After last post's setting up for a simulation, it is now time to look how the models compare. To my disappointment with my simple simulations of assessors behavior the gain is minimal. Unfortunately, the simulation took much more time than I ...
184 search results for "ANOVA"
Ordinal data, models with observers
I recently made three posts regarding analysis of ordinal data. A post looking at all methods I could find in R, a post with an additional method and a post using JAGS. Common in all three was using the cheese data, a data set where...
Using multilevel models to get accurate inferences for repeated measures ANOVA designs
It is now increasingly common for experimental psychologists (among others) to use multilevel models (also known as linear mixed models) to analyze data that used to be shoe-horned into a repeated measures ANOVA design. Chapter 18 of Serious Stats introduces multilevel models by considering them as an extension of repeated measures ANOVA models that can 
R 3.0.0 is released! (what’s new, and how to upgrade)
A few hours ago Peter Dalgaard (of R Core Team) announced the release of R 3.0.0! Bellow you can read the changes in this release. One of the features worth noticing is the introduction of long vectors to R 3.0.0. As David Smith …
Veterinary Epidemiologic Research: GLM – Logistic Regression (part 2)
Second part on logistic regression (first one here). We used in the previous post a likelihood ratio test to compare a full and null model. The same can be done to compare a full and nested model to test the contribution of any subset of parameters: Interpretation of coefficients Note: Dohoo do not report the 
Ordinal Data
I expect to be getting some ordinal data, from 5 or 9 point rating scales, pretty soon, so I am having a look ahead how to treat those. Often ANOVA is used, even though it is well known not to be ideal fro a statistical point of view, so that is the st...
Veterinary Epidemiologic Research: GLM – Logistic Regression
We continue to explore the book Veterinary Epidemiologic Research and today we’ll have a look at generalized linear models (GLM), specifically the logistic regression (chapter 16). In veterinary epidemiology, often the outcome is dichotomous (yes/no), representing the presence or absence of disease or mortality. We code 1 for the presence of the outcome and 0 
R to Latex packages: Coverage
There are now quite a few R packages to turn cross-tables and fitted models into nicely formatted latex. In a previous post I showed how to use one of them to display regression tables on the fly. In this post I summarise what types of R object each of the major packages can deal with.
Veterinary Epidemiologic Research: Linear Regression Part 3 – Box-Cox and Matrix Representation
In the previous post, I forgot to show an example of Box-Cox transformation when there’s a lack of normality. The Box-Cox procedure computes values of which best “normalises” the errors. value Transformed value of Y 2 1 0.5 0 -0.5 -1 -2 For example: The plot indicates a log transformation. Matrix Representation We can use 
A slightly different introduction to R, part IV
Now, after reading in data, making plots and organising commands with scripts and Sweave, we’re ready to do some numerical data analysis. If you’re following this introduction, you’ve probably been waiting for this moment, but I really think it’s a good idea to start with graphics and scripting before statistical calculations. We’ll use the silly 
Zero Inflated Models and Generalized Linear Mixed Models with R.
Zuur, Saveliev, Ieno (2012).
