By W.N. Venables

S is a robust atmosphere for the statistical and graphical research of information. It offers the instruments to enforce many statistical principles which were made attainable through the frequent availability of workstations having solid snap shots and computational functions. This publication is a consultant to utilizing S environments to accomplish statistical analyses and offers either an creation to using S and a direction in glossy statistical tools. Implementations of S can be found commercially in S-PLUS(R) workstations and because the Open resource R for a variety of computers. the purpose of this ebook is to teach how one can use S as a strong and graphical information research process. Readers are assumed to have a simple grounding in facts, and so the publication is meant for would-be clients of S-PLUS or R and either scholars and researchers utilizing information. all through, the emphasis is on proposing functional difficulties and entire analyses of genuine information units. a few of the equipment mentioned are cutting-edge methods to themes equivalent to linear, nonlinear and soft regression types, tree-based equipment, multivariate research, trend attractiveness, survival research, time sequence and spatial facts. all through sleek thoughts reminiscent of strong tools, non-parametric smoothing and bootstrapping are used the place acceptable. This fourth version is meant for clients of S-PLUS 6.0 or R 1.5.0 or later. a considerable swap from the 3rd version is updating for the present models of S-PLUS and including insurance of R. The introductory fabric has been rewritten to emphasis the import, export and manipulation of information. elevated computational strength permits much more computer-intensive the way to be used, and techniques similar to GLMMs, MARS, SOM and aid vector machines are thought of.

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The index vector must be of the same length as the vector from which elements are to be selected. Values corresponding to T in the index vector are selected and those corresponding to F or NA are omitted. na(x)] creates an object y that will contain the non-missing values of x , in the same order as they originally occurred. Note that if x has any missing values, y will be shorter than x. na(x)] <- 0 replaces any missing values in x by zeros. 3. A vector of negative integers. This specifies the values to be excluded rather than included.

Calculations on cross-classifications The combination of a vector and a labelling factor or factors is an example of what is called a ragged array, since the group sizes can be irregular. ) To calculate the average number of days absent for each age group (used on page 170) we can use the function tapply, the analogue of lapply and apply for ragged arrays. 606 The first argument is the vector for which functions on the groups are required, the second argument, INDICES, is the factor defining the groups and the third argument, FUN, is the function to be evaluated on each group.

The function c has a named argument recursive ; if this is true the list arguments are unlisted before being joined together. Thus c(list(x = 1:3, a = 3:6), list(y = 8:23, b = c(3, 8, 39))) is a list with four (vector) components, but adding recursive = T gives a vector of length 26. ) S-PLUS has a function concat that concatenates vectors and omits the names, whereas c keeps all the names (even if this results in duplicates). xxx that convert to the specified type in the best way possible. matrix will convert a numerical data frame to a numerical matrix, and a data frame with any character or factor columns to a character matrix.