Journal of Statistical Software
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Server-side Statistics Scripting in PHP
Full text linkOn the UCLA Statistics WWW server there are a large number of demos and calculators that can be used in statistics teaching and research. Some of these demos require substantial amounts of computation, others mainly use graphics. These calculators and demos are implemented in various different ways, reflecting developments in WWW based computing.
As usual, one of the main choices is between doing the work on the client-side (i.e. in the browser) or on the server-side (i.e. on our WWW server). Obviously, client-side computation puts fewer demands on the server. On the other hand, it requires that the client downloads Java applets, or installs plugins and/or helpers. If JavaScript is used, client-side computations will generally be slow. We also have to assume that the client is installed properly, and has the required capabilities. Requiring too much on the client-side has caused browsing machines such as Netscape Communicator to grow beyond all reasonable bounds, both in size and RAM requirements. Moreover requiring Java and JavaScript rules out such excellent browsers as Lynx or Emacs W3.
For server-side computing, we can configure the server and its resources ourselves, and we need not worry about browser capabilities and configuration. Nothing needs to be downloaded, except the usual HTML pages and graphics. In the same way as on the client side, there is a scripting solution, where code is interpreted, or a ob ject-code solution using compiled code. For the server-side scripting, we use embedded languages, such as PHP/FI. The scripts in the HTML pages are interpreted by a CGI program, and the output of the CGI program is send to the clients. Of course the CGI program is compiled, but the statistics procedures will usually be interpreted, because PHP/FI does not have the appropriate functions in its scripting language. This will tend to be slow, because embedded languages do not deal efficiently with loops and similar
constructs.
Thus a first step towards greater efficiency is to compile the necessary primitives into the PHP/FI executable. This is easy to do, because the API is quite simple. In the extensions below, we have added the complete ranlib and dcdflib to PHP, plus some additional useful functions. The source code for these extensions, plus Solaris binaries for libranlib.a and libdcdf.a can be obtained from our server.
Interpreting a PHP script, even with our new primitives, still requires starting up a CGI process for each page that is read. Again, this can be improved upon. We could use FastCGI to keep the CGI process around on a permanent basis. Instead, we have chosen a more direct method. PHP can be compiled as an Apache module, i.e. it can be compiled into the Apache HTTPD server binary. This means that PHP scripts are interpreted by the WWW server, which is always around, and which will fork additional children if necessary. No CGI processes need to be started. The PHP install process creates a libphp.a and mod_php.c in the Apache source directories, which can be used to build an enhanced server. This has the additional advantage of security, because all security features of the server can be used, and none of the pitfalls of using CGI or Java apply.
Using PHP, in combination with the WWW server, also has some disadvantages. Although we can make simple static plots, using the gd library, we cannot use any dynamics, and interaction between the user and the page is somewhat limited. Java, or scripts using a client-side Xlisp-Stat as a helper, are more flexible in this respect. As a consequence, the UCLA Statistics pages still use a combined approach, with server-side PHP and CGI and client-side Xlisp-Stat and Java/JavaScript. Sometime this year, server-side Java scripting will become available, and then it seems advisable to switch as much of the code as possible to the server-side
The Asypow S(plus) Library for Asymptotic Power Calculations
Full text linkThe asypow library consists of routines written in the S language that calculate power and related quantities utilizing asymptotic methods. A paper describing these methods with examples is in preparation [1]. Two methods are available. The likelihood ratio method (LR) is described in [2]. Another general method appears recently in [3]; and we designate it the SMO method after the initials of the authors
Calibrate Your Eyes to Recognize High-Dimensional Shapes from Their Low-Dimensional Projections
No full textThis paper provides a suite of datasets from standard multivariate distributions and simple high-dimensional geomtric shapes that can be used to visually calibrate new users of grand tours. It contains animations of 1-D, 2-D, 3-D, 4-D and 5-D grand tours, links to starting XGobi or XLispStat on the calibration data sets, and C code for generating a grand tour.
The purpose of the paper is two-fold: providing code for the grand tour that others could pick up and modify (it is not easy to code this version which is why there are very few implementations currently available), and secondly, provide a variety of training datasets to help new users get a visual sense for high-dimensional data
MCSim: A Monte Carlo Simulation Program
No full textMCSim consists of two pieces, a model generator and a simulation engine. The model generator, mod, was created to facilitate the model maintenance and simulation definition, while keeping execution time fast. Other programs have been created to the same end, the Matlab family of graphical interactive programs being some of the more general and easy to use. Still, many available tools are not optimal for performing time and computer intensive Monte Carlo analysis. MCSim was created specifically to this end: to perform Monte Carlo analysis in a highly optimized, and easy to maintain environment
COVAR: Computer Program for Multifactor Relative Risks and Tests of Hypotheses Using a Variance-Covariance Matrix from Linear and Log-Linear Regression
Full text linkA computer program for multifactor relative risks, confidence limits, and tests of hypotheses using regression coefficients and a variance-covariance matrix obtained from a previous additive or multiplicative regression analysis is described in detail. Data used by the program can be stored and input from an external disk-file or entered via the keyboard. The output contains a list of the input data, point estimates of single or joint effects, confidence intervals and tests of hypotheses based on a minimum modified chi-square statistic. Availability of the program is also discussed
Interactive Correspondence Analysis in a Dynamic Object-Oriented Environment
Full text linkA highly interactive, user-friendly object-oriented software package written in LispStat is introduced that performs simple and multiple correspondence analysis, and profile analysis. These three techniques are integrated into a single environment driven by a user-friendly graphical interface that takes advantage of Lisp-Stat's advanced graphical capabilities. Techniques that assess the stability of the solution are also introduced. Some of the features of the package include colored graphics, incremental graph zooming capabilities, manual point separation to determine identities of overlapping points, and stability and fit measures. The features of the package are used to show some interesting trends in a large educational dataset
XLISP-Stat Tools for Building Generalised Estimating Equation Models
No full textThis paper describes a set of Lisp-Stat tools for building Generalised Estimating Equation models to analyse longitudinal or clustered measurements. The user interface is based on the built-in regression and generalised linear model prototypes, with the addition of object-based error functions, correlation structures and model formula tools. Residual and deletion diagnostic plots are available on the cluster and observation level and use the dynamic graphics capabilities of Lisp-Stat
A Diagnostic to Assess the Fit of a Variogram Model to Spatial Data
Full text linkThe fit of a variogram model to spatially-distributed data is often difficult to assess. A graphical diagnostic written in S-plus is introduced that allows the user to determine both the general quality of the fit of a variogram model, and to find specific pairs of locations that do not have measurements that are consonant with the fitted variogram. It can help identify nonstationarity, outliers, and poor variogram fit in general. Simulated data sets and a set of soil nitrogen concentration data are examined using this graphical diagnostic
Homogeneity Analysis in Xlisp-Stat
No full textIn this paper a highly interactive, user-friendly Lisp program is introduced to perform homogeneity analysis. A brief introduction to the technique is presented as well as its modification in the presence of missing data. The algorithm and its Lisp implemenation is discussed, and an overview of the object oriented code that produces the interactive dialogs and plots is provided. In order to demonstrate the main features of the program, a small and a large dataset are analyzed. Finally, some comparisons are made with other currently available programs