By William N. Venables, David M. Smith
This educational handbook offers a entire advent to R, a software program package deal for statistical computing and snap shots. R helps a variety of statistical options and is well extensible through user-defined features. one among R's strengths is the benefit with which publication-quality plots could be produced in a wide selection of codecs. this can be a revealed version of the educational documentation from the R distribution, with extra examples, notes and corrections. it really is in response to R model 2.9.0, published April 2009. R is loose software program, allotted below the phrases of the GNU basic Public License (GPL). it may be used with GNU/Linux, Unix and Microsoft home windows. the entire funds raised from the sale of this booklet helps the improvement of unfastened software program and documentation.
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Extra resources for An Introduction to R
Functions are provided to evaluate the cumulative distribution function P (X ≤ x), the probability density function and the quantile function (given q, the smallest x such that P (X ≤ x) > q), and to simulate from the distribution. Distribution beta binomial Cauchy chi-squared exponential F gamma geometric hypergeometric log-normal logistic negative binomial normal Poisson signed rank Student’s t uniform Weibull Wilcoxon R name beta binom cauchy chisq exp f gamma geom hyper lnorm logis nbinom norm pois signrank t unif weibull wilcox additional arguments shape1, shape2, ncp size, prob location, scale df, ncp rate df1, df2, ncp shape, scale prob m, n, k meanlog, sdlog location, scale size, prob mean, sd lambda n df, ncp min, max shape, scale m, n Prefix the name given here by ‘d’ for the density, ‘p’ for the CDF, ‘q’ for the quantile function and ‘r’ for simulation (r andom deviates).
4 Editing data When invoked on a data frame or matrix, edit brings up a separate spreadsheet-like environment for editing. This is useful for making small changes once a data set has been read. The command > xnew <- edit(xold) will allow you to edit your data set xold, and on completion the changed object is assigned to xnew. If you want to alter the original dataset xold, the simplest way is to use fix(xold), which is equivalent to xold <- edit(xold). frame()) to enter new data via the spreadsheet interface.
Chapter 5: Arrays and matrices 25 If M is in fact square, then, it is not hard to see that > absdetM <- prod(svd(M)$d) calculates the absolute value of the determinant of M. If this calculation were needed often with a variety of matrices it could be defined as an R function > absdet <- function(M) prod(svd(M)$d) after which we could use absdet() as just another R function. As a further trivial but potentially useful example, you might like to consider writing a function, say tr(), to calculate the trace of a square matrix.