Posts Tagged ‘ statistics ’

Interactive visualization of non-linear logistic regression decision boundaries with Shiny

July 24, 2014
By
Interactive visualization of non-linear logistic regression decision boundaries with Shiny

(skip to the shiny app) Model building is very often an iterative process that involves multiple steps of choosing an algorithm and hyperparameters, evaluating that model / cross validation, and optimizing the hyperparameters. I find a great aid in this process, for classification tasks, is not only to keep track of the accuracy across models, »more

Read more »

Coherent population forecasting using R

July 24, 2014
By
Coherent population forecasting using R

This is an example of how to use the demography package in R for stochastic population forecasting with coherent components. It is based on the papers by Hyndman and Booth (IJF 2008) and Hyndman, Booth and Yasmeen (Demography 2013). I will use Australian data from 1950 to 2009 and forecast the next 50 years. In demography, “coherent” forecasts are where male and females (or other sub-groups) do not diverge over…

Read more »

Continued:”P-values overstate the evidence against the null”: legit or fallacious?

July 23, 2014
By
Continued:”P-values overstate the evidence against the null”: legit or fallacious?

Since the comments to my previous post are getting too long, I’m reblogging it here to make more room. I say that the issue raised by J. Berger and Sellke (1987) and Casella and R. Berger (1987) concerns evaluating the evidence in relation to a given hypothesis (using error probabilities). Given the information that this hypothesis H* was randomly […]

Read more »

Plotting the characteristic roots for ARIMA models

July 23, 2014
By
Plotting the characteristic roots for ARIMA models

When modelling data with ARIMA models, it is sometimes useful to plot the inverse characteristic roots. The following functions will compute and plot the inverse roots for any fitted ARIMA model (including seasonal models). # Compute AR roots arroots <- function(object) { if(class(object) != "Arima" & class(object) != "ar") stop("object must be of class Arima or ar") if(class(object) == "Arima") parvec <- object$model$phi else parvec <- object$ar if(length(parvec) > 0)…

Read more »

Dr Nic goes to ICOTS9

July 23, 2014
By
Dr Nic goes to ICOTS9

I had a great time at ICOTS9. Academic conferences are a bit of a lottery, but ICOTS is two for two for me. Both ICOTS8 and ICOTS9 were winners – enjoyable, interesting and inspiring.  I’ve just returned from ICOTS9 in … Continue reading →

Read more »

I am not an econometrician

July 21, 2014
By
I am not an econometrician

I am a statistician, but I have worked in a department of predominantly econometricians for the past 17 years. It is a little like an Australian visiting the United States. Initially, it seems that we talk the same language, do the same sorts of things, and have a very similar culture. But the longer you stay there, the more you realise there are differences that run deep and affect the…

Read more »

Compile R and OpenBLAS from Source Guide

Compile R and OpenBLAS from Source Guide

1. Get OpenBLAS 2.1 Get R 2.2 Specific Instructions for DSS Users 3. Validation 4. Benchmark This guide is intended to aid any R and Linux user who desires a threaded version of BLAS. In particular I hope this will allow other grad students, who like me do not have many user privileges on their […] The post Compile R and OpenBLAS from Source Guide appeared first on Lindons Log.

Read more »

Video Tutorial: Allelic Frequencies Remain Constant From Generation to Generation Under the Hardy-Weinberg Equilibrium

Video Tutorial: Allelic Frequencies Remain Constant From Generation to Generation Under the Hardy-Weinberg Equilibrium

The Hardy-Weinberg law is a fundamental principle in statistical genetics.  If its 7 assumptions are fulfilled, then it predicts that the allelic frequency of a genetic trait will remain constant from generation to generation.  In this new video tutorial in my Youtube channel, I explain the math behind the Hardy-Weinberg theorem.  In particular, I clarify […]

Read more »

Variations on rolling forecasts

July 15, 2014
By
Variations on rolling forecasts

Rolling forecasts are commonly used to compare time series models. Here are a few of the ways they can be computed using R. I will use ARIMA models as a vehicle of illustration, but the code can easily be adapted to other univariate time series models. One-step forecasts without re-estimation The simplest approach is to estimate the model on a single set of training data, and then compute one-step forecasts…

Read more »

another R new trick [new for me!]

July 15, 2014
By
another R new trick [new for me!]

While working with Andrew and a student from Dauphine on importance sampling, we wanted to assess the distribution of the resulting sample via the Kolmogorov-Smirnov measure where F is the target.  This distance (times √n) has an asymptotic distribution that does not depend on n, called the Kolmogorov distribution. After searching for a little while, […]

Read more »


Subscribe

Email:

  Subscribe