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An R Introduction to Statistics

An R Introduction to Statistics

Why R is Hard to Learn by Bob Muenchen R has a reputation of being hard to learn. Some of that is due to the fact that it is radically different from other analytics software. Some is an unavoidable byproduct of its extreme power and flexibility. And, as with any software, some is due to design decisions that, in hindsight, could have been better. Statistics with R Warning Here are the notes I took while discovering and using the statistical environment R. However, I do not claim any competence in the domains I tackle: I hope you will find those notes useful, but keep you eyes open -- errors and bad advice are still lurking in those pages... Should you want it, I have prepared a quick-and-dirty PDF version of this document. The old, French version is still available, in HTML or as a single file. You may also want all the code in this document. 1.

Hidden Markov Model for CpG islands — Stats366 / Stats 166 Course Notes Does a Short DNA Stretch Come from a CpG Island? Here we use likelihood scores as discriminatory statistic. We need to model both the CpG Islands and the regions that are not CpG Islands. For this we need training sequences. We can collect databases of nucleotides from both types of sequences.

Learn R Upload mybringback Loading... Working... Beginner's guide to R: Introduction R is hot. Whether measured by more than 4,400 add-on packages, the 18,000+ members of LinkedIn's R group or the close to 80 R Meetup groups currently in existence, there can be little doubt that interest in the R statistics language, especially for data analysis, is soaring. Why R? It's free, open source, powerful and highly extensible. Software - Miquel De Cáceres Ainsa Indicspecies R package Indicator species are species that are used as ecological indicators of community or habitat types, environmental conditions, or environmental changes. In order to determine indicator species, the characteristic to be predicted is represented in the form of a classification of the sites, which is compared to the patterns of distribution of the species found in that set of sites. 'Indicspecies' is an R package that contains a set of functions to assess the strength of relationship between species and a classification of sites. As such, it includes the well-known IndVal method (Dufrêne & Legendre 1997) and extends it by allowing the user to study combinations of site groups (De Cáceres et al. 2010). Apart from the IndVal index, the package allows computing many other indices suitable for this kind of associations (De Cáceres & Legendre 2009), such as the phi coefficient of association.

Hidden Markov Models — Bioinformatics 0.1 documentation A little more about R In previous practicals, you learnt how to create different types of variables in R such as scalars, vectors and lists. Sometimes it is useful to create a variable before you actually need to store any data in the variable. To create a vector without actually storing any data in it, you can use the numeric() command to create a vector for storing numbers, or the character() command to create a vector for storing characters (eg. “A”, “hello”, etc.) For example, you may want to create a vector variable for storing the square of a number, and then store numbers in its elements afterwards:

Statistical Learning About This Course This is an introductory-level course in supervised learning, with a focus on regression and classification methods. The syllabus includes: linear and polynomial regression, logistic regression and linear discriminant analysis; cross-validation and the bootstrap, model selection and regularization methods (ridge and lasso); nonlinear models, splines and generalized additive models; tree-based methods, random forests and boosting; support-vector machines. Some unsupervised learning methods are discussed: principal components and clustering (k-means and hierarchical). This is not a math-heavy class, so we try and describe the methods without heavy reliance on formulas and complex mathematics.

knitr: Elegant, flexible and fast dynamic report generation with R Overview The knitr package was designed to be a transparent engine for dynamic report generation with R, solve some long-standing problems in Sweave, and combine features in other add-on packages into one package (knitr ≈ Sweave + cacheSweave + pgfSweave + weaver + animation::saveLatex + R2HTML::RweaveHTML + highlight::HighlightWeaveLatex + 0.2 * brew + 0.1 * SweaveListingUtils + more). This package is developed on GitHub; for installation instructions and FAQ’s, see README. This website serves as the full documentation of knitr, and you can find the main manual, the graphics manual and other demos / examples here. For a more organized reference, see the knitr book.

Introduction to R and Bioconductor Introduction to R and Bioconductor Seattle, USA Instructors Chao-Jen Wong Herve Pages Marc Carlson Martin Morgan Nishant Gopalakrishnan Patrick Aboyoun Seth Falcon