2018 Italian general election: Details on my simulation

This article describes the simulation behind the app that you find here

This simulation of the results for the 2018 general election is based on the results from the last two national elections (the Italian parliament election in 2013 and the European Parliament election 2014) and national polls conducted until 16 February 2018. The simulation is based on one assumption, which is reasonable but not necessarily realistic: the relative territorial strength of parties is stable. From this assumption derives that if the national support for a party (as measured by national voting intention polls) varies, it varies consistently and proportionally everywhere. A rising tide lifts all boats and vice versa. The assumption has some empirical justification. If we compare the difference from the national support (in percentage) for each district in 2013 and 2014 we see a significant correlation, especially in the major parties.

Votes to party in the 2018 Chamber districts

(more…)

Tuesday, 27 February 2018

tweets


Twitter: frbailo

links


blogroll


RSS r-bloggers.com

  • Writing Functions to Automate Repetitive Plotting Tasks in ggplot2
    Introduction Making Multiple Plots on the Same Subject Preparing the Data Writing Functions to Generate Multiple Plots Making Custom Plot Themes Updating Plot Themes Introduction There are often situations when you need to perform repetitive plotting tasks. For example, you’d like to plot the same kind of data (e....
  • Expand broom::tidy() output for categorical parameter estimates
    Introduction The tidycat package includes the tidy_categorical() function to expand broom::tidy() outputs for categorical parameter estimates. Documentation For full documentation, see the package vignette: The tidycat package: expand broom::tidy() output for categorical parameter estimates Hello World The tidy() function in the broom package takes the messy output ...
  • RcppSimdJson 0.1.0: Now on Windows, With Parsers and Faster Still!
    A smashing new RcppSimdJson release 0.1.0 containing several small updates to upstream simdjson (now at 0.4.6) in part triggered by very excisting work by Brendan who added actual parser from file and string—and together with Daniel upstream worked...
  • Drunk-under-the-lamppost testing
    I’m writing a response here to Abraham Mathews’s post, Best practices for code review, R edition, because my comment there didn’t show up and I think the topic’s important. Mathews’s post starts out on the right track, then veers away from best practices in the ...
  • xspliner: An R Package to Build Explainable Surrogate ML Models
    This talk was presented virtually at eRum 2020 by Appsilon engineer Krystian Igras. Here is a direct link to the video. Why Should We Explain Black Box ML Models? A vast majority of state-of-the-art ML algorithms are black boxes, meaning it is difficult to understand their inner workings. The more that ...

RSS Simply Statistics

  • Asymptotics of Reproducibility
    Every once in a while, I see a tweet or post that asks whether one should use tool X or software Y in order to “make their data analysis reproducible”. I think this is a reasonable question because, in part, there are so many good tools out there! This is undeniably a good thing and […]
  • Amplifying people I trust on COVID-19
    Like a lot of people, I’ve been glued to various media channels trying to learn about the latest with what is going on with COVID-19. I have also been frustrated - like a lot of people - with misinformation and the deluge of preprints and peer reviewed material. Some of this information is critically important […]
  • Is Artificial Intelligence Revolutionizing Environmental Health?
    NOTE: This post was written by Kevin Elliott, Michigan State University; Nicole Kleinstreuer, National Institutes of Health; Patrick McMullen, ScitoVation; Gary Miller, Columbia University; Bhramar Mukherjee, University of Michigan; Roger D. Peng, Johns Hopkins University; Melissa Perry, The George Washington University; Reza Rasoulpour, Corteva Agriscience, and Elizabeth Boyle, National Academies of Sciences, Engineering, and Medicine. […]

RSS Statistical Modeling, Causal Inference, and Social Science

  • Drunk-under-the-lamppost testing
    I’m writing a response here to Abraham Mathews’s post, Best practices for code review, R edition, because my comment there didn’t show up and I think the topic’s important. Mathews’s post starts out on the right track, then veers away from best practices in the section “What code should be reviewed?” where he says, …In […]
  • “Time Travel in the Brain”
    Natalie Biderman and Daphna Shohamy wrote this science article for kids. Here’s the abstract: Do you believe in time travel? Every time we remember something from the past or imagine something that will happen in the future, we engage in mental time travel. Scientists discovered that, whether we mentally travel back into the past or […]
  • Statistical controversy on estimating racial bias in the criminal justice system
    1. Background A bunch of people have asked me to comment on these two research articles: Administrative Records Mask Racially Biased Policing, by Dean Knox, Will Lowe, and Jonathan Mummolo: Researchers often lack the necessary data to credibly estimate racial discrimination in policing. In particular, police administrative records lack information on civilians police observe but […]