Frequently Asked Questions

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The 'controlStat' parameter specifies whether the control limits on the box chart are computed based on subgroup means ('MEAN') or subgroup medians ('MEDIAN'). The default value is 'MEAN'....
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Use the 'sMethod' parameter. It offers several methods: 'RMSDF' (weighted root mean square), 'RMVLUE' (minimum variance linear unbiased estimate from ranges), 'RNOWEIGHT' (unweighted estimate from ranges), 'SMVLUE' (minimum variance linear unbiased estimate from standard deviations), and 'SNOWEIGHT'...
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You can request tests for special causes using the 'primaryTests' parameter. The available tests are: Test 1 (one point beyond control limits), Test 2 (nine points in a row on one side of the center line), Test 3 (six points in a row steadily increasing or decreasing), Test 4 (fourteen points in a r...
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You can use the 'limitN' parameter to specify a nominal sample size for control limits. To include all subgroups regardless of their size, set 'allN' to True. To enable tests for special causes with varying subgroup sizes, set 'testNStd' to True....
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The 'specsTable' parameter is used to specify an input data table containing specification limits. When this table is provided, the action will compute and return process capability indices....
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The 'chartsTable' specifies the output data table for the charts summary, containing the summary statistics for each subgroup. The 'outLimitsTable' specifies the output data table for the control limits themselves....
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The boxPlot action is used to calculate quantiles, high and low whiskers, and outliers for statistical analysis, which are the fundamental components of a box plot....
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The 'table' parameter is required. It specifies the input CAS table that you want to analyze....
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You can specify the analysis variables using the 'inputs' parameter. This parameter takes a list of variables from the input table for which the box plot statistics will be calculated....
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The 'method' parameter allows you to choose between two algorithms: "ITERATIVE" and "EXACT". The default method is "ITERATIVE", which is generally faster. "EXACT" provides precise results but may be slower....
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Outlier handling is managed through several parameters. Setting 'outliers' to TRUE enables their calculation. The 'whiskerPercentile' parameter defines the range for whiskers (e.g., a value of 10 sets whiskers at the 10th and 90th percentiles). Additionally, 'nOutBins' can be used to bin the outlier...
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The 'pctlDef' parameter specifies one of five definitions for computing quantile statistics, as detailed in the UNIVARIATE procedure documentation. The default value is 6, which corresponds to an iterative process....
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The brTrain action extracts Boolean rules from text data. It is part of the Boolean Rule action set....
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The `docId` parameter specifies the variable in the input data table that contains the document ID. Its default value is "_document_"....
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The `docInfo` parameter specifies the information about the document table. It includes subparameters like `events`, `id` (which is required and specifies the document ID variable), `table` (the input data table with document info), `targets` (the target variables), and `targetType` (BINARY, MULTICL...
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The `gPositive` parameter specifies the minimum g-score for a positive term, while `gNegative` specifies the minimum g-score for a negative term to be considered for rule extraction. The default for both is 8....
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The `maxCandidates` parameter specifies the number of term candidates to be selected for each category during the rule creation process. The default value is 500....
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`maxTriesIn` specifies the k-in value for k-best search in the term ensemble process for creating rules (default 150). `maxTriesOut` specifies the k-out value for k-best search in the rule ensemble process for creating a rule set (default 50)....
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The `minSupports` parameter specifies the minimum number of documents in which a term must appear to be used for creating a rule. The default value is 3....
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The `mPositive` parameter specifies the m value for computing estimated precision for positive terms (default 2), and `mNegative` specifies the m value for computing estimated precision for negative terms (default 4)....
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The `termInfo` parameter specifies information about the terms table. It requires the `id` subparameter for the term ID, and can optionally include `label` for the term's text and `table` for the input data table containing term information....
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The `casOuts` parameter specifies the output data tables for the results. This can include `rules` (the generated rules), `ruleTerms` (the terms in each rule), and `candidateTerms` (the terms selected for rule creation)....
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The buildAutoComplete action builds an auto complete index table which is used for auto complete queries....
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The required parameters for the buildAutoComplete action are 'casOut' to specify the output table and 'index' to specify the input index table....
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The 'casOut' parameter specifies the name of the output table that will be used to store the resulting term list....
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The 'index' parameter specifies the name of the input index table that is used to provide the terms for the buildAutoComplete action....
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Yes, an example titled 'Producing a Terms List Using the buildAutoComplete Action' is available in the documentation....
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The buildModel action is used to create an empty Deep Learning model. This is the initial step before adding layers and training the model....
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The buildModel action can create three types of models, specified by the 'type' parameter: 'DNN' (Deep Neural Network) for a deep, fully connected neural network, 'CNN' (Convolutional Neural Network), and 'RNN' (Recurrent Neural Network). The default type is 'DNN'....
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The 'modelTable' parameter is required. It specifies the in-memory table where the newly created empty model will be stored....
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Yes, you can use the 'nThreads' parameter to specify the number of threads to be used for the operation. It accepts an integer value....
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The buildSurface action is used to build surfaces from 3-D biomedical images. It processes an input image table and generates two output tables: one containing the vertices of the surfaces and another containing the faces....
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The buildSurface action requires three mandatory parameters: 'images' to specify the input image table, 'outputFaces' to define the output table for surface faces, and 'outputVertices' to define the output table for surface vertices....
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You can define regions using either the 'intensities' parameter, which specifies the exact intensity values of the desired regions, or the 'thresholds' parameter. The 'thresholds' parameter allows you to specify a range with 'low' and 'high' values to define the regions of interest....
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You can control surface smoothing using the 'smoothing' parameter. It includes two sub-parameters: 'iterations', which sets the maximum number of smoothing iterations (default is 0), and 'relaxationFactor', which specifies the degree of smoothing in each iteration (a value between 0 and 1, with a de...
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The action generates two primary output tables. The table specified in 'outputVertices' contains the vertices that make up the surfaces. The table specified in 'outputFaces' contains the faces of the surfaces, which are typically defined by connecting the vertices....
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The buildSurface action is used to build surfaces from 3-D biomedical images....
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The required parameters are 'images' for the input image table, 'outputFaces' for the output table containing surface faces, and 'outputVertices' for the output table containing surface vertices....
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You can specify regions of interest using either the 'intensities' parameter, which takes a list of desired intensity values, or the 'thresholds' parameter, which allows you to define regions with low and high threshold values....
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The 'smoothing' parameter can be used to smooth the surface. It includes two sub-parameters: 'iterations' to define the maximum number of smoothing iterations (default is 0), and 'relaxationFactor' to set the degree of smoothing (default is 1, with a range from 0 to 1)....
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The 'outputFaces' parameter specifies the output table that will contain the surface faces generated by the action....
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The 'outputVertices' parameter is used to specify the output table that will store the surface vertices created during the process....
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The buildTermIndex action creates a term index table for significant terms....
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The required parameters are 'casOut', which specifies the output table to store the term list, and 'table', which specifies the input index table....
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The 'language' parameter specifies the language to use for the index field tokenizer. The default value is UNIVERSAL....
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The 'fields' parameter specifies an optional list of fields where term frequency should be counted....
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The 'tokenize' parameter specifies whether the index field is tokenized. By default, its value is FALSE....
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The caEffect action provides model-agnostic methods for estimating potential outcome means and causal effects of categorical treatments....
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The caEffect action offers several estimation methods: 'IPW' (Inverse Probability Weighting), 'REGADJ' (Regression Adjustment), 'AIPW' (Augmented Inverse Probability Weighting), and 'TMLE' (Targeted Maximum Likelihood Estimation)....
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'IPW' requires observed outcome values and predicted treatment probabilities. 'REGADJ' requires predicted counterfactual outcome values. 'AIPW' is a doubly robust method that requires observed outcomes, predicted treatment probabilities, and predicted counterfactual outcomes....
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