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SAS/STAT Topics

SAS/STAT Software

Market Research

The SAS/STAT market research procedures include the following:

BCHOICE Procedure


The BCHOICE procedure fits Bayesian discrete choice models by using MCMC methods. The procedure's capabilities include the following:

  • fits the following types of models:
    • multinomial logit
    • multinomial probit
    • nested logit
    • multinomial logit with random effects
    • multinomial probit with random effects
  • samples directly from the full conditional distribution when possible
  • supports the following sampling algorithms:
    • Metropolis-Hastings approach of Gamerman
    • random walk Metropolis
    • latent variables via the data augmentation method
  • provides a variety of Markov chain convergence diagnostics
  • works with the postprocessing autocall macros that are designed for Bayesian posterior samples
  • supports a CLASS statement for specifying classification variables
  • supports a RESTRICT statement, enabling you to specify boundary requirements and order constraints on fixed effects for logit models
  • multithreaded
  • creates an output data set that contains the posterior samples of all parameters
  • creates an output data set that contains random samples from the posterior predictive distribution of the choice probabilities
  • creates an output data set that corresponds to any output table
  • supports BY group processing
  • automatically produces graphs by using ODS Graphics
For further details, see BCHOICE Procedure

CORRESP Procedure


The CORRESP procedure performs simple correspondence analysis and multiple correspondence analysis (MCA). You can use correspondence analysis to find a low-dimensional graphical representation of the rows and columns of a crosstabulation or contingency table. Each row and column is represented by a point in a plot determined from the cell frequencies. PROC CORRESP can also compute coordinates for supplementary rows and columns. The procedure enables you to do the following:

  • use two kinds of input: raw categorical responses on two or more classification variables or a two-way contingency table
  • specify the number of dimensions or axes
  • specify the standardization for the row and column coordinates
  • create a data set that contains coordinates and the results of the correspondence analysis
  • create a data set that contains frequencies and percentages
  • create a data set that corresponds to any output table
  • perform BY group processing, which enebales you to obtain separate analyses on grouped observations
  • automatically display the correspondence analysis plot by using ODS Graphics
For further details, see CORRESP Procedure

MDS Procedure


The MDS procedure fits two- and three-way, metric and nonmetric multidimensional scaling models. Multidimensional scaling refers to a class of methods. These methods estimate coordinates for a set of objects in a space of specified dimensionality. The input data are measurements of distances between pairs of objects. A variety of models can be used that include different ways of computing distances and various functions relating the distances to the actual data. The following are highlights of the MDS procedure's features:

  • estimates the following parameters by nonlinear least squares:
    • configuration — the coordinates of each object in a Euclidean or weighted Euclidean space of one or more dimensions
    • dimension coefficients — for each data matrix, the coefficients that multiply each coordinate of the common or group weighted Euclidean space to yield the individual unweighted Euclidean space
    • transformation parameters — intercept, slope, or exponent in a linear, affine, or power transformation relating the distances to the data
  • fits either a regression model of the form
    fit(datum) = fit(trans(distance)) + error
    or a measurement model of the form
    fit(trans(datum)) = fit(distance) + error
    where
    • fit is a predetermined power or logarithmic transformation
    • trans is an estimated (`optimal') linear, affine, power, or monotone transformation
    • datum is a measure of the similarity or dissimilarity of two objects or stimuli
    • distance is a distance computed from the estimated coordinates of the two objects and estimated dimension coefficients in a space of one or more dimensions
    • error is an error term assumed to have an approximately normal distribution and to be independently and identically distributed for all data
  • performs BY group processing, whcih enables you to obtain separate analyses on grouped observations
  • performs weighted analysis
  • creates a SAS data set that corresponds to any output table
  • automatically creates graphs by using ODS Graphics
For further details, see MDS Procedure

PHREG Procedure


The PHREG procedure performs regression analysis of survival data based on the Cox proportional hazards model. Cox's semiparametric model is widely used in the analysis of survival data to explain the effect of explanatory variables on hazard rates. The following are highlights of the PHREG procedure's features:

  • fits a superset of the Cox model, known as the multiplicative hazards model or the Anderson-Gill model
  • fits frailty models
  • fits competing risk model of Fine and Gray
  • performs stratified analysis
  • includes four methods for handling ties in the failure times
  • provides four methods of variable selection
  • permits an offset in the model
  • performs weighted estimation
  • enables you to use SAS programming statements within the procedure to modify values of the explanatory variables or to create ne explanatory variables
  • tests linear hypotheses about the regression parameters
  • estimates customized hazard ratios
  • performs graphical and numerical assessment of the adequacy of the Cox regression model
  • creates a new SAS data set that contains the baseline function estimates at the event times of each stratum for every specified set of covariates
  • outputs survivor function estimates, residuals, and regression diagnostics
  • performs conditional logistic regression analysis for matched case-control studies
  • fits multinomial logit choice models for discrete choice data
  • performs sampling-based Bayesian analysis
  • performs BY group processing, which enables you to obtain separate analyses on grouped observations
  • creates an output data set that contains parameter and covariance estimates
  • creates an output data set that contains user-specified statistics
  • creates a SAS data set that corresponds to any output table
  • automatically created graphs by using ODS Graphics
For further details, see PHREG Procedure

PRINQUAL Procedure


The PRINQUAL procedure performs principal component analysis (PCA) of qualitative, quantitative, or mixed data. PROC PRINQUAL enables you to do the following:

  • find linear and nonlinear transformations of variables, using the method of alternating least squares, that optimize properties of the transformed variables' correlation or covariance matrix. Nonoptimal transformations such as logarithm and rank are also available.
  • fit metric and nonmetric principal component analyses
  • perform metric and nonmetric multidimensional preference (MDPREF) analyses
  • reduce the number of variables for subsequent use in regression analyses, cluster analyses, and other analyses
  • choose between three methods, each of which seeks to optimize a different property of the transformed variables' covariance or correlation matrix. These methods are as follows:
    • maximum total variance, or MTV
    • minimum generalized variance, or MGV
    • maximum average correlation, or MAC
  • transform ordinal variables monotonically by scoring the ordered categories so that order is weakly preserved (adjacent categories can be merged) and the covariance matrix is optimized. You can undo ties optimally or leave them tied. You can also transform ordinal variables to ranks.
  • transform nominal variables by optimally scoring the categories
  • transform interval and ratio scale of measurement variables linearly, or transform them nonlinearly with spline transformations or monotone spline transformations. In addition, nonoptimal transformations for logarithm, rank, exponential, power, logit, and inverse trigonometric sine are available.
  • estimate missing data without constraint, with category constraints (missing values within the same group get the same value), and with order constraints (missing value estimates in adjacent groups can be tied to preserve a specified ordering).
  • detect nonlinear relationships
  • perform weighted estimation
  • perform BY group processing, which enables you to obtain separate analyses on grouped observations
  • create a SAS data set that contains the original variables, transformed variables, components, or data approximations
  • create a SAS data set that corresponds to any output table
  • automatically create graphs by using ODS Graphics
For further details, see PRINQUAL Procedure

TRANSREG Procedure


The TRANSREG (transformation regression) procedure fits linear models, optionally with smooth, spline, Box-Cox, and other nonlinear transformations of the variables. The following are highlights of the TRANSREG procedure's features:

  • enables you to fit linear models including:
    • ordinary regression and ANOVA
    • metric and nonmetric conjoint analysis (Green and Wind 1975; de Leeuw, Young, and Takane 1976)
    • linear models with Box-Cox (1964) transformations of the dependent variables
    • regression with a smooth (Reinsch 1967), spline (de Boor 1978; van Rijckevorsel 1982), monotone spline (Winsberg and Ramsay 1980), or penalized B-spline (Eilers and Marx 1996) fit function
    • metric and nonmetric vector and ideal point preference mapping (Carroll 1972)
    • simple, multiple, and multivariate regression with variable transformations (Young, de Leeuw, and Takane 1976; Winsberg and Ramsay 1980; Breiman and Friedman 1985)
    • redundancy analysis (Stewart and Love 1968) with variable transformations (Israels 1984)
    • canonical correlation analysis with variable transformations (van der Burg and de Leeuw 1983)
    • response surface regression (Meyers 1976; Khuri and Cornell 1987) with variable transformations
  • enables you to use a data set that can contain variables measured on nominal, ordinal, interval, and ratio scales; you can specify any mix of these variable types for the dependent and independent variables
  • transform nominal variables by scoring the categories to minimize squared error (Fisher 1938), or treat nominal variables as classification variables
  • enables you to transform ordinal variables by monotonically scoring the ordered categories so that order is weakly preserved (adjacent categories can be merged) and squared error is minimized. Ties can be optimally untied or left tied (Kruskal 1964). Ordinal variables can also be transformed to ranks.
  • enables you to transform interval and ratio scale of measurement variables linearly or nonlinearly with spline (de Boor 1978; van Rijckevorsel 1982), monotone spline (Winsberg and Ramsay 1980), penalized B-spline (Eilers and Marx 1996), smooth (Reinsch 1967), or Box-Cox (Box and Cox 1964) transformations. In addition, logarithmic, exponential, power, logit, and inverse trigonometric sine transformations are available.
  • fits a curve through a scatter plot or fit multiple curves, one for each level of a classification variable
  • enables you to constrain the functions to be parallel or monotone or have the same intercept
  • enables you to code experimental designs and classification variables prior to their use in other analyses
  • perform sweighted estimation
  • generates output data sets including
    • ANOVA results
    • regression tables
    • conjoint analysis part-worth utilities
    • coefficients
    • marginal means
    • original and transformed variables, predicted values, residuals, scores, and more
  • performs BY group processing, which enables you to obtain separate analyses on grouped observations
  • automatically creates graphs by using ODS Graphics
For further details, see TRANSREG Procedure