################################################################################
# Test examples from package since most are dontrun!
################################################################################
# Ebp function
emdi_model <- ebp(fixed = eqIncome ~ gender + eqsize + cash + self_empl +
unempl_ben + age_ben + surv_ben + sick_ben + dis_ben + rent + fam_allow +
house_allow + cap_inv + tax_adj, pop_data = eusilcA_pop,
pop_domains = "district", smp_data = eusilcA_smp, smp_domains = "district",
na.rm = TRUE)
# funktioniert
emdi_model <- ebp(fixed = eqIncome ~ gender + eqsize + cash +
self_empl + unempl_ben + age_ben + surv_ben + sick_ben + dis_ben + rent +
fam_allow + house_allow + cap_inv + tax_adj, pop_data = eusilcA_pop,
pop_domains = "district", smp_data = eusilcA_smp, smp_domains = "district",
threshold = function(y){0.6 * median(y)}, transformation = "log",
L= 50, MSE = TRUE, boot_type = "wild", B = 50, custom_indicator =
list( my_max = function(y, threshold){max(y)},
my_min = function(y, threshold){min(y)}), na.rm = TRUE, cpus = 1)
# funktioniert
# Direct estimation
emdi_direct <- direct(y = "eqIncome", smp_data = eusilcA_smp,
smp_domains = "district", weights = "weight",
threshold = 11064.82, var = TRUE,
boot_type = "naive", B = 50, seed = 123, X = NULL,
totals = NULL, na.rm = TRUE)
# funktioniert
emdi_direct <- direct(y="eqIncome", smp_data=eusilcA_smp, smp_domains="district",
weights="weight",
threshold=function(y, weights) {0.6 * Hmisc::wtd.quantile(eusilcA_smp$eqIncome, weights=eusilcA_smp$weight, probs=0.5)},
var=TRUE, bootType = "naive", B=50,
seed=123, X = NULL, totals = NULL, custom_indicator = list( my_max =
function(y, weights, threshold){max(y)}, my_min =
function(y, weights, threshold){min(y)}), na.rm=TRUE)
# Warnings
# estimators
estimators(emdi_model, indicator = "Gini", MSE = TRUE, CV = TRUE)
estimators(emdi_model, indicator = "Custom")
# map_plot
# Load shape file
load_shapeaustria()
# Create mapping table such that variables that indicate domains correspond
# in population data and shape file
mapping_table <- data.frame(unique(eusilcA_pop$district),
unique(shape_austria_dis$NAME_2))
# Create map plot for mean indicator - point and MSE estimates but no CV
map_plot(object = emdi_model, MSE = TRUE, CV = FALSE,
map_obj = shape_austria_dis, indicator = c("Mean"), map_dom_id = "NAME_2",
map_tab = mapping_table)
# hat geklappt
# plot function
# Creation of default diagnostic plots
plot(emdi_model)
# Creation of diagnostic plots without labels and titles, different colors
# and without Cook's distance plot.
plot(emdi_model, label="no_title", color=c("red", "yellow"), cooks = FALSE)
# summary function
# Example with two additional indicators
emdi_model <- ebp(fixed = eqIncome ~ gender + eqsize + cash +
self_empl + unempl_ben + age_ben + surv_ben + sick_ben + dis_ben + rent +
fam_allow + house_allow + cap_inv + tax_adj, pop_data = eusilcA_pop,
pop_domains = "district", smp_data = eusilcA_smp, smp_domains = "district",
threshold = function(y){0.6 * median(y)}, L= 50, MSE = TRUE, B = 50,
custom_indicator = list( my_max = function(y, threshold){max(y)},
my_min = function(y, threshold){min(y)}), na.rm = TRUE, cpus = 1)
# Receive first overview
summary(emdi_model)
# write.excel
# Export estimates for all indicators and uncertainty measures and
# diagnostics to excel
write.excel(emdi_model, file ="excel_output_all.xlsx", indicator = "all",
MSE = TRUE, CV = TRUE)
# Single excel sheets for point, MSE and CV estimates
write.excel(emdi_model, file ="excel_output_all_split.xlsx", indicator = "all",
MSE = TRUE, CV = TRUE, split=TRUE)
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