs. average income
d %>%
GG_scatter("GNIpc_PPP_2019", "meanIncome", case_names = "nice_name", repel_names = T) +
scale_x_continuous("GNI per capita, PPP, 2019") +
scale_y_continuous("Average income, PPP, 2021") +
ggtitle("GNI per capita (PPP, 2019) vs. average income 2021 (PPP)")## `geom_smooth()` using formula = 'y ~ x'## Warning: ggrepel: 87 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
GG_save("figs/GNI_avg_income.png")## `geom_smooth()` using formula = 'y ~ x'## Warning: ggrepel: 75 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps#GNI vs. average income
d %>%
GG_scatter("meanIncome", "medianIncome", case_names = "nice_name", repel_names = T) +
scale_x_continuous("Mean income, PPP, 2021") +
scale_y_continuous("Median income, PPP, 2021") +
ggtitle("Mean income vs. median income (2021, PPP)") +
geom_abline(slope = 1, linetype = "dotted")## `geom_smooth()` using formula = 'y ~ x'## Warning: ggrepel: 142 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
GG_save("figs/mean_median_income.png")## `geom_smooth()` using formula = 'y ~ x'## Warning: ggrepel: 130 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps#GNI vs. median income
d %>%
GG_scatter("medianIncome", "HDI_2019", case_names = "nice_name", repel_names = T) +
scale_x_continuous("Median income, PPP, 2021") +
scale_y_continuous("HDI, 2019") +
ggtitle("Median income (2021, PPP) vs. Human Development Index (2019)") +
geom_smooth(se = T)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: ggrepel: 147 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
GG_save("figs/median_income_HDI.png")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: ggrepel: 122 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps#median income vs. SPI
d %>%
GG_scatter("medianIncome", "SPI_2019", case_names = "nice_name", repel_names = T) +
scale_x_continuous("Median income, PPP, 2021") +
scale_y_continuous("SPI, 2019") +
ggtitle("Median income (2021, PPP) vs. Social Progress Index (2019)") +
geom_smooth(se = T)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: ggrepel: 136 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
GG_save("figs/median_income_SPI.png")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: ggrepel: 108 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps#log median income vs. SPI
d %>%
GG_scatter("medianIncome_log10", "SPI_2019", case_names = "nice_name", repel_names = T) +
scale_x_continuous("Median income, PPP, 2021, log10") +
scale_y_continuous("SPI, 2019") +
ggtitle("Median income log10 (2021, PPP) vs. Social Progress Index (2019)") +
geom_smooth(se = T)## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: ggrepel: 100 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps
GG_save("figs/median_income_log10_SPI.png")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'## Warning: ggrepel: 45 unlabeled data points (too many overlaps). Consider
## increasing max.overlaps#print top
d %>% select(nice_name, medianIncome_log10, SPI_2019) %>% arrange(-SPI_2019) %>% head(25) %>% print(n=Inf)## # A tibble: 25 × 3
## nice_name medianIncome_log10 SPI_2019
## <chr> <dbl> <dbl>
## 1 Norway 4.36 90.8
## 2 Denmark 4.24 90.6
## 3 Finland 4.21 90.5
## 4 Switzerland 4.33 89.6
## 5 Iceland 4.23 89.5
## 6 Netherlands 4.23 89.4
## 7 Sweden 4.25 89.2
## 8 Germany 4.23 88.8
## 9 Australia 4.23 88.1
## 10 Canada 4.27 88.0
## 11 Luxembourg 4.42 87.9
## 12 Ireland 4.16 87.8
## 13 Japan 4.15 87.7
## 14 Austria 4.26 87.4
## 15 New Zealand NA 87.4
## 16 Belgium 4.21 87.1
## 17 United Kingdom 4.17 86.5
## 18 France 4.21 86.3
## 19 South Korea 4.10 85.6
## 20 Spain 4.07 85.5
## 21 Slovenia 4.07 85.3
## 22 Estonia 4.03 85.3
## 23 USA 4.29 85.1
## 24 Portugal 3.93 85
## 25 Czech Republic 4.00 84.5SPI EFA models
#EFA of SPI
#any missing?
d_spi = d %>% select(ISO, Child_stunting_0_low_risk_100_high_risk:Quality_weighted_universities_points) %>%
miss_filter(missing = imp_max_miss)
d_spi_imp = miss_impute(d_spi, leave_out = "ISO")
#EFA
spi_efa = fa(
d_spi_imp %>% select(-ISO)
)
spi_efa## Factor Analysis using method = minres
## Call: fa(r = d_spi_imp %>% select(-ISO))
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1
## Child_stunting_0_low_risk_100_high_risk -0.87
## Infectious_diseases_DALYs_100_000 -0.74
## Maternal_mortality_rate_deaths_100_000_live_births -0.82
## Child_mortality_rate_deaths_1_000_live_births -0.87
## Undernourishment_pct_of_pop -0.73
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk -0.60
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 -0.78
## Access_to_improved_sanitation_proportion_of_pop 0.85
## Access_to_improved_water_source_proportion_of_pop 0.79
## Satisfaction_with_water_quality_proportion_of_pop 0.72
## Household_air_pollution_DALYs_100_000 -0.83
## Dissatisfaction_with_housing_affordability_proportion_of_pop -0.24
## Access_to_electricity_pct_of_pop 0.77
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.82
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.69
## Interpersonal_violence_DALYs_100_000 -0.28
## Transportation_related_injuries_DALYs_100_000 -0.63
## Intimate_Partner_Violence_pct_of_women_aged_15plus -0.81
## Money_Stolen_proportion_of_pop -0.58
## Equal_access_to_quality_education_0_unequal_4_equal 0.79
## Population_with_no_schooling_proportion_of_pop -0.75
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.79
## Primary_school_enrollment_pct_of_children 0.68
## Gender_parity_in_secondary_attainment_distance_from_parity -0.77
## Alternative_sources_of_information_index_0_low_1_high 0.32
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.63
## Internet_users_pct_of_pop 0.90
## Access_to_online_governance_0_low_1_high 0.77
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.85
## Life_expectancy_at_60_years 0.82
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 -0.61
## Access_to_essential_health_services_0_none_100_full_coverage 0.95
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.53
## Outdoor_air_pollution_DALYs_100_000 -0.35
## Lead_exposure_DALYs_100_000 -0.65
## Particulate_matter_pollution_mean_annual_exposure_g_m3 -0.55
## Species_protection_0_low_100_high 0.29
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.35
## Property_rights_for_women_0_no_rights_5_full_rights 0.74
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.56
## Access_to_justice_0_nonexistent_1_observed 0.69
## Freedom_of_discussion_0_low_1_high 0.54
## Political_rights_0_and_lower_no_rights_40_full_rights 0.67
## Freedom_of_domestic_movement_0_low_1_high 0.59
## Early_marriage_pct_of_married_women_aged_15_19 -0.72
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.61
## Young_people_not_in_education_employment_or_training_pct_of_youth -0.50
## Vulnerable_employment_pct_of_total_employment -0.84
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.81
## Equal_protection_index_0_low_1_high 0.68
## Equal_access_index_0_low_1_high 0.58
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.65
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.76
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.60
## Discrimination_and_violence_against_minorities_0_low_10_high -0.50
## Academic_freedom_0_low_1_high 0.50
## Women_with_advanced_education_proportion_of_females 0.86
## Expected_years_of_tertiary_schooling_years 0.80
## Citable_documents_documents_1_000_people 0.73
## Quality_weighted_universities_points 0.28
## h2
## Child_stunting_0_low_risk_100_high_risk 0.752
## Infectious_diseases_DALYs_100_000 0.546
## Maternal_mortality_rate_deaths_100_000_live_births 0.670
## Child_mortality_rate_deaths_1_000_live_births 0.750
## Undernourishment_pct_of_pop 0.536
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 0.358
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 0.604
## Access_to_improved_sanitation_proportion_of_pop 0.723
## Access_to_improved_water_source_proportion_of_pop 0.618
## Satisfaction_with_water_quality_proportion_of_pop 0.514
## Household_air_pollution_DALYs_100_000 0.696
## Dissatisfaction_with_housing_affordability_proportion_of_pop 0.057
## Access_to_electricity_pct_of_pop 0.593
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.677
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.481
## Interpersonal_violence_DALYs_100_000 0.081
## Transportation_related_injuries_DALYs_100_000 0.393
## Intimate_Partner_Violence_pct_of_women_aged_15plus 0.655
## Money_Stolen_proportion_of_pop 0.341
## Equal_access_to_quality_education_0_unequal_4_equal 0.626
## Population_with_no_schooling_proportion_of_pop 0.566
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.631
## Primary_school_enrollment_pct_of_children 0.465
## Gender_parity_in_secondary_attainment_distance_from_parity 0.597
## Alternative_sources_of_information_index_0_low_1_high 0.106
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.391
## Internet_users_pct_of_pop 0.808
## Access_to_online_governance_0_low_1_high 0.596
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.716
## Life_expectancy_at_60_years 0.677
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 0.370
## Access_to_essential_health_services_0_none_100_full_coverage 0.907
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.284
## Outdoor_air_pollution_DALYs_100_000 0.123
## Lead_exposure_DALYs_100_000 0.422
## Particulate_matter_pollution_mean_annual_exposure_g_m3 0.307
## Species_protection_0_low_100_high 0.082
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.120
## Property_rights_for_women_0_no_rights_5_full_rights 0.549
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.309
## Access_to_justice_0_nonexistent_1_observed 0.472
## Freedom_of_discussion_0_low_1_high 0.290
## Political_rights_0_and_lower_no_rights_40_full_rights 0.454
## Freedom_of_domestic_movement_0_low_1_high 0.343
## Early_marriage_pct_of_married_women_aged_15_19 0.519
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.371
## Young_people_not_in_education_employment_or_training_pct_of_youth 0.254
## Vulnerable_employment_pct_of_total_employment 0.713
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.655
## Equal_protection_index_0_low_1_high 0.458
## Equal_access_index_0_low_1_high 0.342
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.423
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.577
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.362
## Discrimination_and_violence_against_minorities_0_low_10_high 0.255
## Academic_freedom_0_low_1_high 0.247
## Women_with_advanced_education_proportion_of_females 0.746
## Expected_years_of_tertiary_schooling_years 0.640
## Citable_documents_documents_1_000_people 0.537
## Quality_weighted_universities_points 0.077
## u2
## Child_stunting_0_low_risk_100_high_risk 0.248
## Infectious_diseases_DALYs_100_000 0.454
## Maternal_mortality_rate_deaths_100_000_live_births 0.330
## Child_mortality_rate_deaths_1_000_live_births 0.250
## Undernourishment_pct_of_pop 0.464
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 0.642
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 0.396
## Access_to_improved_sanitation_proportion_of_pop 0.277
## Access_to_improved_water_source_proportion_of_pop 0.382
## Satisfaction_with_water_quality_proportion_of_pop 0.486
## Household_air_pollution_DALYs_100_000 0.304
## Dissatisfaction_with_housing_affordability_proportion_of_pop 0.943
## Access_to_electricity_pct_of_pop 0.407
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.323
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.519
## Interpersonal_violence_DALYs_100_000 0.919
## Transportation_related_injuries_DALYs_100_000 0.607
## Intimate_Partner_Violence_pct_of_women_aged_15plus 0.345
## Money_Stolen_proportion_of_pop 0.659
## Equal_access_to_quality_education_0_unequal_4_equal 0.374
## Population_with_no_schooling_proportion_of_pop 0.434
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.369
## Primary_school_enrollment_pct_of_children 0.535
## Gender_parity_in_secondary_attainment_distance_from_parity 0.403
## Alternative_sources_of_information_index_0_low_1_high 0.894
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.609
## Internet_users_pct_of_pop 0.192
## Access_to_online_governance_0_low_1_high 0.404
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.284
## Life_expectancy_at_60_years 0.323
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 0.630
## Access_to_essential_health_services_0_none_100_full_coverage 0.093
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.716
## Outdoor_air_pollution_DALYs_100_000 0.877
## Lead_exposure_DALYs_100_000 0.578
## Particulate_matter_pollution_mean_annual_exposure_g_m3 0.693
## Species_protection_0_low_100_high 0.918
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.880
## Property_rights_for_women_0_no_rights_5_full_rights 0.451
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.691
## Access_to_justice_0_nonexistent_1_observed 0.528
## Freedom_of_discussion_0_low_1_high 0.710
## Political_rights_0_and_lower_no_rights_40_full_rights 0.546
## Freedom_of_domestic_movement_0_low_1_high 0.657
## Early_marriage_pct_of_married_women_aged_15_19 0.481
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.629
## Young_people_not_in_education_employment_or_training_pct_of_youth 0.746
## Vulnerable_employment_pct_of_total_employment 0.287
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.345
## Equal_protection_index_0_low_1_high 0.542
## Equal_access_index_0_low_1_high 0.658
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.577
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.423
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.638
## Discrimination_and_violence_against_minorities_0_low_10_high 0.745
## Academic_freedom_0_low_1_high 0.753
## Women_with_advanced_education_proportion_of_females 0.254
## Expected_years_of_tertiary_schooling_years 0.360
## Citable_documents_documents_1_000_people 0.463
## Quality_weighted_universities_points 0.923
## com
## Child_stunting_0_low_risk_100_high_risk 1
## Infectious_diseases_DALYs_100_000 1
## Maternal_mortality_rate_deaths_100_000_live_births 1
## Child_mortality_rate_deaths_1_000_live_births 1
## Undernourishment_pct_of_pop 1
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 1
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 1
## Access_to_improved_sanitation_proportion_of_pop 1
## Access_to_improved_water_source_proportion_of_pop 1
## Satisfaction_with_water_quality_proportion_of_pop 1
## Household_air_pollution_DALYs_100_000 1
## Dissatisfaction_with_housing_affordability_proportion_of_pop 1
## Access_to_electricity_pct_of_pop 1
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 1
## Political_killings_and_torture_0_low_freedom_1_high_freedom 1
## Interpersonal_violence_DALYs_100_000 1
## Transportation_related_injuries_DALYs_100_000 1
## Intimate_Partner_Violence_pct_of_women_aged_15plus 1
## Money_Stolen_proportion_of_pop 1
## Equal_access_to_quality_education_0_unequal_4_equal 1
## Population_with_no_schooling_proportion_of_pop 1
## Secondary_school_attainment_pct_of_pop_aged_25plus 1
## Primary_school_enrollment_pct_of_children 1
## Gender_parity_in_secondary_attainment_distance_from_parity 1
## Alternative_sources_of_information_index_0_low_1_high 1
## Mobile_telephone_subscriptions_subscriptions_1_000_people 1
## Internet_users_pct_of_pop 1
## Access_to_online_governance_0_low_1_high 1
## Equal_access_to_quality_healthcare_0_unequal_4_equal 1
## Life_expectancy_at_60_years 1
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 1
## Access_to_essential_health_services_0_none_100_full_coverage 1
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 1
## Outdoor_air_pollution_DALYs_100_000 1
## Lead_exposure_DALYs_100_000 1
## Particulate_matter_pollution_mean_annual_exposure_g_m3 1
## Species_protection_0_low_100_high 1
## Freedom_of_religion_0_no_freedom_4_full_freedom 1
## Property_rights_for_women_0_no_rights_5_full_rights 1
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 1
## Access_to_justice_0_nonexistent_1_observed 1
## Freedom_of_discussion_0_low_1_high 1
## Political_rights_0_and_lower_no_rights_40_full_rights 1
## Freedom_of_domestic_movement_0_low_1_high 1
## Early_marriage_pct_of_married_women_aged_15_19 1
## Satisfied_demand_for_contraception_pct_satisfied_demand 1
## Young_people_not_in_education_employment_or_training_pct_of_youth 1
## Vulnerable_employment_pct_of_total_employment 1
## Perception_of_corruption_0_high_corruption_100_low_corruption 1
## Equal_protection_index_0_low_1_high 1
## Equal_access_index_0_low_1_high 1
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 1
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 1
## Acceptance_of_gays_and_lesbians_proportion_of_pop 1
## Discrimination_and_violence_against_minorities_0_low_10_high 1
## Academic_freedom_0_low_1_high 1
## Women_with_advanced_education_proportion_of_females 1
## Expected_years_of_tertiary_schooling_years 1
## Citable_documents_documents_1_000_people 1
## Quality_weighted_universities_points 1
##
## MR1
## SS loadings 28.43
## Proportion Var 0.47
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 1770 with the objective function = 96.6 with Chi Square = 13245
## df of the model are 1710 and the objective function was 56.4
##
## The root mean square of the residuals (RMSR) is 0.14
## The df corrected root mean square of the residuals is 0.15
##
## The harmonic n.obs is 159 with the empirical chi square 11783 with prob < 0
## The total n.obs was 159 with Likelihood Chi Square = 7702 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.456
## RMSEA index = 0.148 and the 90 % confidence intervals are 0.146 0.152
## BIC = -966
## Fit based upon off diagonal values = 0.91
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 1.00
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.99spi_efa %>% fa_plot_loadings() +
geom_point(mapping = aes(color = fa)) +
scale_color_gradient(low = "red", high = "blue", guide = NULL) +
ylim(-1, 1) +
ggtitle("Exploratory factor analysis of Social Progress Index indicators",
subtitle = str_glue("n = {nrow(d_spi)} countries"))
GG_save("figs/SPI EFA.png")
#save scores to main dataset
#remove if exists
d$SPI_EFA = NULL
d = full_join(
tibble(
SPI_EFA = spi_efa$scores[, 1] %>% standardize(),
ISO = d_spi$ISO
),
d
)## Joining with `by = join_by(ISO)`#compare
GG_scatter(d, "SPI_2019", "SPI_EFA", case_names = "nice_name") +
ylab("SPI, factor score") +
xlab("SPI, premade score")## `geom_smooth()` using formula = 'y ~ x'
GG_save("figs/SPI premade vs. EFA.png")## `geom_smooth()` using formula = 'y ~ x'd %>% filter(!is.na(SPI_2019)) %>% select(nice_name, SPI_2019, SPI_EFA) %>% mutate(SPI_2019_rank = rank(-SPI_2019), SPI_EFA_rank = rank(-SPI_EFA), rank_diff = SPI_2019_rank - SPI_EFA_rank) %>% arrange(-SPI_EFA) %>% head(30) %>% print(n=Inf)## # A tibble: 30 × 6
## nice_name SPI_2019 SPI_EFA SPI_2019_rank SPI_EFA_rank rank_diff
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Netherlands 89.4 1.61 6 1 5
## 2 Norway 90.8 1.58 1 2 -1
## 3 Luxembourg 87.9 1.58 11 3 8
## 4 Germany 88.8 1.54 8 4 4
## 5 Sweden 89.2 1.54 7 5 2
## 6 Canada 88.0 1.53 10 6 4
## 7 Switzerland 89.6 1.50 4 7 -3
## 8 Finland 90.5 1.49 3 8 -5
## 9 France 86.3 1.48 18 9 9
## 10 Iceland 89.5 1.47 5 10 -5
## 11 Denmark 90.6 1.46 2 11 -9
## 12 Japan 87.7 1.46 13 12 1
## 13 Australia 88.1 1.41 9 13 -4
## 14 Ireland 87.8 1.40 12 14 -2
## 15 United Kingdom 86.5 1.40 17 15 2
## 16 Belgium 87.1 1.39 16 16 0
## 17 New Zealand 87.4 1.38 15 17 -2
## 18 Slovenia 85.3 1.35 21 18 3
## 19 Austria 87.4 1.35 14 19 -5
## 20 Czech Republic 84.5 1.27 25 20 5
## 21 Estonia 85.3 1.25 22 21 1
## 22 Italy 84.4 1.22 26 22 4
## 23 Spain 85.5 1.22 20 23 -3
## 24 Portugal 85 1.20 24 24 0
## 25 Cyprus 82.2 1.12 30 25 5
## 26 USA 85.1 1.12 23 26 -3
## 27 Singapore 83.4 1.12 27 27 0
## 28 Malta 83.2 1.11 28 28 0
## 29 South Korea 85.6 1.09 19 29 -10
## 30 Slovakia 80.6 1.05 36 30 6#rerun with economic indicators
d %>% select(GNIpc_PPP_2019, GDPpc_PPP_2019, meanIncome, medianIncome) %>% wtd.cors()## GNIpc_PPP_2019 GDPpc_PPP_2019 meanIncome medianIncome
## GNIpc_PPP_2019 1.000 0.985 0.973 0.970
## GDPpc_PPP_2019 0.985 1.000 0.954 0.950
## meanIncome 0.973 0.954 1.000 0.995
## medianIncome 0.970 0.950 0.995 1.000d %>% select(GNIpc_PPP_2019, GDPpc_PPP_2019, meanIncome, medianIncome) %>% map_df(log10_fix) %>% wtd.cors()## GNIpc_PPP_2019 GDPpc_PPP_2019 meanIncome medianIncome
## GNIpc_PPP_2019 1.000 0.998 0.954 0.955
## GDPpc_PPP_2019 0.998 1.000 0.932 0.931
## meanIncome 0.954 0.932 1.000 0.994
## medianIncome 0.955 0.931 0.994 1.000#EFA of SPI + GNI + median income
d_spi2 = d %>% select(ISO, Child_stunting_0_low_risk_100_high_risk:Quality_weighted_universities_points, GDPpc_PPP_2019_log10) %>%
miss_filter(missing = imp_max_miss)
d_spi2_imp = miss_impute(d_spi2, leave_out = "ISO")
#EFA
spi2_efa = fa(
d_spi2_imp %>% select(-ISO)
)
spi2_efa## Factor Analysis using method = minres
## Call: fa(r = d_spi2_imp %>% select(-ISO))
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1
## Child_stunting_0_low_risk_100_high_risk -0.87
## Infectious_diseases_DALYs_100_000 -0.74
## Maternal_mortality_rate_deaths_100_000_live_births -0.82
## Child_mortality_rate_deaths_1_000_live_births -0.87
## Undernourishment_pct_of_pop -0.74
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk -0.60
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 -0.78
## Access_to_improved_sanitation_proportion_of_pop 0.85
## Access_to_improved_water_source_proportion_of_pop 0.79
## Satisfaction_with_water_quality_proportion_of_pop 0.72
## Household_air_pollution_DALYs_100_000 -0.84
## Dissatisfaction_with_housing_affordability_proportion_of_pop -0.24
## Access_to_electricity_pct_of_pop 0.77
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.83
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.69
## Interpersonal_violence_DALYs_100_000 -0.28
## Transportation_related_injuries_DALYs_100_000 -0.62
## Intimate_Partner_Violence_pct_of_women_aged_15plus -0.81
## Money_Stolen_proportion_of_pop -0.59
## Equal_access_to_quality_education_0_unequal_4_equal 0.79
## Population_with_no_schooling_proportion_of_pop -0.75
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.80
## Primary_school_enrollment_pct_of_children 0.67
## Gender_parity_in_secondary_attainment_distance_from_parity -0.78
## Alternative_sources_of_information_index_0_low_1_high 0.33
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.64
## Internet_users_pct_of_pop 0.90
## Access_to_online_governance_0_low_1_high 0.78
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.85
## Life_expectancy_at_60_years 0.82
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 -0.61
## Access_to_essential_health_services_0_none_100_full_coverage 0.95
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.54
## Outdoor_air_pollution_DALYs_100_000 -0.35
## Lead_exposure_DALYs_100_000 -0.65
## Particulate_matter_pollution_mean_annual_exposure_g_m3 -0.55
## Species_protection_0_low_100_high 0.30
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.34
## Property_rights_for_women_0_no_rights_5_full_rights 0.75
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.56
## Access_to_justice_0_nonexistent_1_observed 0.70
## Freedom_of_discussion_0_low_1_high 0.54
## Political_rights_0_and_lower_no_rights_40_full_rights 0.68
## Freedom_of_domestic_movement_0_low_1_high 0.59
## Early_marriage_pct_of_married_women_aged_15_19 -0.73
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.61
## Young_people_not_in_education_employment_or_training_pct_of_youth -0.50
## Vulnerable_employment_pct_of_total_employment -0.85
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.81
## Equal_protection_index_0_low_1_high 0.67
## Equal_access_index_0_low_1_high 0.58
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.65
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.76
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.60
## Discrimination_and_violence_against_minorities_0_low_10_high -0.50
## Academic_freedom_0_low_1_high 0.50
## Women_with_advanced_education_proportion_of_females 0.86
## Expected_years_of_tertiary_schooling_years 0.81
## Citable_documents_documents_1_000_people 0.74
## Quality_weighted_universities_points 0.28
## GDPpc_PPP_2019_log10 0.92
## h2
## Child_stunting_0_low_risk_100_high_risk 0.756
## Infectious_diseases_DALYs_100_000 0.549
## Maternal_mortality_rate_deaths_100_000_live_births 0.672
## Child_mortality_rate_deaths_1_000_live_births 0.753
## Undernourishment_pct_of_pop 0.541
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 0.361
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 0.606
## Access_to_improved_sanitation_proportion_of_pop 0.726
## Access_to_improved_water_source_proportion_of_pop 0.621
## Satisfaction_with_water_quality_proportion_of_pop 0.516
## Household_air_pollution_DALYs_100_000 0.701
## Dissatisfaction_with_housing_affordability_proportion_of_pop 0.059
## Access_to_electricity_pct_of_pop 0.599
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.687
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.478
## Interpersonal_violence_DALYs_100_000 0.080
## Transportation_related_injuries_DALYs_100_000 0.390
## Intimate_Partner_Violence_pct_of_women_aged_15plus 0.655
## Money_Stolen_proportion_of_pop 0.348
## Equal_access_to_quality_education_0_unequal_4_equal 0.627
## Population_with_no_schooling_proportion_of_pop 0.567
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.633
## Primary_school_enrollment_pct_of_children 0.448
## Gender_parity_in_secondary_attainment_distance_from_parity 0.601
## Alternative_sources_of_information_index_0_low_1_high 0.110
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.410
## Internet_users_pct_of_pop 0.814
## Access_to_online_governance_0_low_1_high 0.606
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.718
## Life_expectancy_at_60_years 0.678
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 0.370
## Access_to_essential_health_services_0_none_100_full_coverage 0.910
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.286
## Outdoor_air_pollution_DALYs_100_000 0.119
## Lead_exposure_DALYs_100_000 0.421
## Particulate_matter_pollution_mean_annual_exposure_g_m3 0.303
## Species_protection_0_low_100_high 0.088
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.118
## Property_rights_for_women_0_no_rights_5_full_rights 0.564
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.313
## Access_to_justice_0_nonexistent_1_observed 0.485
## Freedom_of_discussion_0_low_1_high 0.296
## Political_rights_0_and_lower_no_rights_40_full_rights 0.461
## Freedom_of_domestic_movement_0_low_1_high 0.348
## Early_marriage_pct_of_married_women_aged_15_19 0.528
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.373
## Young_people_not_in_education_employment_or_training_pct_of_youth 0.252
## Vulnerable_employment_pct_of_total_employment 0.720
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.655
## Equal_protection_index_0_low_1_high 0.452
## Equal_access_index_0_low_1_high 0.335
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.420
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.576
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.363
## Discrimination_and_violence_against_minorities_0_low_10_high 0.254
## Academic_freedom_0_low_1_high 0.251
## Women_with_advanced_education_proportion_of_females 0.747
## Expected_years_of_tertiary_schooling_years 0.651
## Citable_documents_documents_1_000_people 0.540
## Quality_weighted_universities_points 0.078
## GDPpc_PPP_2019_log10 0.848
## u2
## Child_stunting_0_low_risk_100_high_risk 0.24
## Infectious_diseases_DALYs_100_000 0.45
## Maternal_mortality_rate_deaths_100_000_live_births 0.33
## Child_mortality_rate_deaths_1_000_live_births 0.25
## Undernourishment_pct_of_pop 0.46
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 0.64
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 0.39
## Access_to_improved_sanitation_proportion_of_pop 0.27
## Access_to_improved_water_source_proportion_of_pop 0.38
## Satisfaction_with_water_quality_proportion_of_pop 0.48
## Household_air_pollution_DALYs_100_000 0.30
## Dissatisfaction_with_housing_affordability_proportion_of_pop 0.94
## Access_to_electricity_pct_of_pop 0.40
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.31
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.52
## Interpersonal_violence_DALYs_100_000 0.92
## Transportation_related_injuries_DALYs_100_000 0.61
## Intimate_Partner_Violence_pct_of_women_aged_15plus 0.34
## Money_Stolen_proportion_of_pop 0.65
## Equal_access_to_quality_education_0_unequal_4_equal 0.37
## Population_with_no_schooling_proportion_of_pop 0.43
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.37
## Primary_school_enrollment_pct_of_children 0.55
## Gender_parity_in_secondary_attainment_distance_from_parity 0.40
## Alternative_sources_of_information_index_0_low_1_high 0.89
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.59
## Internet_users_pct_of_pop 0.19
## Access_to_online_governance_0_low_1_high 0.39
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.28
## Life_expectancy_at_60_years 0.32
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 0.63
## Access_to_essential_health_services_0_none_100_full_coverage 0.09
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.71
## Outdoor_air_pollution_DALYs_100_000 0.88
## Lead_exposure_DALYs_100_000 0.58
## Particulate_matter_pollution_mean_annual_exposure_g_m3 0.70
## Species_protection_0_low_100_high 0.91
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.88
## Property_rights_for_women_0_no_rights_5_full_rights 0.44
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.69
## Access_to_justice_0_nonexistent_1_observed 0.52
## Freedom_of_discussion_0_low_1_high 0.70
## Political_rights_0_and_lower_no_rights_40_full_rights 0.54
## Freedom_of_domestic_movement_0_low_1_high 0.65
## Early_marriage_pct_of_married_women_aged_15_19 0.47
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.63
## Young_people_not_in_education_employment_or_training_pct_of_youth 0.75
## Vulnerable_employment_pct_of_total_employment 0.28
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.34
## Equal_protection_index_0_low_1_high 0.55
## Equal_access_index_0_low_1_high 0.67
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.58
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.42
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.64
## Discrimination_and_violence_against_minorities_0_low_10_high 0.75
## Academic_freedom_0_low_1_high 0.75
## Women_with_advanced_education_proportion_of_females 0.25
## Expected_years_of_tertiary_schooling_years 0.35
## Citable_documents_documents_1_000_people 0.46
## Quality_weighted_universities_points 0.92
## GDPpc_PPP_2019_log10 0.15
## com
## Child_stunting_0_low_risk_100_high_risk 1
## Infectious_diseases_DALYs_100_000 1
## Maternal_mortality_rate_deaths_100_000_live_births 1
## Child_mortality_rate_deaths_1_000_live_births 1
## Undernourishment_pct_of_pop 1
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 1
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 1
## Access_to_improved_sanitation_proportion_of_pop 1
## Access_to_improved_water_source_proportion_of_pop 1
## Satisfaction_with_water_quality_proportion_of_pop 1
## Household_air_pollution_DALYs_100_000 1
## Dissatisfaction_with_housing_affordability_proportion_of_pop 1
## Access_to_electricity_pct_of_pop 1
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 1
## Political_killings_and_torture_0_low_freedom_1_high_freedom 1
## Interpersonal_violence_DALYs_100_000 1
## Transportation_related_injuries_DALYs_100_000 1
## Intimate_Partner_Violence_pct_of_women_aged_15plus 1
## Money_Stolen_proportion_of_pop 1
## Equal_access_to_quality_education_0_unequal_4_equal 1
## Population_with_no_schooling_proportion_of_pop 1
## Secondary_school_attainment_pct_of_pop_aged_25plus 1
## Primary_school_enrollment_pct_of_children 1
## Gender_parity_in_secondary_attainment_distance_from_parity 1
## Alternative_sources_of_information_index_0_low_1_high 1
## Mobile_telephone_subscriptions_subscriptions_1_000_people 1
## Internet_users_pct_of_pop 1
## Access_to_online_governance_0_low_1_high 1
## Equal_access_to_quality_healthcare_0_unequal_4_equal 1
## Life_expectancy_at_60_years 1
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 1
## Access_to_essential_health_services_0_none_100_full_coverage 1
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 1
## Outdoor_air_pollution_DALYs_100_000 1
## Lead_exposure_DALYs_100_000 1
## Particulate_matter_pollution_mean_annual_exposure_g_m3 1
## Species_protection_0_low_100_high 1
## Freedom_of_religion_0_no_freedom_4_full_freedom 1
## Property_rights_for_women_0_no_rights_5_full_rights 1
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 1
## Access_to_justice_0_nonexistent_1_observed 1
## Freedom_of_discussion_0_low_1_high 1
## Political_rights_0_and_lower_no_rights_40_full_rights 1
## Freedom_of_domestic_movement_0_low_1_high 1
## Early_marriage_pct_of_married_women_aged_15_19 1
## Satisfied_demand_for_contraception_pct_satisfied_demand 1
## Young_people_not_in_education_employment_or_training_pct_of_youth 1
## Vulnerable_employment_pct_of_total_employment 1
## Perception_of_corruption_0_high_corruption_100_low_corruption 1
## Equal_protection_index_0_low_1_high 1
## Equal_access_index_0_low_1_high 1
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 1
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 1
## Acceptance_of_gays_and_lesbians_proportion_of_pop 1
## Discrimination_and_violence_against_minorities_0_low_10_high 1
## Academic_freedom_0_low_1_high 1
## Women_with_advanced_education_proportion_of_females 1
## Expected_years_of_tertiary_schooling_years 1
## Citable_documents_documents_1_000_people 1
## Quality_weighted_universities_points 1
## GDPpc_PPP_2019_log10 1
##
## MR1
## SS loadings 29.43
## Proportion Var 0.48
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 1830 with the objective function = 100 with Chi Square = 13637
## df of the model are 1769 and the objective function was 57.9
##
## The root mean square of the residuals (RMSR) is 0.14
## The df corrected root mean square of the residuals is 0.14
##
## The harmonic n.obs is 158 with the empirical chi square 11746 with prob < 0
## The total n.obs was 158 with Likelihood Chi Square = 7825 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.466
## RMSEA index = 0.147 and the 90 % confidence intervals are 0.144 0.151
## BIC = -1131
## Fit based upon off diagonal values = 0.92
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 1.00
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.99spi2_efa %>% fa_plot_loadings() +
geom_point(mapping = aes(color = fa)) +
scale_color_gradient(low = "red", high = "blue", guide = NULL) +
ylim(-1, 1) +
ggtitle("Exploratory factor analysis of Social Progress Index indicators + GDP + median income",
subtitle = str_glue("n = {nrow(d_spi2)} countries"))
GG_save("figs/SPI2 EFA.png")
#save scores to main dataset
#remove if exists
d$SPI2_EFA = NULL
d = full_join(
tibble(
SPI2_EFA = spi2_efa$scores[, 1] %>% standardize(),
ISO = d_spi2$ISO
),
d
)## Joining with `by = join_by(ISO)`d %>% filter(!is.na(SPI_EFA)) %>% select(nice_name, SPI_EFA, SPI2_EFA) %>% mutate(SPI_EFA_rank = rank(-SPI_EFA), SPI2_EFA_rank = rank(-SPI2_EFA), rank_diff = SPI_EFA_rank - SPI2_EFA_rank) %>% arrange(-SPI2_EFA) %>% head(30) %>% print(n=Inf)## # A tibble: 30 × 6
## nice_name SPI_EFA SPI2_EFA SPI_EFA_rank SPI2_EFA_rank rank_diff
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Luxembourg 1.58 1.67 3 1 2
## 2 Netherlands 1.61 1.60 1 2 -1
## 3 Norway 1.58 1.59 2 3 -1
## 4 Germany 1.54 1.53 4 4 0
## 5 Switzerland 1.50 1.50 7 5 2
## 6 Ireland 1.40 1.50 14 6 8
## 7 Sweden 1.54 1.50 5 7 -2
## 8 Canada 1.53 1.49 6 8 -2
## 9 Finland 1.49 1.47 8 9 -1
## 10 France 1.48 1.46 9 10 -1
## 11 Iceland 1.47 1.45 10 11 -1
## 12 Denmark 1.46 1.45 11 12 -1
## 13 Japan 1.46 1.43 12 13 -1
## 14 Belgium 1.39 1.40 16 14 2
## 15 Australia 1.41 1.39 13 15 -2
## 16 United Kingdom 1.40 1.36 15 16 -1
## 17 Austria 1.35 1.36 19 17 2
## 18 New Zealand 1.38 1.33 17 18 -1
## 19 Slovenia 1.35 1.33 18 19 -1
## 20 Italy 1.22 1.27 22 20 2
## 21 Czech Republic 1.27 1.27 20 21 -1
## 22 Spain 1.22 1.22 23 22 1
## 23 Estonia 1.25 1.20 21 23 -2
## 24 Portugal 1.20 1.19 24 24 0
## 25 Singapore 1.12 1.17 27 25 2
## 26 USA 1.12 1.13 26 26 0
## 27 Cyprus 1.12 1.13 25 27 -2
## 28 Malta 1.11 1.13 28 28 0
## 29 South Korea 1.09 1.10 29 29 0
## 30 Greece 1.04 1.06 33 30 3#SPI with median income
d_spi3 = d %>% select(ISO, Child_stunting_0_low_risk_100_high_risk:Quality_weighted_universities_points, medianIncome_log10) %>%
miss_filter(missing = imp_max_miss)
d_spi3_imp = miss_impute(d_spi3, leave_out = "ISO")
#EFA
spi3_efa = fa(
d_spi3_imp %>% select(-ISO)
)
spi3_efa## Factor Analysis using method = minres
## Call: fa(r = d_spi3_imp %>% select(-ISO))
## Standardized loadings (pattern matrix) based upon correlation matrix
## MR1
## Child_stunting_0_low_risk_100_high_risk -0.87
## Infectious_diseases_DALYs_100_000 -0.73
## Maternal_mortality_rate_deaths_100_000_live_births -0.81
## Child_mortality_rate_deaths_1_000_live_births -0.86
## Undernourishment_pct_of_pop -0.72
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk -0.59
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 -0.77
## Access_to_improved_sanitation_proportion_of_pop 0.85
## Access_to_improved_water_source_proportion_of_pop 0.78
## Satisfaction_with_water_quality_proportion_of_pop 0.74
## Household_air_pollution_DALYs_100_000 -0.83
## Dissatisfaction_with_housing_affordability_proportion_of_pop -0.25
## Access_to_electricity_pct_of_pop 0.77
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.82
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.69
## Interpersonal_violence_DALYs_100_000 -0.28
## Transportation_related_injuries_DALYs_100_000 -0.63
## Intimate_Partner_Violence_pct_of_women_aged_15plus -0.80
## Money_Stolen_proportion_of_pop -0.60
## Equal_access_to_quality_education_0_unequal_4_equal 0.79
## Population_with_no_schooling_proportion_of_pop -0.75
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.80
## Primary_school_enrollment_pct_of_children 0.66
## Gender_parity_in_secondary_attainment_distance_from_parity -0.77
## Alternative_sources_of_information_index_0_low_1_high 0.35
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.63
## Internet_users_pct_of_pop 0.90
## Access_to_online_governance_0_low_1_high 0.78
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.85
## Life_expectancy_at_60_years 0.82
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 -0.60
## Access_to_essential_health_services_0_none_100_full_coverage 0.95
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.55
## Outdoor_air_pollution_DALYs_100_000 -0.38
## Lead_exposure_DALYs_100_000 -0.64
## Particulate_matter_pollution_mean_annual_exposure_g_m3 -0.58
## Species_protection_0_low_100_high 0.31
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.34
## Property_rights_for_women_0_no_rights_5_full_rights 0.75
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.57
## Access_to_justice_0_nonexistent_1_observed 0.69
## Freedom_of_discussion_0_low_1_high 0.55
## Political_rights_0_and_lower_no_rights_40_full_rights 0.68
## Freedom_of_domestic_movement_0_low_1_high 0.59
## Early_marriage_pct_of_married_women_aged_15_19 -0.72
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.60
## Young_people_not_in_education_employment_or_training_pct_of_youth -0.50
## Vulnerable_employment_pct_of_total_employment -0.85
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.81
## Equal_protection_index_0_low_1_high 0.67
## Equal_access_index_0_low_1_high 0.60
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.65
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.76
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.62
## Discrimination_and_violence_against_minorities_0_low_10_high -0.50
## Academic_freedom_0_low_1_high 0.51
## Women_with_advanced_education_proportion_of_females 0.87
## Expected_years_of_tertiary_schooling_years 0.81
## Citable_documents_documents_1_000_people 0.74
## Quality_weighted_universities_points 0.27
## medianIncome_log10 0.88
## h2
## Child_stunting_0_low_risk_100_high_risk 0.763
## Infectious_diseases_DALYs_100_000 0.535
## Maternal_mortality_rate_deaths_100_000_live_births 0.661
## Child_mortality_rate_deaths_1_000_live_births 0.746
## Undernourishment_pct_of_pop 0.518
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 0.351
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 0.591
## Access_to_improved_sanitation_proportion_of_pop 0.718
## Access_to_improved_water_source_proportion_of_pop 0.614
## Satisfaction_with_water_quality_proportion_of_pop 0.541
## Household_air_pollution_DALYs_100_000 0.690
## Dissatisfaction_with_housing_affordability_proportion_of_pop 0.061
## Access_to_electricity_pct_of_pop 0.593
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.677
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.476
## Interpersonal_violence_DALYs_100_000 0.077
## Transportation_related_injuries_DALYs_100_000 0.396
## Intimate_Partner_Violence_pct_of_women_aged_15plus 0.647
## Money_Stolen_proportion_of_pop 0.361
## Equal_access_to_quality_education_0_unequal_4_equal 0.623
## Population_with_no_schooling_proportion_of_pop 0.561
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.644
## Primary_school_enrollment_pct_of_children 0.432
## Gender_parity_in_secondary_attainment_distance_from_parity 0.588
## Alternative_sources_of_information_index_0_low_1_high 0.121
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.396
## Internet_users_pct_of_pop 0.818
## Access_to_online_governance_0_low_1_high 0.601
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.720
## Life_expectancy_at_60_years 0.679
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 0.364
## Access_to_essential_health_services_0_none_100_full_coverage 0.910
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.300
## Outdoor_air_pollution_DALYs_100_000 0.148
## Lead_exposure_DALYs_100_000 0.410
## Particulate_matter_pollution_mean_annual_exposure_g_m3 0.341
## Species_protection_0_low_100_high 0.097
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.113
## Property_rights_for_women_0_no_rights_5_full_rights 0.558
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.330
## Access_to_justice_0_nonexistent_1_observed 0.476
## Freedom_of_discussion_0_low_1_high 0.307
## Political_rights_0_and_lower_no_rights_40_full_rights 0.466
## Freedom_of_domestic_movement_0_low_1_high 0.349
## Early_marriage_pct_of_married_women_aged_15_19 0.524
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.355
## Young_people_not_in_education_employment_or_training_pct_of_youth 0.254
## Vulnerable_employment_pct_of_total_employment 0.715
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.655
## Equal_protection_index_0_low_1_high 0.454
## Equal_access_index_0_low_1_high 0.357
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.425
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.579
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.386
## Discrimination_and_violence_against_minorities_0_low_10_high 0.249
## Academic_freedom_0_low_1_high 0.261
## Women_with_advanced_education_proportion_of_females 0.753
## Expected_years_of_tertiary_schooling_years 0.660
## Citable_documents_documents_1_000_people 0.551
## Quality_weighted_universities_points 0.076
## medianIncome_log10 0.774
## u2
## Child_stunting_0_low_risk_100_high_risk 0.24
## Infectious_diseases_DALYs_100_000 0.46
## Maternal_mortality_rate_deaths_100_000_live_births 0.34
## Child_mortality_rate_deaths_1_000_live_births 0.25
## Undernourishment_pct_of_pop 0.48
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 0.65
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 0.41
## Access_to_improved_sanitation_proportion_of_pop 0.28
## Access_to_improved_water_source_proportion_of_pop 0.39
## Satisfaction_with_water_quality_proportion_of_pop 0.46
## Household_air_pollution_DALYs_100_000 0.31
## Dissatisfaction_with_housing_affordability_proportion_of_pop 0.94
## Access_to_electricity_pct_of_pop 0.41
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.32
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.52
## Interpersonal_violence_DALYs_100_000 0.92
## Transportation_related_injuries_DALYs_100_000 0.60
## Intimate_Partner_Violence_pct_of_women_aged_15plus 0.35
## Money_Stolen_proportion_of_pop 0.64
## Equal_access_to_quality_education_0_unequal_4_equal 0.38
## Population_with_no_schooling_proportion_of_pop 0.44
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.36
## Primary_school_enrollment_pct_of_children 0.57
## Gender_parity_in_secondary_attainment_distance_from_parity 0.41
## Alternative_sources_of_information_index_0_low_1_high 0.88
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.60
## Internet_users_pct_of_pop 0.18
## Access_to_online_governance_0_low_1_high 0.40
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.28
## Life_expectancy_at_60_years 0.32
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 0.64
## Access_to_essential_health_services_0_none_100_full_coverage 0.09
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.70
## Outdoor_air_pollution_DALYs_100_000 0.85
## Lead_exposure_DALYs_100_000 0.59
## Particulate_matter_pollution_mean_annual_exposure_g_m3 0.66
## Species_protection_0_low_100_high 0.90
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.89
## Property_rights_for_women_0_no_rights_5_full_rights 0.44
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.67
## Access_to_justice_0_nonexistent_1_observed 0.52
## Freedom_of_discussion_0_low_1_high 0.69
## Political_rights_0_and_lower_no_rights_40_full_rights 0.53
## Freedom_of_domestic_movement_0_low_1_high 0.65
## Early_marriage_pct_of_married_women_aged_15_19 0.48
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.64
## Young_people_not_in_education_employment_or_training_pct_of_youth 0.75
## Vulnerable_employment_pct_of_total_employment 0.29
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.35
## Equal_protection_index_0_low_1_high 0.55
## Equal_access_index_0_low_1_high 0.64
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.57
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.42
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.61
## Discrimination_and_violence_against_minorities_0_low_10_high 0.75
## Academic_freedom_0_low_1_high 0.74
## Women_with_advanced_education_proportion_of_females 0.25
## Expected_years_of_tertiary_schooling_years 0.34
## Citable_documents_documents_1_000_people 0.45
## Quality_weighted_universities_points 0.92
## medianIncome_log10 0.23
## com
## Child_stunting_0_low_risk_100_high_risk 1
## Infectious_diseases_DALYs_100_000 1
## Maternal_mortality_rate_deaths_100_000_live_births 1
## Child_mortality_rate_deaths_1_000_live_births 1
## Undernourishment_pct_of_pop 1
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk 1
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000 1
## Access_to_improved_sanitation_proportion_of_pop 1
## Access_to_improved_water_source_proportion_of_pop 1
## Satisfaction_with_water_quality_proportion_of_pop 1
## Household_air_pollution_DALYs_100_000 1
## Dissatisfaction_with_housing_affordability_proportion_of_pop 1
## Access_to_electricity_pct_of_pop 1
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 1
## Political_killings_and_torture_0_low_freedom_1_high_freedom 1
## Interpersonal_violence_DALYs_100_000 1
## Transportation_related_injuries_DALYs_100_000 1
## Intimate_Partner_Violence_pct_of_women_aged_15plus 1
## Money_Stolen_proportion_of_pop 1
## Equal_access_to_quality_education_0_unequal_4_equal 1
## Population_with_no_schooling_proportion_of_pop 1
## Secondary_school_attainment_pct_of_pop_aged_25plus 1
## Primary_school_enrollment_pct_of_children 1
## Gender_parity_in_secondary_attainment_distance_from_parity 1
## Alternative_sources_of_information_index_0_low_1_high 1
## Mobile_telephone_subscriptions_subscriptions_1_000_people 1
## Internet_users_pct_of_pop 1
## Access_to_online_governance_0_low_1_high 1
## Equal_access_to_quality_healthcare_0_unequal_4_equal 1
## Life_expectancy_at_60_years 1
## Premature_deaths_from_non_communicable_diseases_deaths_100_000 1
## Access_to_essential_health_services_0_none_100_full_coverage 1
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 1
## Outdoor_air_pollution_DALYs_100_000 1
## Lead_exposure_DALYs_100_000 1
## Particulate_matter_pollution_mean_annual_exposure_g_m3 1
## Species_protection_0_low_100_high 1
## Freedom_of_religion_0_no_freedom_4_full_freedom 1
## Property_rights_for_women_0_no_rights_5_full_rights 1
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 1
## Access_to_justice_0_nonexistent_1_observed 1
## Freedom_of_discussion_0_low_1_high 1
## Political_rights_0_and_lower_no_rights_40_full_rights 1
## Freedom_of_domestic_movement_0_low_1_high 1
## Early_marriage_pct_of_married_women_aged_15_19 1
## Satisfied_demand_for_contraception_pct_satisfied_demand 1
## Young_people_not_in_education_employment_or_training_pct_of_youth 1
## Vulnerable_employment_pct_of_total_employment 1
## Perception_of_corruption_0_high_corruption_100_low_corruption 1
## Equal_protection_index_0_low_1_high 1
## Equal_access_index_0_low_1_high 1
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 1
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 1
## Acceptance_of_gays_and_lesbians_proportion_of_pop 1
## Discrimination_and_violence_against_minorities_0_low_10_high 1
## Academic_freedom_0_low_1_high 1
## Women_with_advanced_education_proportion_of_females 1
## Expected_years_of_tertiary_schooling_years 1
## Citable_documents_documents_1_000_people 1
## Quality_weighted_universities_points 1
## medianIncome_log10 1
##
## MR1
## SS loadings 29.40
## Proportion Var 0.48
##
## Mean item complexity = 1
## Test of the hypothesis that 1 factor is sufficient.
##
## df null model = 1830 with the objective function = 101 with Chi Square = 13386
## df of the model are 1769 and the objective function was 58.8
##
## The root mean square of the residuals (RMSR) is 0.14
## The df corrected root mean square of the residuals is 0.14
##
## The harmonic n.obs is 155 with the empirical chi square 11363 with prob < 0
## The total n.obs was 155 with Likelihood Chi Square = 7771 with prob < 0
##
## Tucker Lewis Index of factoring reliability = 0.46
## RMSEA index = 0.148 and the 90 % confidence intervals are 0.145 0.152
## BIC = -1151
## Fit based upon off diagonal values = 0.92
## Measures of factor score adequacy
## MR1
## Correlation of (regression) scores with factors 1.00
## Multiple R square of scores with factors 0.99
## Minimum correlation of possible factor scores 0.99spi3_efa %>% fa_plot_loadings() +
geom_point(mapping = aes(color = fa)) +
scale_color_gradient(low = "red", high = "blue", guide = NULL) +
ylim(-1, 1) +
ggtitle("Exploratory factor analysis of Social Progress Index indicators + GDP + median income",
subtitle = str_glue("n = {nrow(d_spi3)} countries"))
GG_save("figs/SPI3 EFA.png")
#save scores to main dataset
#remove if exists
d$SPI3_EFA = NULL
d = full_join(
tibble(
SPI3_EFA = spi3_efa$scores[, 1] %>% standardize(),
ISO = d_spi3$ISO
),
d
)## Joining with `by = join_by(ISO)`d %>% filter(!is.na(SPI_EFA)) %>% select(nice_name, SPI_EFA, SPI3_EFA) %>% mutate(SPI_EFA_rank = rank(-SPI_EFA), SPI3_EFA_rank = rank(-SPI3_EFA), rank_diff = SPI_EFA_rank - SPI3_EFA_rank) %>% arrange(-SPI3_EFA) %>% head(30) %>% print(n=Inf)## # A tibble: 30 × 6
## nice_name SPI_EFA SPI3_EFA SPI_EFA_rank SPI3_EFA_rank rank_diff
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Netherlands 1.61 1.62 1 1 0
## 2 Norway 1.58 1.59 2 2 0
## 3 Luxembourg 1.58 1.59 3 3 0
## 4 Sweden 1.54 1.54 5 4 1
## 5 Canada 1.53 1.54 6 5 1
## 6 Germany 1.54 1.52 4 6 -2
## 7 Iceland 1.47 1.52 10 7 3
## 8 Switzerland 1.50 1.51 7 8 -1
## 9 Finland 1.49 1.48 8 9 -1
## 10 Denmark 1.46 1.48 11 10 1
## 11 Japan 1.46 1.48 12 11 1
## 12 France 1.48 1.46 9 12 -3
## 13 Belgium 1.39 1.42 16 13 3
## 14 United Kingdom 1.40 1.41 15 14 1
## 15 Australia 1.41 1.41 13 15 -2
## 16 Ireland 1.40 1.39 14 16 -2
## 17 New Zealand 1.38 1.39 17 17 0
## 18 Austria 1.35 1.34 19 18 1
## 19 Slovenia 1.35 1.33 18 19 -1
## 20 Spain 1.22 1.27 23 20 3
## 21 Czech Republic 1.27 1.26 20 21 -1
## 22 Estonia 1.25 1.24 21 22 -1
## 23 Portugal 1.20 1.21 24 23 1
## 24 Italy 1.22 1.18 22 24 -2
## 25 USA 1.12 1.14 26 25 1
## 26 Cyprus 1.12 1.13 25 26 -1
## 27 Singapore 1.12 1.12 27 27 0
## 28 Malta 1.11 1.11 28 28 0
## 29 South Korea 1.09 1.10 29 29 0
## 30 Slovakia 1.05 1.06 30 30 0SPI bifactor models
#use their theoretical model
spi_model = str_glue("
#general factor
S =~ {str_c(names(d_spi_all_imp[-1]), collapse = ' + ')}
#nutrition basic medical care
nutrition_basic_med_care =~ Child_stunting_0_low_risk_100_high_risk + Infectious_diseases_DALYs_100_000 + Maternal_mortality_rate_deaths_100_000_live_births + Child_mortality_rate_deaths_1_000_live_births + Undernourishment_pct_of_pop + Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk
#water sanitation
water_sanitation =~ Unsafe_water_sanitation_and_hygiene_DALYs_100_000 + Access_to_improved_sanitation_proportion_of_pop + Access_to_improved_water_source_proportion_of_pop + Satisfaction_with_water_quality_proportion_of_pop
#shelter
shelter =~ Household_air_pollution_DALYs_100_000 + Dissatisfaction_with_housing_affordability_proportion_of_pop + Access_to_electricity_pct_of_pop + Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop
#personal safety
personal_safety =~ Political_killings_and_torture_0_low_freedom_1_high_freedom + Interpersonal_violence_DALYs_100_000 + Transportation_related_injuries_DALYs_100_000 + Intimate_Partner_Violence_pct_of_women_aged_15plus + Money_Stolen_proportion_of_pop
#access basic knowledge
basic_knowledge =~ Equal_access_to_quality_education_0_unequal_4_equal + Population_with_no_schooling_proportion_of_pop + Secondary_school_attainment_pct_of_pop_aged_25plus + Primary_school_enrollment_pct_of_children + Gender_parity_in_secondary_attainment_distance_from_parity
#access to information
info_access =~ Alternative_sources_of_information_index_0_low_1_high + Mobile_telephone_subscriptions_subscriptions_1_000_people + Internet_users_pct_of_pop + Access_to_online_governance_0_low_1_high
#health and wellness
health_wellness =~ Equal_access_to_quality_healthcare_0_unequal_4_equal + Life_expectancy_at_60_years + Premature_deaths_from_non_communicable_diseases_deaths_100_000 + Access_to_essential_health_services_0_none_100_full_coverage + Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop
#environmental quality
envir_quality =~ Outdoor_air_pollution_DALYs_100_000 + Lead_exposure_DALYs_100_000 + Particulate_matter_pollution_mean_annual_exposure_g_m3 + Species_protection_0_low_100_high
#personal rights
personal_rights =~ Freedom_of_religion_0_no_freedom_4_full_freedom + Property_rights_for_women_0_no_rights_5_full_rights + Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom + Access_to_justice_0_nonexistent_1_observed + Freedom_of_discussion_0_low_1_high + Political_rights_0_and_lower_no_rights_40_full_rights
#personal freedom and choice
personal_freedom_a_choice =~ Freedom_of_domestic_movement_0_low_1_high + Early_marriage_pct_of_married_women_aged_15_19 + Satisfied_demand_for_contraception_pct_satisfied_demand + Young_people_not_in_education_employment_or_training_pct_of_youth + Vulnerable_employment_pct_of_total_employment + Perception_of_corruption_0_high_corruption_100_low_corruption
#inclusiveness
inclusiveness =~ Equal_protection_index_0_low_1_high + Equal_access_index_0_low_1_high + Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal + Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal + Acceptance_of_gays_and_lesbians_proportion_of_pop + Discrimination_and_violence_against_minorities_0_low_10_high
#access advanced education
advanced_education =~ Academic_freedom_0_low_1_high + Women_with_advanced_education_proportion_of_females + Expected_years_of_tertiary_schooling_years + Citable_documents_documents_1_000_people + Quality_weighted_universities_points
#economics
economics =~ medianIncome_log10 + meanIncome_log10 + GDPpc_PPP_2019_log10 + GNIpc_PPP_2019_log10
")
#attempt to fit
spi_all_fit_spi_model = sem(
spi_model,
data = d_spi_all_imp_z,
orthogonal = T,
std.lv = T,
std.ov = T,
ridge = 1,
estimator = "GLS"
)
spi_all_fit_spi_model
spi_all_fit_spi_model %>% summary()
parameterestimates(spi_all_fit_spi_model, standardized = T)
#get scores
# spi_all_fit_spi_model_scores = lavaan::lavScores(spi_all_fit_spi_model)
spi_all_fit_spi_model_scores = predict(spi_all_fit_spi_model)SPI hierarhical models
Bifactor failed us, but maybe a hierarchical factor will work.
SPI_hier_model = str_glue("
#nutrition basic medical care
nutrition_basic_med_care =~ Child_stunting_0_low_risk_100_high_risk + Infectious_diseases_DALYs_100_000 + Maternal_mortality_rate_deaths_100_000_live_births + Child_mortality_rate_deaths_1_000_live_births + Undernourishment_pct_of_pop + Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk
#water sanitation
water_sanitation =~ Unsafe_water_sanitation_and_hygiene_DALYs_100_000 + Access_to_improved_sanitation_proportion_of_pop + Access_to_improved_water_source_proportion_of_pop + Satisfaction_with_water_quality_proportion_of_pop
#shelter
shelter =~ Household_air_pollution_DALYs_100_000 + Dissatisfaction_with_housing_affordability_proportion_of_pop + Access_to_electricity_pct_of_pop + Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop
#personal safety
personal_safety =~ Political_killings_and_torture_0_low_freedom_1_high_freedom + Interpersonal_violence_DALYs_100_000 + Transportation_related_injuries_DALYs_100_000 + Intimate_Partner_Violence_pct_of_women_aged_15plus + Money_Stolen_proportion_of_pop
#access basic knowledge
basic_knowledge =~ Equal_access_to_quality_education_0_unequal_4_equal + Population_with_no_schooling_proportion_of_pop + Secondary_school_attainment_pct_of_pop_aged_25plus + Primary_school_enrollment_pct_of_children + Gender_parity_in_secondary_attainment_distance_from_parity
#access to information
info_access =~ Alternative_sources_of_information_index_0_low_1_high + Mobile_telephone_subscriptions_subscriptions_1_000_people + Internet_users_pct_of_pop + Access_to_online_governance_0_low_1_high
#health and wellness
health_wellness =~ Equal_access_to_quality_healthcare_0_unequal_4_equal + Life_expectancy_at_60_years + Premature_deaths_from_non_communicable_diseases_deaths_100_000 + Access_to_essential_health_services_0_none_100_full_coverage + Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop
#environmental quality
envir_quality =~ Outdoor_air_pollution_DALYs_100_000 + Lead_exposure_DALYs_100_000 + Particulate_matter_pollution_mean_annual_exposure_g_m3 + Species_protection_0_low_100_high
#personal rights
personal_rights =~ Freedom_of_religion_0_no_freedom_4_full_freedom + Property_rights_for_women_0_no_rights_5_full_rights + Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom + Access_to_justice_0_nonexistent_1_observed + Freedom_of_discussion_0_low_1_high + Political_rights_0_and_lower_no_rights_40_full_rights
#personal freedom and choice
personal_freedom_a_choice =~ Freedom_of_domestic_movement_0_low_1_high + Early_marriage_pct_of_married_women_aged_15_19 + Satisfied_demand_for_contraception_pct_satisfied_demand + Young_people_not_in_education_employment_or_training_pct_of_youth + Vulnerable_employment_pct_of_total_employment + Perception_of_corruption_0_high_corruption_100_low_corruption
#inclusiveness
inclusiveness =~ Equal_protection_index_0_low_1_high + Equal_access_index_0_low_1_high + Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal + Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal + Acceptance_of_gays_and_lesbians_proportion_of_pop + Discrimination_and_violence_against_minorities_0_low_10_high
#access advanced education
advanced_education =~ Academic_freedom_0_low_1_high + Women_with_advanced_education_proportion_of_females + Expected_years_of_tertiary_schooling_years + Citable_documents_documents_1_000_people + Quality_weighted_universities_points
#economics
economics =~ medianIncome_log10 + meanIncome_log10 + GDPpc_PPP_2019_log10 + GNIpc_PPP_2019_log10
#second order
basic_human_needs =~ nutrition_basic_med_care + water_sanitation + shelter + personal_safety
foundations_wellbeing =~ basic_knowledge + info_access + health_wellness + envir_quality
opportunity =~ personal_rights + personal_freedom_a_choice + inclusiveness + advanced_education
#third order
S =~ basic_human_needs + foundations_wellbeing + opportunity + economics
")
#fit
SPI_hier_fit = sem(
SPI_hier_model,
data = d_spi_all_imp_z,
estimator = "DWLS"
#options: ML GLS WLS ULS DWLS DLS
)EFA bifactor
efa_bifactor = omega(
d_spi_all_imp_z[-1],
nfactors = 3
)## Loading required namespace: GPArotation## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
efa_bifactor## Omega
## Call: omegah(m = m, nfactors = nfactors, fm = fm, key = key, flip = flip,
## digits = digits, title = title, sl = sl, labels = labels,
## plot = plot, n.obs = n.obs, rotate = rotate, Phi = Phi, option = option,
## covar = covar)
## Alpha: 0.98
## G.6: 1
## Omega Hierarchical: 0.61
## Omega H asymptotic: 0.62
## Omega Total 0.99
##
## Schmid Leiman Factor loadings greater than 0.2
## g
## Child_stunting_0_low_risk_100_high_risk- 0.62
## Infectious_diseases_DALYs_100_000- 0.47
## Maternal_mortality_rate_deaths_100_000_live_births- 0.55
## Child_mortality_rate_deaths_1_000_live_births- 0.59
## Undernourishment_pct_of_pop- 0.51
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk- 0.36
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000- 0.48
## Access_to_improved_sanitation_proportion_of_pop 0.56
## Access_to_improved_water_source_proportion_of_pop 0.52
## Satisfaction_with_water_quality_proportion_of_pop 0.65
## Household_air_pollution_DALYs_100_000- 0.51
## Dissatisfaction_with_housing_affordability_proportion_of_pop- 0.22
## Access_to_electricity_pct_of_pop 0.46
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.52
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.57
## Interpersonal_violence_DALYs_100_000- 0.28
## Transportation_related_injuries_DALYs_100_000- 0.53
## Intimate_Partner_Violence_pct_of_women_aged_15plus- 0.60
## Money_Stolen_proportion_of_pop- 0.43
## Equal_access_to_quality_education_0_unequal_4_equal 0.66
## Population_with_no_schooling_proportion_of_pop- 0.51
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.56
## Primary_school_enrollment_pct_of_children 0.47
## Gender_parity_in_secondary_attainment_distance_from_parity- 0.50
## Alternative_sources_of_information_index_0_low_1_high 0.34
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.41
## Internet_users_pct_of_pop 0.68
## Access_to_online_governance_0_low_1_high 0.62
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.69
## Life_expectancy_at_60_years 0.67
## Premature_deaths_from_non_communicable_diseases_deaths_100_000- 0.56
## Access_to_essential_health_services_0_none_100_full_coverage 0.73
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.55
## Outdoor_air_pollution_DALYs_100_000- 0.49
## Lead_exposure_DALYs_100_000- 0.57
## Particulate_matter_pollution_mean_annual_exposure_g_m3- 0.50
## Species_protection_0_low_100_high 0.38
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.37
## Property_rights_for_women_0_no_rights_5_full_rights 0.56
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.47
## Access_to_justice_0_nonexistent_1_observed 0.61
## Freedom_of_discussion_0_low_1_high 0.46
## Political_rights_0_and_lower_no_rights_40_full_rights 0.60
## Freedom_of_domestic_movement_0_low_1_high 0.51
## Early_marriage_pct_of_married_women_aged_15_19- 0.52
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.49
## Young_people_not_in_education_employment_or_training_pct_of_youth- 0.54
## Vulnerable_employment_pct_of_total_employment- 0.59
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.75
## Equal_protection_index_0_low_1_high 0.61
## Equal_access_index_0_low_1_high 0.57
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.61
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.65
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.61
## Discrimination_and_violence_against_minorities_0_low_10_high- 0.48
## Academic_freedom_0_low_1_high 0.46
## Women_with_advanced_education_proportion_of_females 0.66
## Expected_years_of_tertiary_schooling_years 0.58
## Citable_documents_documents_1_000_people 0.71
## Quality_weighted_universities_points 0.23
## medianIncome_log10 0.73
## meanIncome_log10 0.73
## GDPpc_PPP_2019_log10 0.69
## GNIpc_PPP_2019_log10 0.69
## F1*
## Child_stunting_0_low_risk_100_high_risk- 0.63
## Infectious_diseases_DALYs_100_000- 0.72
## Maternal_mortality_rate_deaths_100_000_live_births- 0.71
## Child_mortality_rate_deaths_1_000_live_births- 0.73
## Undernourishment_pct_of_pop- 0.51
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk- 0.65
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000- 0.75
## Access_to_improved_sanitation_proportion_of_pop 0.74
## Access_to_improved_water_source_proportion_of_pop 0.66
## Satisfaction_with_water_quality_proportion_of_pop 0.33
## Household_air_pollution_DALYs_100_000- 0.82
## Dissatisfaction_with_housing_affordability_proportion_of_pop-
## Access_to_electricity_pct_of_pop 0.80
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.77
## Political_killings_and_torture_0_low_freedom_1_high_freedom
## Interpersonal_violence_DALYs_100_000-
## Transportation_related_injuries_DALYs_100_000- 0.30
## Intimate_Partner_Violence_pct_of_women_aged_15plus- 0.52
## Money_Stolen_proportion_of_pop- 0.55
## Equal_access_to_quality_education_0_unequal_4_equal 0.36
## Population_with_no_schooling_proportion_of_pop- 0.59
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.61
## Primary_school_enrollment_pct_of_children 0.46
## Gender_parity_in_secondary_attainment_distance_from_parity- 0.67
## Alternative_sources_of_information_index_0_low_1_high
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.54
## Internet_users_pct_of_pop 0.63
## Access_to_online_governance_0_low_1_high 0.44
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.42
## Life_expectancy_at_60_years 0.43
## Premature_deaths_from_non_communicable_diseases_deaths_100_000- 0.25
## Access_to_essential_health_services_0_none_100_full_coverage 0.61
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop
## Outdoor_air_pollution_DALYs_100_000- -0.20
## Lead_exposure_DALYs_100_000- 0.22
## Particulate_matter_pollution_mean_annual_exposure_g_m3- 0.22
## Species_protection_0_low_100_high
## Freedom_of_religion_0_no_freedom_4_full_freedom -0.21
## Property_rights_for_women_0_no_rights_5_full_rights 0.35
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom
## Access_to_justice_0_nonexistent_1_observed
## Freedom_of_discussion_0_low_1_high
## Political_rights_0_and_lower_no_rights_40_full_rights
## Freedom_of_domestic_movement_0_low_1_high
## Early_marriage_pct_of_married_women_aged_15_19- 0.57
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.31
## Young_people_not_in_education_employment_or_training_pct_of_youth-
## Vulnerable_employment_pct_of_total_employment- 0.70
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.20
## Equal_protection_index_0_low_1_high
## Equal_access_index_0_low_1_high
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.30
## Acceptance_of_gays_and_lesbians_proportion_of_pop
## Discrimination_and_violence_against_minorities_0_low_10_high-
## Academic_freedom_0_low_1_high
## Women_with_advanced_education_proportion_of_females 0.53
## Expected_years_of_tertiary_schooling_years 0.56
## Citable_documents_documents_1_000_people
## Quality_weighted_universities_points
## medianIncome_log10 0.55
## meanIncome_log10 0.55
## GDPpc_PPP_2019_log10 0.65
## GNIpc_PPP_2019_log10 0.65
## F2*
## Child_stunting_0_low_risk_100_high_risk-
## Infectious_diseases_DALYs_100_000-
## Maternal_mortality_rate_deaths_100_000_live_births-
## Child_mortality_rate_deaths_1_000_live_births-
## Undernourishment_pct_of_pop-
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk-
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000-
## Access_to_improved_sanitation_proportion_of_pop
## Access_to_improved_water_source_proportion_of_pop
## Satisfaction_with_water_quality_proportion_of_pop
## Household_air_pollution_DALYs_100_000-
## Dissatisfaction_with_housing_affordability_proportion_of_pop- -0.23
## Access_to_electricity_pct_of_pop
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.66
## Interpersonal_violence_DALYs_100_000-
## Transportation_related_injuries_DALYs_100_000-
## Intimate_Partner_Violence_pct_of_women_aged_15plus-
## Money_Stolen_proportion_of_pop- -0.24
## Equal_access_to_quality_education_0_unequal_4_equal
## Population_with_no_schooling_proportion_of_pop-
## Secondary_school_attainment_pct_of_pop_aged_25plus
## Primary_school_enrollment_pct_of_children
## Gender_parity_in_secondary_attainment_distance_from_parity-
## Alternative_sources_of_information_index_0_low_1_high 0.78
## Mobile_telephone_subscriptions_subscriptions_1_000_people
## Internet_users_pct_of_pop
## Access_to_online_governance_0_low_1_high
## Equal_access_to_quality_healthcare_0_unequal_4_equal
## Life_expectancy_at_60_years
## Premature_deaths_from_non_communicable_diseases_deaths_100_000-
## Access_to_essential_health_services_0_none_100_full_coverage
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop -0.22
## Outdoor_air_pollution_DALYs_100_000- 0.39
## Lead_exposure_DALYs_100_000- 0.24
## Particulate_matter_pollution_mean_annual_exposure_g_m3- 0.33
## Species_protection_0_low_100_high 0.22
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.67
## Property_rights_for_women_0_no_rights_5_full_rights 0.48
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.76
## Access_to_justice_0_nonexistent_1_observed 0.58
## Freedom_of_discussion_0_low_1_high 0.81
## Political_rights_0_and_lower_no_rights_40_full_rights 0.69
## Freedom_of_domestic_movement_0_low_1_high 0.68
## Early_marriage_pct_of_married_women_aged_15_19-
## Satisfied_demand_for_contraception_pct_satisfied_demand
## Young_people_not_in_education_employment_or_training_pct_of_youth-
## Vulnerable_employment_pct_of_total_employment-
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.26
## Equal_protection_index_0_low_1_high 0.52
## Equal_access_index_0_low_1_high 0.66
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.43
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.25
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.27
## Discrimination_and_violence_against_minorities_0_low_10_high- 0.36
## Academic_freedom_0_low_1_high 0.81
## Women_with_advanced_education_proportion_of_females
## Expected_years_of_tertiary_schooling_years
## Citable_documents_documents_1_000_people
## Quality_weighted_universities_points
## medianIncome_log10
## meanIncome_log10
## GDPpc_PPP_2019_log10
## GNIpc_PPP_2019_log10
## F3*
## Child_stunting_0_low_risk_100_high_risk-
## Infectious_diseases_DALYs_100_000-
## Maternal_mortality_rate_deaths_100_000_live_births-
## Child_mortality_rate_deaths_1_000_live_births-
## Undernourishment_pct_of_pop-
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk-
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000-
## Access_to_improved_sanitation_proportion_of_pop
## Access_to_improved_water_source_proportion_of_pop
## Satisfaction_with_water_quality_proportion_of_pop 0.38
## Household_air_pollution_DALYs_100_000-
## Dissatisfaction_with_housing_affordability_proportion_of_pop- 0.24
## Access_to_electricity_pct_of_pop
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop
## Political_killings_and_torture_0_low_freedom_1_high_freedom
## Interpersonal_violence_DALYs_100_000- 0.28
## Transportation_related_injuries_DALYs_100_000-
## Intimate_Partner_Violence_pct_of_women_aged_15plus-
## Money_Stolen_proportion_of_pop-
## Equal_access_to_quality_education_0_unequal_4_equal 0.24
## Population_with_no_schooling_proportion_of_pop-
## Secondary_school_attainment_pct_of_pop_aged_25plus
## Primary_school_enrollment_pct_of_children
## Gender_parity_in_secondary_attainment_distance_from_parity-
## Alternative_sources_of_information_index_0_low_1_high
## Mobile_telephone_subscriptions_subscriptions_1_000_people
## Internet_users_pct_of_pop
## Access_to_online_governance_0_low_1_high
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.22
## Life_expectancy_at_60_years 0.20
## Premature_deaths_from_non_communicable_diseases_deaths_100_000- 0.22
## Access_to_essential_health_services_0_none_100_full_coverage
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.49
## Outdoor_air_pollution_DALYs_100_000- 0.34
## Lead_exposure_DALYs_100_000- 0.21
## Particulate_matter_pollution_mean_annual_exposure_g_m3-
## Species_protection_0_low_100_high 0.29
## Freedom_of_religion_0_no_freedom_4_full_freedom
## Property_rights_for_women_0_no_rights_5_full_rights
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom
## Access_to_justice_0_nonexistent_1_observed
## Freedom_of_discussion_0_low_1_high
## Political_rights_0_and_lower_no_rights_40_full_rights
## Freedom_of_domestic_movement_0_low_1_high
## Early_marriage_pct_of_married_women_aged_15_19-
## Satisfied_demand_for_contraception_pct_satisfied_demand
## Young_people_not_in_education_employment_or_training_pct_of_youth- 0.47
## Vulnerable_employment_pct_of_total_employment-
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.38
## Equal_protection_index_0_low_1_high
## Equal_access_index_0_low_1_high
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.23
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.22
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.32
## Discrimination_and_violence_against_minorities_0_low_10_high-
## Academic_freedom_0_low_1_high
## Women_with_advanced_education_proportion_of_females
## Expected_years_of_tertiary_schooling_years
## Citable_documents_documents_1_000_people 0.41
## Quality_weighted_universities_points
## medianIncome_log10
## meanIncome_log10
## GDPpc_PPP_2019_log10
## GNIpc_PPP_2019_log10
## h2
## Child_stunting_0_low_risk_100_high_risk- 0.81
## Infectious_diseases_DALYs_100_000- 0.74
## Maternal_mortality_rate_deaths_100_000_live_births- 0.82
## Child_mortality_rate_deaths_1_000_live_births- 0.88
## Undernourishment_pct_of_pop- 0.53
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk- 0.58
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000- 0.81
## Access_to_improved_sanitation_proportion_of_pop 0.86
## Access_to_improved_water_source_proportion_of_pop 0.72
## Satisfaction_with_water_quality_proportion_of_pop 0.68
## Household_air_pollution_DALYs_100_000- 0.94
## Dissatisfaction_with_housing_affordability_proportion_of_pop- 0.18
## Access_to_electricity_pct_of_pop 0.87
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.88
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.79
## Interpersonal_violence_DALYs_100_000- 0.18
## Transportation_related_injuries_DALYs_100_000- 0.42
## Intimate_Partner_Violence_pct_of_women_aged_15plus- 0.66
## Money_Stolen_proportion_of_pop- 0.56
## Equal_access_to_quality_education_0_unequal_4_equal 0.65
## Population_with_no_schooling_proportion_of_pop- 0.61
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.69
## Primary_school_enrollment_pct_of_children 0.44
## Gender_parity_in_secondary_attainment_distance_from_parity- 0.70
## Alternative_sources_of_information_index_0_low_1_high 0.75
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.46
## Internet_users_pct_of_pop 0.87
## Access_to_online_governance_0_low_1_high 0.62
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.73
## Life_expectancy_at_60_years 0.70
## Premature_deaths_from_non_communicable_diseases_deaths_100_000- 0.46
## Access_to_essential_health_services_0_none_100_full_coverage 0.94
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.62
## Outdoor_air_pollution_DALYs_100_000- 0.55
## Lead_exposure_DALYs_100_000- 0.47
## Particulate_matter_pollution_mean_annual_exposure_g_m3- 0.42
## Species_protection_0_low_100_high 0.29
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.65
## Property_rights_for_women_0_no_rights_5_full_rights 0.67
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.82
## Access_to_justice_0_nonexistent_1_observed 0.74
## Freedom_of_discussion_0_low_1_high 0.89
## Political_rights_0_and_lower_no_rights_40_full_rights 0.86
## Freedom_of_domestic_movement_0_low_1_high 0.73
## Early_marriage_pct_of_married_women_aged_15_19- 0.59
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.37
## Young_people_not_in_education_employment_or_training_pct_of_youth- 0.52
## Vulnerable_employment_pct_of_total_employment- 0.83
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.82
## Equal_protection_index_0_low_1_high 0.68
## Equal_access_index_0_low_1_high 0.79
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.62
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.63
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.55
## Discrimination_and_violence_against_minorities_0_low_10_high- 0.39
## Academic_freedom_0_low_1_high 0.87
## Women_with_advanced_education_proportion_of_females 0.76
## Expected_years_of_tertiary_schooling_years 0.67
## Citable_documents_documents_1_000_people 0.74
## Quality_weighted_universities_points 0.09
## medianIncome_log10 0.88
## meanIncome_log10 0.87
## GDPpc_PPP_2019_log10 0.91
## GNIpc_PPP_2019_log10 0.92
## u2
## Child_stunting_0_low_risk_100_high_risk- 0.19
## Infectious_diseases_DALYs_100_000- 0.26
## Maternal_mortality_rate_deaths_100_000_live_births- 0.18
## Child_mortality_rate_deaths_1_000_live_births- 0.12
## Undernourishment_pct_of_pop- 0.47
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk- 0.42
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000- 0.19
## Access_to_improved_sanitation_proportion_of_pop 0.14
## Access_to_improved_water_source_proportion_of_pop 0.28
## Satisfaction_with_water_quality_proportion_of_pop 0.32
## Household_air_pollution_DALYs_100_000- 0.06
## Dissatisfaction_with_housing_affordability_proportion_of_pop- 0.82
## Access_to_electricity_pct_of_pop 0.13
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.12
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.21
## Interpersonal_violence_DALYs_100_000- 0.82
## Transportation_related_injuries_DALYs_100_000- 0.58
## Intimate_Partner_Violence_pct_of_women_aged_15plus- 0.34
## Money_Stolen_proportion_of_pop- 0.44
## Equal_access_to_quality_education_0_unequal_4_equal 0.35
## Population_with_no_schooling_proportion_of_pop- 0.39
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.31
## Primary_school_enrollment_pct_of_children 0.56
## Gender_parity_in_secondary_attainment_distance_from_parity- 0.30
## Alternative_sources_of_information_index_0_low_1_high 0.25
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.54
## Internet_users_pct_of_pop 0.13
## Access_to_online_governance_0_low_1_high 0.38
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.27
## Life_expectancy_at_60_years 0.30
## Premature_deaths_from_non_communicable_diseases_deaths_100_000- 0.54
## Access_to_essential_health_services_0_none_100_full_coverage 0.06
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.38
## Outdoor_air_pollution_DALYs_100_000- 0.45
## Lead_exposure_DALYs_100_000- 0.53
## Particulate_matter_pollution_mean_annual_exposure_g_m3- 0.58
## Species_protection_0_low_100_high 0.71
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.35
## Property_rights_for_women_0_no_rights_5_full_rights 0.33
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.18
## Access_to_justice_0_nonexistent_1_observed 0.26
## Freedom_of_discussion_0_low_1_high 0.11
## Political_rights_0_and_lower_no_rights_40_full_rights 0.14
## Freedom_of_domestic_movement_0_low_1_high 0.27
## Early_marriage_pct_of_married_women_aged_15_19- 0.41
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.63
## Young_people_not_in_education_employment_or_training_pct_of_youth- 0.48
## Vulnerable_employment_pct_of_total_employment- 0.17
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.18
## Equal_protection_index_0_low_1_high 0.32
## Equal_access_index_0_low_1_high 0.21
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.38
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.37
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.45
## Discrimination_and_violence_against_minorities_0_low_10_high- 0.61
## Academic_freedom_0_low_1_high 0.13
## Women_with_advanced_education_proportion_of_females 0.24
## Expected_years_of_tertiary_schooling_years 0.33
## Citable_documents_documents_1_000_people 0.26
## Quality_weighted_universities_points 0.91
## medianIncome_log10 0.12
## meanIncome_log10 0.13
## GDPpc_PPP_2019_log10 0.09
## GNIpc_PPP_2019_log10 0.08
## p2
## Child_stunting_0_low_risk_100_high_risk- 0.48
## Infectious_diseases_DALYs_100_000- 0.29
## Maternal_mortality_rate_deaths_100_000_live_births- 0.38
## Child_mortality_rate_deaths_1_000_live_births- 0.39
## Undernourishment_pct_of_pop- 0.49
## Diet_low_in_fruits_and_vegetables_0_low_risk_100_high_risk- 0.22
## Unsafe_water_sanitation_and_hygiene_DALYs_100_000- 0.28
## Access_to_improved_sanitation_proportion_of_pop 0.36
## Access_to_improved_water_source_proportion_of_pop 0.38
## Satisfaction_with_water_quality_proportion_of_pop 0.63
## Household_air_pollution_DALYs_100_000- 0.28
## Dissatisfaction_with_housing_affordability_proportion_of_pop- 0.28
## Access_to_electricity_pct_of_pop 0.25
## Usage_of_clean_fuels_and_technology_for_cooking_pct_of_pop 0.31
## Political_killings_and_torture_0_low_freedom_1_high_freedom 0.42
## Interpersonal_violence_DALYs_100_000- 0.44
## Transportation_related_injuries_DALYs_100_000- 0.67
## Intimate_Partner_Violence_pct_of_women_aged_15plus- 0.55
## Money_Stolen_proportion_of_pop- 0.32
## Equal_access_to_quality_education_0_unequal_4_equal 0.67
## Population_with_no_schooling_proportion_of_pop- 0.43
## Secondary_school_attainment_pct_of_pop_aged_25plus 0.46
## Primary_school_enrollment_pct_of_children 0.51
## Gender_parity_in_secondary_attainment_distance_from_parity- 0.36
## Alternative_sources_of_information_index_0_low_1_high 0.15
## Mobile_telephone_subscriptions_subscriptions_1_000_people 0.37
## Internet_users_pct_of_pop 0.53
## Access_to_online_governance_0_low_1_high 0.62
## Equal_access_to_quality_healthcare_0_unequal_4_equal 0.65
## Life_expectancy_at_60_years 0.64
## Premature_deaths_from_non_communicable_diseases_deaths_100_000- 0.69
## Access_to_essential_health_services_0_none_100_full_coverage 0.56
## Satisfaction_with_availability_of_quality_healthcare_proportion_of_pop 0.49
## Outdoor_air_pollution_DALYs_100_000- 0.43
## Lead_exposure_DALYs_100_000- 0.68
## Particulate_matter_pollution_mean_annual_exposure_g_m3- 0.60
## Species_protection_0_low_100_high 0.50
## Freedom_of_religion_0_no_freedom_4_full_freedom 0.21
## Property_rights_for_women_0_no_rights_5_full_rights 0.47
## Freedom_of_peaceful_assembly_0_no_freedom_4_full_freedom 0.27
## Access_to_justice_0_nonexistent_1_observed 0.51
## Freedom_of_discussion_0_low_1_high 0.24
## Political_rights_0_and_lower_no_rights_40_full_rights 0.42
## Freedom_of_domestic_movement_0_low_1_high 0.36
## Early_marriage_pct_of_married_women_aged_15_19- 0.45
## Satisfied_demand_for_contraception_pct_satisfied_demand 0.65
## Young_people_not_in_education_employment_or_training_pct_of_youth- 0.57
## Vulnerable_employment_pct_of_total_employment- 0.42
## Perception_of_corruption_0_high_corruption_100_low_corruption 0.69
## Equal_protection_index_0_low_1_high 0.55
## Equal_access_index_0_low_1_high 0.42
## Power_distributed_by_sexual_orientation_0_extremely_unequal_3_equal 0.60
## Access_to_public_services_distributed_by_social_group_0_extremely_unequal_4_equal 0.68
## Acceptance_of_gays_and_lesbians_proportion_of_pop 0.67
## Discrimination_and_violence_against_minorities_0_low_10_high- 0.58
## Academic_freedom_0_low_1_high 0.25
## Women_with_advanced_education_proportion_of_females 0.58
## Expected_years_of_tertiary_schooling_years 0.51
## Citable_documents_documents_1_000_people 0.69
## Quality_weighted_universities_points 0.60
## medianIncome_log10 0.60
## meanIncome_log10 0.60
## GDPpc_PPP_2019_log10 0.52
## GNIpc_PPP_2019_log10 0.52
##
## With Sums of squares of:
## g F1* F2* F3*
## 19.8 13.6 7.0 2.2
##
## general/max 1.46 max/min = 6.21
## mean percent general = 0.47 with sd = 0.15 and cv of 0.31
## Explained Common Variance of the general factor = 0.46
##
## The degrees of freedom are 1827 and the fit is 47.1
## The number of observations was 150 with Chi Square = 5884 with prob < 0
## The root mean square of the residuals is 0.05
## The df corrected root mean square of the residuals is 0.05
## RMSEA index = 0.121 and the 10 % confidence intervals are 0.119 0.126
## BIC = -3270
##
## Compare this with the adequacy of just a general factor and no group factors
## The degrees of freedom for just the general factor are 1952 and the fit is 79.8
## The number of observations was 150 with Chi Square = 10070 with prob < 0
## The root mean square of the residuals is 0.25
## The df corrected root mean square of the residuals is 0.25
##
## RMSEA index = 0.166 and the 10 % confidence intervals are 0.164 0.17
## BIC = 289
##
## Measures of factor score adequacy
## g F1* F2* F3*
## Correlation of scores with factors 0.83 0.91 0.92 0.78
## Multiple R square of scores with factors 0.70 0.82 0.85 0.61
## Minimum correlation of factor score estimates 0.39 0.64 0.70 0.22
##
## Total, General and Subset omega for each subset
## g F1* F2* F3*
## Omega total for total scores and subscales 0.99 0.98 0.96 0.84
## Omega general for total scores and subscales 0.61 0.49 0.42 0.56
## Omega group for total scores and subscales 0.28 0.49 0.53 0.27omega.diagram(efa_bifactor)
save_plot_to_file(omega.diagram(efa_bifactor), "figs/omega_fit.png")
#vary the group factor number
efa_bifactor_models = map(3:10, ~omega(d_spi_all_imp_z[-1], nfactors = .))## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in GPFoblq(A, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : convergence not obtained in GPFoblq. 1000 iterations used.
## Warning in GPFoblq(A, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : convergence not obtained in GPFoblq. 1000 iterations used.
## Warning in GPFoblq(A, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : The estimated weights for the factor scores are probably incorrect.
## Try a different factor score estimation method.
## Warning in fa.stats(r = r, f = f, phi = phi, n.obs = n.obs, np.obs = np.obs, :
## The estimated weights for the factor scores are probably incorrect. Try a
## different factor score estimation method.
## Warning in GPFoblq(A, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : convergence not obtained in GPFoblq. 1000 iterations used.
## Warning in GPFoblq(A, Tmat = Tmat, normalize = normalize, eps = eps, maxit =
## maxit, : The estimated weights for the factor scores are probably incorrect.
## Try a different factor score estimation method.
#save all the scores
efa_bifactor_scores = d_spi_all_imp_z[, 1]
for (i in seq_along(efa_bifactor_models)) efa_bifactor_scores[[str_glue("SPI_omega_{i+2}")]] = efa_bifactor_models[[i]]$scores[, 1] %>% standardize()
#join them to main
for (v in str_subset(names(d), "SPI_omega_")) d[[v]] = NULL
d = full_join(
d,
efa_bifactor_scores
)## Joining with `by = join_by(ISO)`#do they correlate?
d %>%
select(contains("SPI_omega_")) %>%
GG_heatmap()
GG_save("figs/efa_bifactor_scores.png")
#check for GDP bias
d %>%
GG_scatter("GDPpc_PPP_2019_log10", "SPI_omega_5", case_names = "nice_name") +
scale_x_continuous("GDP per capita, PPP, 2019, log10") +
scale_y_continuous("General factor of SPI (64 indicators, 2019, EFA bifactor, 5 group factors)") +
ggtitle("GDP per capita log10 vs. very fancy bifactor model of SPI") +
geom_smooth()## `geom_smooth()` using formula = 'y ~ x'## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'
GG_save("figs/GDPpclog10_SPI_EFA_bifactor5.png")## `geom_smooth()` using formula = 'y ~ x'
## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'#ranking
d %>% select(
nice_name, GDPpc_PPP_2019_log10, medianIncome_log10, SPI_2019, SPI_EFA, SPI2_EFA, SPI3_EFA, SPI_omega_3, SPI_omega_5, SPI_omega_10
) %>%
arrange(-SPI_omega_10) %>%
map_df(function(v) {
if (!is.numeric(v)) return(v)
rank(v*-1)
}) %>%
head(25) %>%
print(n=25)## # A tibble: 25 × 10
## nice_name GDPpc_PPP_2019_log10 medianIncome_log10 SPI_2019 SPI_EFA SPI2_EFA
## <chr> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 Norway 10 3 1 2 3
## 2 Switzerland 8 4 4 7 5
## 3 Sweden 20 8 7 5 7
## 4 Canada 24 6 10 6 8
## 5 Iceland 16 12 5 10 11
## 6 France 28 14 18 9 10
## 7 Australia 23 11 9 13 15
## 8 Luxembourg 2 1 11 3 1
## 9 Netherlands 17 10 6 1 2
## 10 Germany 19 13 8 4 4
## 11 Italy 33 22 26 22 20
## 12 Denmark 15 9 2 11 12
## 13 Ireland 5 19 12 14 6
## 14 Finland 25 15 3 8 9
## 15 Belgium 21 16 16 16 14
## 16 New Zealand 31 161 15 17 18
## 17 Austria 18 7 14 19 17
## 18 Singapore 3 162 27 27 25
## 19 United Kin… 26 17 17 15 16
## 20 Malta 30 18 28 28 28
## 21 Spain 37 26 20 23 22
## 22 Japan 34 20 13 12 13
## 23 USA 11 5 23 26 26
## 24 Slovenia 39 25 21 18 19
## 25 Cyprus 35 23 30 25 27
## # ℹ 4 more variables: SPI3_EFA <dbl>, SPI_omega_3 <dbl>, SPI_omega_5 <dbl>,
## # SPI_omega_10 <dbl>Compare with NIQ
#what has the best correlation with national IQs?
nat_iq_model = str_glue("
#big G factor
G =~ QNWplusSASplusGEO + L_and_V12plusGEO + R
GDPpc_PPP_2019_log10 ~ G
GNIpc_PPP_2019_log10 ~ G
meanIncome_log10 ~ G
medianIncome_log10 ~ G
SPI_2019 ~ G
SPI_EFA ~ G
SPI2_EFA ~ G
SPI3_EFA ~ G
SPI_omega_3 ~ G
SPI_omega_4 ~ G
SPI_omega_5 ~ G
SPI_omega_6 ~ G
SPI_omega_7 ~ G
SPI_omega_8 ~ G
SPI_omega_9 ~ G
SPI_omega_10 ~ G
")
nat_iq_fit = cfa(nat_iq_model, data = d, std.ov = T, std.lv = T, estimator = "DWLS")## Warning in lav_samplestats_from_data(lavdata = lavdata, lavoptions = lavoptions, : lavaan WARNING: number of observations (120) too small to compute Gammanat_iq_fit## lavaan 0.6.15 ended normally after 63 iterations
##
## Estimator DWLS
## Optimization method NLMINB
## Number of model parameters 158
##
## Used Total
## Number of observations 120 215
##
## Model Test User Model:
##
## Test statistic 0.169
## Degrees of freedom 32
## P-value (Chi-square) 1.000nat_iq_validities = parameterestimates(nat_iq_fit, standardized = T) %>%
filter(
op == "~",
rhs == "G"
) %>% arrange(std.all)
nat_iq_validities#regional predictive validity
regional_models = map_df(nat_iq_validities$lhs, function(v) {
dd = d %>% mutate(
yyy = d[[v]]
)
fit_continent = lm("yyy ~ UN_continent", data = dd)
fit_macroregion = lm("yyy ~ UN_macroregion", data = dd)
fit_region = lm("yyy ~ UN_region", data = dd)
tibble(
metric = v,
r_adj_continent = summary(fit_continent)$adj.r.squared %>% sqrt(),
r_adj_macroregion = summary(fit_macroregion)$adj.r.squared %>% sqrt(),
r_adj_region = summary(fit_region)$adj.r.squared %>% sqrt()
)
})
#compare
final_validation = full_join(
nat_iq_validities %>% select(lhs, std.all) %>% rename(metric = lhs, r_nat_G = std.all),
regional_models,
) %>% mutate(
final_metric = (standardize(r_nat_G) + standardize(r_adj_region)) %>% standardize()
) %>% arrange(-final_metric)## Joining with `by = join_by(metric)`final_validationfinal_validation[-1] %>% wtd.cors()## r_nat_G r_adj_continent r_adj_macroregion r_adj_region
## r_nat_G 1.000 0.786 0.511 0.625
## r_adj_continent 0.786 1.000 0.838 0.865
## r_adj_macroregion 0.511 0.838 1.000 0.968
## r_adj_region 0.625 0.865 0.968 1.000
## final_metric 0.901 0.916 0.820 0.901
## final_metric
## r_nat_G 0.901
## r_adj_continent 0.916
## r_adj_macroregion 0.820
## r_adj_region 0.901
## final_metric 1.000Meta
#versions
write_sessioninfo()## R version 4.3.0 (2023-04-21)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Linux Mint 21.1
##
## Matrix products: default
## BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_DK.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_DK.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_DK.UTF-8 LC_IDENTIFICATION=C
##
## time zone: Europe/Berlin
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] lavaan_0.6-15 readxl_1.4.2 kirkegaard_2023-06-27
## [4] psych_2.3.3 assertthat_0.2.1 weights_1.0.4
## [7] Hmisc_5.0-1 magrittr_2.0.3 lubridate_1.9.2
## [10] forcats_1.0.0 stringr_1.5.0 dplyr_1.1.2
## [13] purrr_1.0.1 readr_2.1.4 tidyr_1.3.0
## [16] tibble_3.2.1 ggplot2_3.4.2 tidyverse_2.0.0
##
## loaded via a namespace (and not attached):
## [1] mnormt_2.1.1 gridExtra_2.3 rlang_1.1.0
## [4] e1071_1.7-13 compiler_4.3.0 mgcv_1.8-42
## [7] gdata_2.18.0.1 systemfonts_1.0.4 vctrs_0.6.2
## [10] quadprog_1.5-8 pkgconfig_2.0.3 crayon_1.5.2
## [13] fastmap_1.1.1 backports_1.4.1 labeling_0.4.2
## [16] pbivnorm_0.6.0 utf8_1.2.3 rmarkdown_2.21
## [19] tzdb_0.3.0 nloptr_2.0.3 ragg_1.2.5
## [22] bit_4.0.5 xfun_0.39 cachem_1.0.7
## [25] jsonlite_1.8.4 highr_0.10 broom_1.0.4
## [28] parallel_4.3.0 cluster_2.1.4 R6_2.5.1
## [31] bslib_0.4.2 stringi_1.7.12 vcd_1.4-11
## [34] ranger_0.15.1 car_3.1-2 boot_1.3-28
## [37] rpart_4.1.19 lmtest_0.9-40 jquerylib_0.1.4
## [40] cellranger_1.1.0 Rcpp_1.0.10 knitr_1.42
## [43] zoo_1.8-12 base64enc_0.1-3 Matrix_1.5-1
## [46] splines_4.3.0 nnet_7.3-18 timechange_0.2.0
## [49] tidyselect_1.2.0 rstudioapi_0.14 abind_1.4-5
## [52] yaml_2.3.7 plyr_1.8.8 lattice_0.20-45
## [55] withr_2.5.0 evaluate_0.20 foreign_0.8-82
## [58] proxy_0.4-27 pillar_1.9.0 carData_3.0-5
## [61] mice_3.15.0 checkmate_2.2.0 VIM_6.2.2
## [64] stats4_4.3.0 generics_0.1.3 vroom_1.6.1
## [67] sp_1.6-0 hms_1.1.3 munsell_0.5.0
## [70] scales_1.2.1 laeken_0.5.2 minqa_1.2.5
## [73] gtools_3.9.4 class_7.3-21 glue_1.6.2
## [76] tools_4.3.0 robustbase_0.95-1 data.table_1.14.8
## [79] lme4_1.1-33 grid_4.3.0 colorspace_2.1-0
## [82] nlme_3.1-162 htmlTable_2.4.1 Formula_1.2-5
## [85] cli_3.6.1 textshaping_0.3.6 fansi_1.0.4
## [88] rematch_1.0.1 gtable_0.3.3 DEoptimR_1.0-12
## [91] sass_0.4.5 digest_0.6.31 GPArotation_2023.3-1
## [94] ggrepel_0.9.3 farver_2.1.1 htmlwidgets_1.6.2
## [97] htmltools_0.5.5 lifecycle_1.0.3 bit64_4.0.5
## [100] MASS_7.3-58.3#write data to file for reuse
d %>% write_rds("data/data_for_reuse.rds")