Learn about XAI in R with ,,Predictive Models: Explore, Explain, and Debug” — R-bloggers

(This article was first published on English – SmarterPoland.pl, and kindly contributed to R-bloggers) XAI (eXplainable artificial intelligence) is a fast growing and super interesting area. Working with complex models generates lots of problems with model validation (on test data performance is great but drops at production), model bias, lack of stability and many others.…

via Learn about XAI in R with ,,Predictive Models: Explore, Explain, and Debug” — R-bloggers

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Three Strategies for Working with Big Data in R — R-bloggers

(This article was first published on R Views, and kindly contributed to R-bloggers) For many R users, it’s obvious why you’d want to use R with big data, but not so obvious how. In fact, many people (wrongly) believe that R just doesn’t work very well for big data. In this article, I’ll share three…

via Three Strategies for Working with Big Data in R — R-bloggers

Forecasting tools in development — R-bloggers

(This article was first published on – R, and kindly contributed to R-bloggers) As I’ve been writing up a progress report for my NIGMS R35 MIRA award, I’ve been reminded at how much of the work that we’ve been doing is focused on forecasting infrastructure. A common theme in the Reich Lab is making operational…

via Forecasting tools in development — R-bloggers

RStudio 1.2 Released — R-bloggers

(This article was first published on RStudio Blog, and kindly contributed to R-bloggers) We’re excited to announce the official release of RStudio 1.2! What’s new in RStudio 1.2? Over a year in the making, this new release of RStudio includes dozens of new productivity enhancements and capabilities. You’ll now find RStudio a more comfortable workbench…

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Bayesian models in R — R-bloggers

(This article was first published on poissonisfish, and kindly contributed to R-bloggers) Greater Ani (Crotophaga major) is a cuckoo species whose females occasionally lay eggs in conspecific nests, a form of parasitism recently explored If there was something that always frustrated me was not fully understanding Bayesian inference. Sometime last year, I came across an…

via Bayesian models in R — R-bloggers

Econometría Espacial: Proyecciones de población

 

Econometría Espacial: Proyecciones de población

Francisco Parra

27 de marzo de 2019

Planteamiento del problema

La población municipal obtenida de los padrones municipales (población observada), se va a estimar en cada municipio con la población municipal que hubiera resultado de su dinámica natural (Población esperada):

Esta población resultado de la dinámica natural de la población de cada municipio es la que se va a proyectar mediante las tasas demográficas (tasa de natalidad por edades y tasas de mortalidad por edades), se trataría por tanto de una población esperada que como resultado de los movimientos residenciales va a diferir en cada municipio de la población real o empadronada. Las diferencias entre ambas poblaciones en los años 2005-2016, tendrán lógicamente una correlación espacial, derivada de los movimientos de población entre municipios limítrofes, asociados a los procesos de urbanización y descongestión de los centros urbanos. Estas correlaciones espaciales, estudiadas con metodología de econometría espacial, servirán para pronosticar los saldos migratorios asociados a las proyecciones que se hagan de la población natural en cada municipio.

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Econometria espacial: proyecciones de población