Categorical data analysis
Of analyses of categorical wikipedia, the free to: navigation, a list of statistical procedures which can be used for the analysis of categorical data, also known as data on the nominal scale and as categorical variables. Hazards rated failure time (aft) –aalen al trials / ering s / quality tion nmental phic information ries: statistics-related listscategorical logged intalkcontributionscreate accountlog pagecontentsfeatured contentcurrent eventsrandom articledonate to wikipediawikipedia out wikipediacommunity portalrecent changescontact links hererelated changesupload filespecial pagespermanent linkpage informationwikidata itemcite this a bookdownload as pdfprintable page was last edited on 28 april 2015, at 23: is available under the creative commons attribution-sharealike license;. A non-profit ations/ case ng & int ations/ case int tical statistical rical data mining & machine of atory data data analysis & ement systems carlo ametric ility s capability size tical process control series analysis & rical data rical data is data that classifies an observation as belonging to one or more categories.
Statgraphics includes many procedures for dealing with such data, including modeling procedures contained in the sections on analysis of variance, regression analysis, and statistical process aphics centurion aphicssigma aphics web pondence le correspondence reliability o and butterfly tabulation procedure is designed to summarize a single column of attribute data. If desired, a selected slice may be offset from the rest of the pie or : piechart/donut or watch frequency tables procedure analyzes a single categorical factor that has already been tabulated. Statistical tests may also be performed to determine whether the data conform to a set of multinomial : frequency crosstabulation analysis procedure is designed to summarize two columns of attribute data.
The frequencies are displayed both in tabular form and graphically as a barchart, mosaic plot, or : contingency tables procedure is designed to analyze and display frequency data contained in a two-way table. The frequencies are displayed both in tabular form and graphically as a barchart, mosaic plot, or : contingency median polish procedure constructs a model for data contained in a two-way table. Although the model used is similar to that estimated using a two-way analysis of variance, the terms in the model are estimated using medians rather than means.
This makes the estimates more resistant to the possible presence of : median pondence correspondence analysis procedure creates a map of the rows and columns in a two-way contingency table for the purpose of providing insights into the relationships amongst the categories of the row and/or column variables. An important part of the output is a correspondence map on which the distance between two categories is a measure of their : correspondence le correspondence multiple correspondence analysis procedure creates a map of the associations among categories of two or more variables. However, unlike that procedure which compares categories of each variable separately, this categorical data analysis procedure is concerned with interrelationships amongst the variables.
For a complex map such as that to the right, the statgraphics dynamic rotate, zoom and pan operations can be very : multiple correspondence likert plot procedure analyzes data recorded on a likert scale. Likert scales are commonly used in survey research to record user responses to a : likert or watch reliability item reliability analysis is designed to estimate the reliability or consistency of a set of variables. The major output of the procedure is cronbach’s : item reliability o and buttefly tornado and butterfly plots procedure creates two similar plots that compare 2 samples of attribute data.
Case ng & int ations/ case int tical statistical rical data mining & machine of atory data data analysis & ement systems carlo ametric ility s capability size tical process control series analysis & rical data rical data is data that classifies an observation as belonging to one or more categories. Management ility & tics & operations rical data ptive minant bution mixture sequential design and udinal data g data ariate ametric ametric and sample metric ural equations sampling and /stat procedures rical data are two approaches to performing categorical data analyses. The first computes statistics tables defined by categorical variables (variables that assume only a limited number of discrete values),Performs hypothesis tests about the association between these variables, and requires the assumption of ized process; call these methods randomization other approach investigates the association by modeling a categorical response variable, regardless r the explanatory variables are continuous or categorical; call these methods modeling sas/stat categorical data analysis procedures include the following:Catmod procedure — categorical data procedure — one-way to n-way frequency and contingency (crosstabulation) — finite mixture procedure — generalized linear ic procedure — models with binary, ordinal, or nominal dependent procedure — maximum likelihood estimates of regression parameters and the natural.
Or threshold) response rate for quantal response data from biological assays or other discrete event catmod procedure performs categorical data modeling of data that can be represented by a contingency catmod fits linear models to functions of response frequencies, and it can be used for linear modeling,Log-linear modeling, logistic regression, and repeated measurement procedure enables you to do the following:Estimate model parameters by using weighted least squares (wls) for a wide range of general or maximum likelihood (ml) for log-linear models and the analysis of generalized raw data, where each observation is a subject, supply cell count data,Where each observation is a cell in a contingency table, or directly input a covariance uct linear functions of the model parameters or log-linear effects and test the hypothesis that the linear combination equals m constrained m by group precessing, which enables you to obtain separate analyses on grouped a data set that contains the observed and predicted values of the ons, their standard errors, the residuals, and variables that describe the population and es. In addition, if you use the standard response functions, the data set includes predicted values for the cell frequencies or the cell probabilities, together with their standard errors and a data set that contains the estimated parameter vector and its estimated covariance a data set that corresponds to any output further details, see catmod freq procedure produces one-way to n-way frequency and contingency (crosstabulation) two-way tables, proc freq computes tests and measures of association. For n-way tables, proc freq fied analysis by computing statistics across, as well as within, following are highlights of the freq procedure's features:Computes goodness-of-fit tests for equal proportions or specified null proportions for one-way frequency es confidence limits and tests for binomial proportions, including tests for equivalence for one-way frequency e various statistics to examine the relationships between two classification variables.
The statistics for include the following:Chi-square tests and es of (binomial proportions) and risk differences for 2 x 2 ratios and relative risks for 2 x 2 and measures of n-mantel-haenszel es asymptotic standard errors, confidence intervals, and tests for association and measures of es score confidence limits for odds es exact p-values, exact mid-p-values, and confidence intervals for many test statistics and ms by group processing, which enables you to obtain separate analyses on grouped s either raw data or cell count data to produce frequency and crosstabulation s a sas data set that contains the computed s a sas data set that corresponds to any output tically creates graphs by using ods further details, see freq fmm procedure fits statistical models to data for which the distribution of the a finite mixture of univariate distributions–that is, each response comes from one l random univariate distributions with unknown following are highlights of the fmm procedure's features:Model the component distributions in addition to the mixing finite mixture models by maximum likelihood or bayesian finite mixtures of regression and generalized linear the model effects for the mixing probabilities and their link overdispersed te multimodal or heavy-tailed zero-inflated or hurdle models to count data with excess regression models with complex error fy observations based on predicted component five different response equality and inequality constraints on model y the response variable by using either the response syntax or the events/trials ted model selection for homogeneous l the performance characteristics of the procedure (for example, the number of cpus, the number of threads for multithreading, and so on). Separate analyses on observations in a data set that contains observationwise statistics that are computed after fitting the a sas data set corresponding to any output tically create graphs by using ods further details, see fmm genmod procedure fits generalized linear models, as defined by nelder and wedderburn (1972). These include classical linear models with , logistic and probit models for binary data, and log-linear models for multinomial data.
Many other useful statistical be formulated as generalized linear models by the selection of an appropriate link function and response probability following are highlights of the genmod procedure's features:Provides the following built-in distributions and associated variance functions:Zero-inflated es the following built-in link functions:Complementary s you to define your own link functions or distributions through data mming statements used within the models to correlated responses by the gee m bayesian analysis for generalized linear ms exact logistic ms exact poisson s you to fit a sequence of models and to perform type i and type iii n each successive pair of es likelihood ratio statistics for user-defined es estimated values, standard errors, and confidence limits for sts and least squares es confidence intervals for model parameters based on either the hood function or asymptotic es an overdispersion diagnostic plot for zero-inflated ms by group processing, which enables you to obtain separate analyses on grouped s sas data sets that correspond to most output tically generates graphs by using ods further details, see genmod logistic procedure fits linear logistic regression models for discrete response data by the method of maximum can also perform conditional logistic regression for binary response data and exact logistic regression for binary and se data. The ordering of the response e a generalized r2 measure for the fitted sify binary response observations according to their predicted response linear hypotheses about the regression m exact tests of the parameters for the specified effects and optionally parameters and exact conditional y contrasts to compare several receiver operating characteristic a data set by using a previously fitted y units of change for continuous explanatory variables so that customized odds ratios can be m by group processing, which enables you to obtain separate analyses on grouped m weighted a data set for producing a receiver operating characteristic curve for each fitted a data set that contains the estimated response probabilities, residuals, and influence a data set that contains the estimated parameter vector and its estimated covariance a data set that corresponds to any output tically create graphs by using ods further details, see logistic probit procedure calculates maximum likelihood estimates of regression parameters and the natural (or threshold) for quantal response data from biological assays or other discrete event data. The inverse of the predicted probability (ipp) against a single continuous variable (dose variable) for the binomial the linear predictor (lpred) x'b against a single continuous variable (dose variable) for either the binomial model or the multinomial the predicted probability against a single continuous variable (dose variable) for both the binomial model and the multinomial es and compares least squares means (ls-means) of fixed es custom hypothesis tests among least squares ms a partitioned analysis of the ls-means for an m weighted s you to save the context and results of the statistical analysis in an item store, which can be processed with the plm s a sas data set that contains the parameter estimates and their estimated s a sas data set that contains the input data, the fitted probabilities, the linear prediction and the estimate of its standard s a sas data set that corresponds to any output ms by group processing, which enables you to obtain separate analyses on grouped tically creates graphs by using ods further details, see probit procedure.