summarize_indicators: Presents point, MSE and CV estimates

Description

Function summarize_indicators reports point and mean squared error (MSE) estimates as well as calculated coefficients of variation (CV) from a fitted SAEforest object.

Usage

summarize_indicators(object, indicator = "all", MSE = FALSE, CV = FALSE)

Value

The return of summarize_indicators is an object of class summarize_indicators.SAEforest

including domain-specific point and/or MSE estimates and/or calculated CV's from a SAEforest object The returned object contains the data.frame ind and a character including the names of requested indicator(s).

Arguments

object: Object for which point and/or MSE estimates and/or calculated CV's are requested. The object must be of class SAEforest.
indicator: Optional character vector specifying indicators to be mapped: (i) all calculated indicators ("all"); (ii) each default indicators name: "Mean", "Quant10", "Quant25", "Median", "Quant75", "Quant90", "Gini", "Hcr", "Pgap", "Qsr" or the function name/s of "custom_indicator/s"; (iii) a vector of names of indicators. If the object is estimated by SAEforest_model indicator arguments are ignored and only the "Mean" is returned.
MSE: Logical. If TRUE, MSE estimates for selected indicators per domain are added to the data frame of point estimates. Defaults to FALSE.
CV: Logical. If TRUE, coefficients of variation for selected indicators per domain are added to the data frame of point estimates. Defaults to FALSE.

Details

Objects of class summarize_indicators.SAEforest have methods for following generic functions: head and tail (for default documentation, see head), as.matrix (for default documentation, see matrix), as.data.frame (for default documentation, see as.data.frame), subset (for default documentation, see subset).

Examples

Run this code

# \donttest{
# Loading data
data("eusilcA_pop")
data("eusilcA_smp")

income <- eusilcA_smp$eqIncome
X_covar <- eusilcA_smp[, -c(1, 16, 17, 18)]

# Calculating point + MSE estimates and passing arguments to the forest.
# Additionally, two additional indicators and functions as threshold are added.
# Note that B and num.trees are low to speed up estimation time and must be changed for
# practical applications.

model1 <- SAEforest_model(Y = income, X = X_covar, dName = "district",
                          smp_data = eusilcA_smp, pop_data = eusilcA_pop,
                          meanOnly = FALSE, MSE = "nonparametric", B = 5, mtry = 5,
                          num.trees = 50, smearing = FALSE)

# Extract indicator and show generics:
Gini1 <- summarize_indicators(model1, MSE = TRUE, CV = TRUE, indicator = "Gini")

head(Gini1)
tail(Gini1)
as.data.frame(Gini1)
as.matrix(Gini1)
subset(Gini1, district == "Wien")
# }