CvmortalityMult (version 1.1.1)

SpainRegions: Spain Regions Mortality data

Description

Data from the Spanish region of Spain from the Spanish National Institute of Statistics (INE) for both genders years 1991-2020 and abridged ages from 0 to 90. This dataset contains mortality rates (qxt) from 18 different regions of Spain. Additionally, the dataset includes the number of people alive (lxt) for each age and period.

Usage

SpainRegions

Format

A data frame with 10800 rows and 9 columns with class "CVmortalityData" including the following information

• ccaa a vector containing all the regions of Spain. Indeed, the column takes the following information: Spain, Andalucia, Aragon, Asturias, Baleares, Canarias, Cantabria, Castillayla Mancha, CastillayLeon, Cataluna, ComunidadValenciana, Extremadura, Galicia, Madrid, Murcia, Navarra, PaisVasco, and LaRioja.

• periods a vector containing the periods of the dataset from 1991 to 2020.

• ages a vector containing the abridged ages considered in the dataset, 0, <1, 1-4, 5-9, 10-14, 15-19, 20-24, 25-29, 30-34, 35-39, 40-44, 45-49, 50-54, 55-59, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, and 90-94.

• qx_male mortality rates for the males in every region of Spain including Nation data.

• qx_female mortality rates for the females in every region of Spain including Nation data.

• lx_male survivor function considered for the males in every region of Spain including Nation data.

• lx_female survivor function considered for the females in every region of Spain including Nation data.

• series information for the series of data provided.

• label the assigned tag to the data frame.

References

Spanish National Institute of Statistics (INE) (2023). Tablas de mortalidad, metodologia. Technical report, Instituto Nacional de Estadistica

Examples

#The example takes more than 5 seconds because it includes
#several fitting and forecasting process and hence all
#the process is included in donttest
# \donttest{
#In this case, we show the region dataset applying it to a multipopulation model.
#First, we present the dataset
SpainRegions

#An example to how the additive multi-population model fits the data
ages <- c(0, 1, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90)

library(gnm)
multiplicative_Spainmales <- fitLCmulti(model = "multiplicative",
                                        qxt = SpainRegions$qx_male,
                                        periods = c(1991:2020),
                                        ages = c(ages),
                                        nPop = 18,
                                        lxt = SpainRegions$lx_male)

multiplicative_Spainmales
# }