University of Minnesota

School of Statistics
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Summary of MacAnova 4.13 Features

  • Variables and operations
    • Named real, logical and character vectors, matrices and arrays, plus structures and graph variables
    • Coordinate labels for vectors, matrices and arrays
    • Usual arithmetic (+, -, *, /, ^ or **) plus modular division (%%) and bit manipulation (%&, %|, %^, %!)
    • Most operators and many functions work with structures, allowing analysis of non-rectangular data sets.
    • Descriptive notes can be attached to all variables
  • Help
    • On-line usage summaries and complete help for over 550 topics in 8 automatically searched files
    • HTML help you can read in your favorite web browser
  • Descriptive statistics
    • Means, variances, medians, quartiles, extremes, skewness, kurtosis
    • Cross tabs and cell means, variances, standard deviations and extremes, plus cell covariance matrices for multivariate data.
  • Linear and generalized linear models
    • A linear model grammar with multiple error terms and shortcuts for polynomial and periodic regressions
    • Up to 96 variables, up to 31 of which can be categorical factors
    • "On the fly" transformations of response and predictor variables
    • ANOVA, MANOVA and regression with optional weights
    • Robust ANOVA and regression
    • Logistic, probit and Poisson regression
    • Iterative proportional fitting
    • Model coefficients, standard errors, contrasts
    • Residual plots and macros to summarize residuals
    • Branch and bound determination of best subset regression with ability to force in a subset and save the models selected
    • Macros for step-wise regression
    • Power and sample size functions for CRD and RBD
    • t-tests, confidence intervals
    • Expected mean squares
    • Macro for non-linear least squares
  • Matrix algebra
    • Matrix multiplication (operators %*%, %c% and %C%) and inversion
    • Linear equation solution (operators %/% and %\%)
    • Eigenvalues and eigenvectors and relative eigenvalues and eigenvectors
    • Cholesky, QR and SVD decompositions
    • Beaton sweep operator
    • Trace, diagonal, determinant, outer products, and other matrix and vector manipulations
    • Permutation of dimensions of matrices and arrays
    • Other operations such as Kronecker products and Moore-Penrose inverses are provided as macros
  • Time series
    • Fast Fourier transforms (maximum prime factor of length is 29)
    • Convolution and sums of lagged products
    • Forward and backward autoregression and moving average operators
    • Yule-Walker solver and its inverse
    • ACF to partial ACF and its inverse
    • Spectrum and cross-spectrum analysis, including multi-taper
    • estimation
    • Macros for least squares and maximum likelihood estimation of ARIMA models, including seasonal models
    • Macros for Hannan-Rissanen and innovations estimation of ARIMA models
    • Macros for computing approximate covariances and variances of autocorrelations using Bartlett's formula
    • Macros to compute the autocovariance function and the spectrum corresponding to an ARMA model
    • Time series plots
    • Frequency function plots
  • Multivariate analysis
    • MANOVA
    • Hierarchical cluster analysis
    • K-means cluster analysis
    • Macros related to discrimination and factor analysis, including stepwise discriminant analysis
    • Macros for ULS, GLS and ML factor extraction
    • Varimax, quartimax, equimax and orthomax factor rotation
  • Design of experiments
    • Confounding 2 and 3 series factorials
    • Finding aliases in 2 and 3 series fractional factorials
    • Choosing generators and design points in 2 series fractional factorials
    • Canonical analysis of 2nd order response surface
    • Constrained maximization of quadratic functions
    • Recovery of interblock information in incomplete block designs
    • REML analysis of unbalanced models with fixed and random terms
    • "ANOVA" estimates of random effect variances in mixed effects
    • Expectation of mean squares in mixed models analysis of variance
  • Random numbers and probabilities
    • Uniform, normal, Poisson and binomial pseudo-random number generators
    • Cumulative and inverse normal, chisquare, F, t, beta, gamma
    • Cumulative and inverse noncentral chisquare
    • Cumulative non-central F, beta and t
    • Cumulative poisson and binomial
    • Cumulative and inverse Studentized range
    • Cumulative and inverse Dunnett's t
  • Graphical features
    • Scatter plots, including several y's vs one x
    • Line and impulse plots
    • Box plots, histograms and stem and leaf displays
    • Interaction plots
    • Panel graphs, rectangular arrays of small graphs, including scatterplot matrix
    • Contour plots with interactively labelled contours
    • Stepwise construction of graphs
    • Replotting with changed labels, bounds and tick marks
    • Mouse specification of locations for points and lines on a graph
    • GRAPH variables encapsulating all the information in a graph
    • Graphs are saved when you save your workspace and redrawn when you restore the workspace
  • Transformations
    • max, min, sum, prod, all operating on columns of matrices
    • Usual transcendentals
    • Rational functions (ratios of polynomials)
    • Ordering, ranking, sorting
  • Importing and exporting data
    • Direct importing of data from spreadsheets using the clipboard
    • Direct exporting of data and results to other programs using the clipboard
    • Reading and writing named data sets from and to plain text files
    • Reading unstructured data together with variable names from plain text files
  • Programmability
    • Macros used just like functions
    • Many built-in macros plus 8 files of loadable macros (general, time series, ARIMA modeling, design of experiments, regression, multivariate analysis, graphics, and mathematics)
    • Functions for automatic checking of macro arguments
    • Automatic searching for macros that have not yet been loaded
    • For and while loops, if, else, elseif, break, next, return
  • User functions
    • Dynamic loading and execution of user compiled code with callbacks to MacAnova functions.

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Last modified Thu Feb 5 12:26:53 CST 2004