Blog Archives

Linear mixed-effect models in R

December 11, 2017
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Linear mixed-effect models in R

Statistical models generally assume that All observations are independent from each other The distribution of the residuals follows , irrespective of the values taken by the dependent variable y When any of the two is not observed, more sophisticated modelling approaches are necessary. Let’s consider two hypothetical problems that violate the two respective assumptions, where y … Continue reading Linear...

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Genome-wide association studies in R

October 9, 2017
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Genome-wide association studies in R

This time I elaborate on a much more specific subject that will mostly concern biologists and geneticists. I will try my best to outline the approach as to ensure non-experts will still have a basic understanding. This tutorial illustrates the power of genome-wide association (GWA) studies by mapping the genetic determinants of cholesterol levels using … Continue reading Genome-wide...

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Partial least squares in R

June 17, 2017
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Partial least squares in R

My last entry introduces principal component analysis (PCA), one of many unsupervised learning tools. I concluded the post with a demonstration of principal component regression (PCR), which essentially is a ordinary least squares (OLS) fit using the first principal components (PCs) from the predictors. This brings about many advantages: There is virtually no limit for the … Continue reading Partial...

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Principal Component Analysis in R

January 23, 2017
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Principal Component Analysis in R

Principal component analysis (PCA) is routinely employed on a wide range of problems. From the detection of outliers to predictive modeling, PCA has the ability of projecting the observations described by variables into few orthogonal components defined at where the data ‘stretch’ the most, rendering a simplified overview. PCA is particularly powerful in dealing with multicollinearity and variables that … Continue reading Principal...

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Probability distributions in R

December 4, 2016
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Probability distributions in R

Some of the most fundamental functions in R, in my opinion, are those that deal with probability distributions. Whenever you compute a P-value you rely on a probability distribution, and there are many types out there. In this exercise I will cover four: Bernoulli, Binomial, Poisson, and Normal distributions. Let me begin with some theory first: Bernoulli … Continue reading Probability...

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