Linear Models

INLA: Bayes goes to Norway

August 15, 2012 | 0 Comments

INLA is not the Norwegian answer to ABBA; that would probably be a-ha. INLA is the answer to ‘Why do I have enough time to cook a three-course meal while running MCMC analyses?”. Integrated Nested Laplace Approximations (INLA) is based … Continue reading → [Read more...]

Split-plot 1: How does a linear mixed model look like?

June 24, 2012 | 0 Comments

I like statistics and I struggle with statistics. Often times I get frustrated when I don’t understand and I really struggled to make sense of Krushke’s Bayesian analysis of a split-plot, particularly because ‘it didn’t look like’ a split-plot to … Continue reading → [Read more...]

On the (statistical) road, workshops and R

December 3, 2011 | 0 Comments

Things have been a bit quiet at Quantum Forest during the last ten days. Last Monday (Sunday for most readers) I flew to Australia to attend a couple of one-day workshops; one on spatial analysis (in Sydney) and another one … Continue reading → [Read more...]

Surviving a binomial mixed model

November 11, 2011 | 0 Comments

A few years ago we had this really cool idea: we had to establish a trial to understand wood quality in context. Sort of following the saying “we don’t know who discovered water, but we are sure that it wasn’t … Continue reading → [Read more...]

Coming out of the (Bayesian) closet: multivariate version

November 7, 2011 | 0 Comments

This week I’m facing my—and many other lecturers’—least favorite part of teaching: grading exams. In a supreme act of procrastination I will continue the previous post, and the antepenultimate one, showing the code for a bivariate analysis of a randomized … Continue reading → [Read more...]

Multivariate linear mixed models: livin’ la vida loca

October 31, 2011 | 0 Comments

I swear there was a point in writing an introduction to covariance structures: now we can start joining all sort of analyses using very similar notation. In a previous post I described simple (even simplistic) models for a single response … Continue reading → [Read more...]

Spatial correlation in designed experiments

October 20, 2011 | 0 Comments

Last Wednesday I had a meeting with the folks of the New Zealand Drylands Forest Initiative in Blenheim. In addition to sitting in a conference room and having nice sandwiches we went to visit one of our progeny trials at … Continue reading → [Read more...]

Large applications of linear mixed models

October 18, 2011 | 0 Comments

In a previous post I summarily described our options for (generalized to varying degrees) linear mixed models from a frequentist point of view: nlme, lme4 and ASReml-R†, followed by a quick example for a split-plot experiment. But who is really … Continue reading → [Read more...]

Linear mixed models in R

October 16, 2011 | 0 Comments

A substantial part of my job has little to do with statistics; nevertheless, a large proportion of the statistical side of things relates to applications of linear mixed models. The bulk of my use of mixed models relates to the … Continue reading → [Read more...]

Maximum likelihood

October 13, 2011 | 0 Comments

This post is one of those ‘explain to myself how things work’ documents, which are not necessarily completely correct but are close enough to facilitate understanding. Background Let’s assume that we are working with a fairly simple linear model, where … Continue reading → [Read more...]

Simulating data following a given covariance structure

October 12, 2011 | 0 Comments

Every year there is at least a couple of occasions when I have to simulate multivariate data that follow a given covariance matrix. For example, let’s say that we want to create an example of the effect of collinearity when … Continue reading → [Read more...]

Assumptions of the Linear Model

October 6, 2011 | 0 Comments

Linear Assumptions from the Analysis Factor – Assumptions of linear regression (and ANOVA) are about the residuals, not the normality or independence of the response variable (Y). If you don’t know what this means be sure to read this brief … Continue reading →
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Linear regression with correlated data

October 5, 2011 | 0 Comments

I started following the debate on differential minimum wage for youth (15-19 year old) and adults in New Zealand. Eric Crampton has written a nice series of blog posts, making the data from Statistics New Zealand available. I will use … Continue reading → [Read more...]

Model Validation: Interpreting Residual Plots

July 18, 2011 | 0 Comments

When conducting any statistical analysis it is important to evaluate how well the model fits the data and that the data meet the assumptions of the model. There are numerous ways to do this and a variety of statistical tests to evaluate deviations from model assumptions. However, there is little ...
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Variable selection using automatic methods

May 22, 2010 | 0 Comments

When we have a set of data with a small number of variables we can easily use a manual approach to identifying a good set of variables and the form they take in our statistical model. In other situations we may have a large number of potentially important variables and ... [Read more...]

Linear regression models with robust parameter estimation

May 15, 2010 | 0 Comments

There are situations in regression modelling where robust methods could be considered to handle unusual observations that do not follow the general trend of the data set. There are various packages in R that provide robust statistical methods which are summarised on the CRAN Robust Task View. As an example ... [Read more...]

R Tips in Stat 511

March 22, 2010 | 0 Comments

Here are some (trivial) R tips in the course Stat 511. I’ll update this post till the semester is over. Formatting R Code Reading code is pain, but the well-formatted code might alleviate the pain a little bit. The function tidy.source() in the animation package can help us format ... [Read more...]

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