256 search results for "anova"

Science at the speed of ligth

October 15, 2013
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Science at the speed of ligth

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 →

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

September 19, 2013
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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 →

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informative hypotheses (book review)

September 18, 2013
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informative hypotheses (book review)

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

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Mixed models; Random Coefficients, part 2

September 14, 2013
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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...

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Mixed models; Random Coefficients, part 1

September 8, 2013
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Mixed models; Random Coefficients, part 1

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)...

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Latent Variable Analysis with R: Getting Setup with lavaan

September 1, 2013
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Latent Variable Analysis with R: Getting Setup with lavaan

Getting Started with Structural Equation Modeling Part 1Getting Started with Structural Equation Modeling: Part 1IntroductionFor 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...

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The joy and martyrdom of trying to be a Bayesian

August 30, 2013
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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 →

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More REML exercise

August 25, 2013
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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...

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The Bayesian Counterpart of Pearson’s Correlation Test

August 19, 2013
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The Bayesian Counterpart of Pearson’s Correlation Test

Except for maybe the t test, a contender for the title “most used and abused statistical test” is Pearson’s correlation test. Whenever someone wants to check if two variables relate somehow it is a safe bet (at least in psychology) that the first thing to be tested is the strength of a Pearson’s correlation. Only if that doesn’t...

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Exercise in REML/Mixed model

August 18, 2013
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I want to build a bit more experience in REML, so I decided to redo some of the SAS examples in R. This post describes the results of example 59.1 (page 5001, SAS(R)/STAT User guide 12.3 link). Following the list from freshbiostats I will analyze ...

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