# 226 search results for "ANova"

## How Do You Write Your Model Definitions?

October 20, 2013
By

I’m often irritated by that when a statistical method is explained, such as linear regression, it is often characterized by how it can be calculated rather than by what model is assumed and fitted. A typical example of this is that linear regression is often described as a method that uses ordinary least squares to calculate the best...

## Science at the speed of ligth

October 15, 2013
By

May be is not going that fast, but at the speed of R at least. And R is pretty quick. This has pros and cons. I think that understanding the drawbacks is key to maximize the good things of speed, … Continue reading →

## “Statistical Models with R” Course – Milano, October 24-25, 2013

September 19, 2013
By

MilanoR, in collaboration with Quantide, organizes "Statistical Models with R" Course October 24-25, 2013 Course description This two-day course shows a wide variety of statistical models with R ranging from Linear Models (LM) to Generalized Linear Models (GLM) modelling, in … Continue reading →

## informative hypotheses (book review)

September 18, 2013
By

The title of this book Informative Hypotheses somehow put me off from the start: the author, Hebert Hoijtink, seems to distinguish between informative and uninformative (deformative? disinformative?) hypotheses. Namely, something like H0: μ1=μ2=μ3=μ4 is “very informative” and the alternative Ha is completely uninformative, while the “alternative null” H1: μ1<μ2=μ3<μ4 is informative. (Hence the < signs on

## Mixed models; Random Coefficients, part 2

September 14, 2013
By

Continuing from random coefficients part 1, it is time for the second part. To quote the SAS/STAT manual 'a random coefficients model with error terms that follow a nested structure'. The additional random variable is monthc, which is a factor con...

## Mixed models; Random Coefficients, part 1

September 8, 2013
By

Continuing with my exploration of mixed models I am now at the first part of random coefficients: example 59.5 for proc mixed (page 5034 of the SAS/STAT 12.3 Manual). This means I skipped examples 59.3 (plotting the likelihood) and 59.4 (known G and R)...

## Latent Variable Analysis with R: Getting Setup with lavaan

September 1, 2013
By

Getting Started with Structural Equation Modeling Part 1Getting Started with Structural Equation Modeling: Part 1 Introduction For the analyst familiar with linear regression fitting structural equation models can at first feel strange. In the R environment, fitting structural equation models involves learning new modeling syntax, new plotting...

## The joy and martyrdom of trying to be a Bayesian

August 30, 2013
By

Some of my fellow scientists have it easy. They use predefined methods like linear regression and ANOVA to test simple hypotheses; they live in the innocent world of bivariate plots and lm(). Sometimes they notice that the data have odd histograms and they use glm(). The more educated ones use … Continue reading →

## More REML exercise

August 25, 2013
By

Last week I tried exercise 1 of the SAS(R) proc mixed with R libraries lme4 and MCMCglm. So this week I aimed for exercise 2 but ended up redoing exercise 1 with nlme.Exercise 2 results gave me problems with library lme4 and latter parts of the ex...