Usage
tutorial.RmdTutorial package calidad
The package aims to implement in a simple way the methodologies of INE Chile and ECLAC for the quality assessment of estimates from household surveys.
This tutorial shows the basic use of the package and includes the main functions to create the necessary inputs to implement both quality standards.
Data edition
We will use two datasets:
- Encuesta Nacional de Empleo (efm 2020)
- VIII Encuesta de Presupuestos Familiares
Both datasets are loaded into the package and they can be used when the package is loaded in the session 1. The data edition in the case of ENE has the purpuse of creating some subpopulations (work force, unemployed and unemployed).
library(survey)
library(calidad)
library(dplyr)
ene <- ene %>%
mutate(fdt = if_else(cae_especifico >= 1 & cae_especifico <= 9, 1, 0), # labour force
ocupado = if_else(cae_especifico >= 1 & cae_especifico <= 7, 1, 0), # employed
desocupado = if_else(cae_especifico >= 8 & cae_especifico <= 9, 1, 0)) # unemployed
# One row per household
epf <- epf_personas %>%
group_by(folio) %>%
slice(1) %>%
ungroup()Sample design
Before starting to use the package, it is necessary to declare the
sample design of the survey, for which we use the survey
package. The primary sample unit, the stratum and weights must be
declared. It is also possible to use a design with only weights,
nevertheless in that case the variance will be estimated under simple
random sampling assumption. In this case we will declare a complex
design for the two surveys (EPF and ENE). Additionally, it may be useful
to declare an option for strata that only have one PSU.
# Store original options
old_options <- options()Inputs creation
National Labour Survey (part 1)
To assess the quality of an estimate, the INE methodology establishes differentiated criteria for estimates of proportion (or ratio), on the one hand, and estimates of mean, size and total, on the other. In the case of proportion estimation, it is necessary to have the sample size, the degrees of freedom and the standard error. The other estimates require the sample size, the degrees of freedom, and the coefficient of variation.
The package includes separate functions to create the inputs for estimates of mean, proportion, totals and size. The following example shows how the proportion and size functions are used.
insumos_prop <- create_prop(var = "desocupado", domains = "sexo", subpop = "fdt", design = dc_ene) # proportion of unemployed people
insumos_total <- create_size(var = "desocupado", domains = "sexo", subpop = "fdt", design = dc_ene) # number of unemployed people-
var: variable to be estimated. Must be a dummy variable -
domains: required domains. -
subpop: reference subpopulation. It is optional and works as a filter (must be a dummy variable) -
design: sample design
The function returns all the neccesary inputs to implement the standard
To get more domains, we can use the “+” symbol as follows:
desagregar <- create_prop(var = "desocupado", domains = "sexo+region", subpop = "fdt", design = dc_ene)A useful parameter is eclac_input. It allows to return
the ECLAC inputs. By default this parameter is FALSE and with the option
TRUE we can activate it.
eclac_inputs <- create_prop(var = "desocupado", domains = "sexo+region", subpop = "fdt", design = dc_ene, eclac_input = TRUE)Household Budget Survey (part 2)
In some cases it may be of interest to assess the quality of a sum.
For example, the sum of all the income of the EPF at the geographical
area level (Gran Santiago and other regional capitals). For this, there
is the create_total function. This function receives a
continuous variable such as hours, expense, or income and generates
totals at the requested level. The ending “with” of the function alludes
to the fact that a continuous variable is being used.
insumos_suma <- create_total(var = "gastot_hd", domains = "zona", design = dc_epf)If we want to assess the estimate of a mean, we have the function
create_mean. In this case, we will calculate the average
expenditure of households, according to geographical area.
insumos_media <- create_mean(var = "gastot_hd", domains = "zona", design = dc_epf)The default usage is not to disaggregate, in which case the functions should be used as follows:
# ENE dataset
insumos_prop_nacional <- create_prop("desocupado", subpop = "fdt", design = dc_ene)
insumos_total_nacional <- create_total("desocupado", subpop = "fdt", design = dc_ene)
# EPF dataset
insumos_suma_nacional <- create_total("gastot_hd", design = dc_epf)
insumos_media_nacional <- create_mean("gastot_hd", design = dc_epf)Assessment
Once the inputs have been generated, we can do the assessment To do
this, we use the assess function.
evaluacion_prop <- assess(insumos_prop)
evaluacion_tot <- assess(insumos_total)
evaluacion_suma <- assess(insumos_suma)
evaluacion_media <- assess(insumos_media)The output is a dataframe that, in addition to
containing the information already generated, includes a column that
indicates whether the estimate is unreliable, less reliable or
reliable.
The function assess has a parameter that allows us to
know if the table should be published or not. Following the criteria of
the standard, if more than 50% of the estimates of a table are not
reliable, it should not be published.
# Unemployment by region
desagregar <- create_size(var = "desocupado", domains = "region", subpop = "fdt", design = dc_ene)
# assess output
evaluacion_tot_desagreg <- assess(desagregar, publish = T)
evaluacion_tot_desagreg
#> region stat se df n cv eval_n
#> 1 1 13830.218 2105.8402 40 79 0.15226370 sufficient sample size
#> 2 2 33899.555 3457.3783 72 153 0.10198890 sufficient sample size
#> 3 3 13181.473 1288.0572 67 125 0.09771725 sufficient sample size
#> 4 4 38572.642 3502.4280 97 195 0.09080083 sufficient sample size
#> 5 5 87670.219 5253.3214 229 473 0.05992139 sufficient sample size
#> 6 6 41307.341 4015.1321 94 193 0.09720142 sufficient sample size
#> 7 7 35142.800 3171.1718 85 189 0.09023674 sufficient sample size
#> 8 8 65956.207 4167.7798 204 401 0.06319011 sufficient sample size
#> 9 9 34223.136 3409.5040 81 149 0.09962570 sufficient sample size
#> 10 10 20843.453 2212.3500 68 127 0.10614124 sufficient sample size
#> 11 11 3128.436 496.9203 37 59 0.15883985 insufficient sample size
#> 12 12 4560.165 806.3695 28 44 0.17682901 insufficient sample size
#> 13 13 369743.430 19615.0415 318 681 0.05305041 sufficient sample size
#> 14 14 12991.098 1533.0491 61 112 0.11800766 sufficient sample size
#> 15 15 7869.593 1081.7453 52 85 0.13745886 sufficient sample size
#> 16 16 18885.030 2039.7704 66 128 0.10800991 sufficient sample size
#> eval_df eval_cv label publication
#> 1 sufficient df cv between 0.15 and 0.3 weakly reliable publish
#> 2 sufficient df cv <= 0.15 reliable publish
#> 3 sufficient df cv <= 0.15 reliable publish
#> 4 sufficient df cv <= 0.15 reliable publish
#> 5 sufficient df cv <= 0.15 reliable publish
#> 6 sufficient df cv <= 0.15 reliable publish
#> 7 sufficient df cv <= 0.15 reliable publish
#> 8 sufficient df cv <= 0.15 reliable publish
#> 9 sufficient df cv <= 0.15 reliable publish
#> 10 sufficient df cv <= 0.15 reliable publish
#> 11 sufficient df cv between 0.15 and 0.3 non-reliable publish
#> 12 sufficient df cv between 0.15 and 0.3 non-reliable publish
#> 13 sufficient df cv <= 0.15 reliable publish
#> 14 sufficient df cv <= 0.15 reliable publish
#> 15 sufficient df cv <= 0.15 reliable publish
#> 16 sufficient df cv <= 0.15 reliable publish
#> pass
#> 1 81.25% reliable estimates
#> 2 81.25% reliable estimates
#> 3 81.25% reliable estimates
#> 4 81.25% reliable estimates
#> 5 81.25% reliable estimates
#> 6 81.25% reliable estimates
#> 7 81.25% reliable estimates
#> 8 81.25% reliable estimates
#> 9 81.25% reliable estimates
#> 10 81.25% reliable estimates
#> 11 81.25% reliable estimates
#> 12 81.25% reliable estimates
#> 13 81.25% reliable estimates
#> 14 81.25% reliable estimates
#> 15 81.25% reliable estimates
#> 16 81.25% reliable estimates
# Reset original options
options(old_options)