By William N. Venables, David M. Smith, R Development Core Team

ISBN-10: 3900051127

ISBN-13: 9783900051129

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**Additional info for An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics**

**Example text**

Dimnames <- function(a) { ## Remove all dimension names from an array for compact printing. dimnames(X) This is particularly useful for large integer arrays, where patterns are the real interest rather than the values. 3 Recursive numerical integration Functions may be recursive, and may themselves define functions within themselves. Note, however, that such functions, or indeed variables, are not inherited by called functions in higher evaluation frames as they would be if they were on the search path.

First evaluation in S S> cube(2) Error in sq(): Object "n" not found Chapter 10: Writing your own functions 47 Dumped S> n <- 3 S> cube(2) [1] 18 ## then the same function evaluated in R R> cube(2) [1] 8 Lexical scope can also be used to give functions mutable state. In the following example we show how R can be used to mimic a bank account. A functioning bank account needs to have a balance or total, a function for making withdrawals, a function for making deposits and a function for stating the current balance.

R provides an interlocking suite of facilities that make fitting statistical models very simple. As we mention in the introduction, the basic output is minimal, and one needs to ask for the details by calling extractor functions. 1 Defining statistical models; formulae The template for a statistical model is a linear regression model with independent, homoscedastic errors p yi = βj xij + ei , ei ∼ NID(0, σ 2 ), i = 1, . . , n j=0 In matrix terms this would be written y = Xβ + e where the y is the response vector, X is the model matrix or design matrix and has columns x0 , x1 , .

### An Introduction to R: Notes on R: A Programming Environment for Data Analysis and Graphics by William N. Venables, David M. Smith, R Development Core Team

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