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It uses the `restore` parameter, which points to an in-memory table created from a binary large object (BLOB) that contains a previously fitted regression model....
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The longToWide action reshapes a table containing thin records (long format) into a table containing wide records (wide format)....
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The 'id' parameter specifies the ID variables to copy from the input table to the output casOut table....
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You use the 'casOut' parameter to specify the settings for the output table, including its name and the caslib where it should be stored. For example: casOut={'name':'wide_table', 'caslib':'casuser'}....
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The 'inputs' parameter specifies the variables from the long-format table that will be transposed into new variables in the wide-format table....
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You can use the 'sum', 'mean', 'max', 'min', 'range', or 'nMiss' parameters to specify the variables for which you want to compute these statistics. The results are added as new variables to the output table....
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The 'groupBy' parameter specifies the variables to use for grouping results. Each unique combination of the groupBy variables will form a single row in the output wide-format table....
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The lpca action reduces the dimensionality of nominal variables by using a logistic principal component analysis (LPCA)....
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The 'table' parameter is required to specify the input data table. It identifies the table that contains the data to be analyzed....
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You use the 'dimensions' parameter to specify the desired number of reduced variables (principal components)....
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The 'nominals' parameter is used to specify the nominal variables that will be used in the training for the dimensionality reduction....
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You can save the model by using the 'saveState' parameter, which specifies an output data table to store the dimensionality reduction model....
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The 'output' parameter specifies the output data table that will contain the new, reduced variables (principal components) for the training data....
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The 'prefix' parameter allows you to specify a prefix that will be applied to the names of the newly created reduced variables in the output table. The default prefix is 'rv'....
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The maChart action produces uniformly weighted moving average charts. These charts are used to analyze the means of a process, where each point represents the average of the most recent 'n' subgroup means, with 'n' being the span of the moving average....
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The process standard deviation is estimated using the method specified in the `sMethod` parameter. You can choose from three methods: 'RMSDF' (a weighted root mean square estimate), 'SMVLUE' (a minimum variance linear unbiased estimate based on subgroup standard deviations), or 'SNOWEIGHT' (an unwei...
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The `span` parameter is a required integer that specifies the number of subgroups to use when calculating the moving average. Its value must be at least 1....
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The `limitN` parameter specifies a nominal sample size for the control limits. If you provide a value for `limitN`, the control limits are calculated for that fixed sample size. Subgroups with sample sizes that do not match this nominal size may be excluded from the analysis, depending on other para...
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By default, the maChart action requires subgroups to have a constant sample size. To include all subgroups in the analysis regardless of their sample size, you can set the `allN` parameter to TRUE. The control limits will then vary with the subgroup sample size....
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The `limitsTable` is an input parameter that specifies a CAS table containing pre-calculated control limit parameters. This allows you to apply existing control limits to your data. Conversely, the `outLimitsTable` is an output parameter that creates a new CAS table to save the control limits calcul...
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The marketattribution action is used to perform market analysis by using various market attribution models....
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The required parameters are 'table' to specify the input data, 'channel' to define the channel variable, 'section' to identify the observation section, and 'time' to set the temporal order of observations....
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The 'halflife' parameter specifies the half-life for the time-decay model. It has a default value of 1 and must be greater than 0....
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The action can generate three main output tables: 'outcontribution' for contribution results, 'outremoval' for removal effect results, and 'outtpm' for the transition probability matrix....
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The input data is specified using the 'table' parameter, which is a required CAS table containing the data for analysis....
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The 'nchannel' parameter specifies the number of channels to be used in the model. It defaults to 2 and must be at least 2....
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The `match` action is used for Data Management Matching, which involves grouping rows into clusters based on specified rules....
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The `algorithm` parameter specifies the algorithm to use for matching. The available options are 'AUTO', 'DISTRIBUTED', or 'SINGLE'. 'AUTO' is the default....
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Use the `inTable` parameter to specify the input data table. You need to provide the table name and optionally the caslib....
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The matching criteria are defined using the `matchRules` parameter. It requires a list of rules, where each rule specifies the set of columns to be used for matching rows....
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The `outTable` parameter specifies the output data table where the results, including the original columns and the new cluster IDs, are written. The `clusterId` parameter lets you name the column containing the cluster IDs, which defaults to 'cID'....
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Yes, you can use the `doNotCluster` parameter. Specify the name of a column in the input table that contains a Boolean flag. If the value for a row is 'true' or '1', it will be placed in its own separate cluster....
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By default, empty strings are treated as NULL values (`emptyStringIsNull` is TRUE). However, NULL values are not matched with each other (`nullValuesMatch` is FALSE). You can change these behaviors by setting these boolean parameters....
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Yes, the `columns` parameter allows you to specify a list of column names from the input table to be passed through to the output table. If this parameter is not specified, all input columns will be included in the output....
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The matchImages action matches a query image against a collection of images in a specified table to find similarities....
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You must use the 'queryImage' parameter, providing the path to the image you want to use as the basis for the search....
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The 'table' parameter is used to specify the input table that contains the image data to be searched....
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The action supports two methods specified via the 'methodOptions' parameter: "DESCRIPTORMATCH" for matching using keypoint descriptors, and "TEMPLATEMATCH" for matching by template....
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By setting the 'highlight' parameter to TRUE, the action will draw rectangles around the detected matches in the output images....
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Yes, you can use the 'threshold' parameter to specify a value for eliminating matches that have a low confidence score....
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The 'descType' parameter is used only with the "DESCRIPTORMATCH" method. It specifies the type of descriptor to be computed on the keypoints, with options like "BRISK" or "ORB"....
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The maxFlow action calculates the maximum flow of a graph. It is part of the Network Optimization action set, which provides tools for combinatorial and network flow optimization....
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optNetwork.maxFlow / , , , , , ...;...
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The maxFlow action uses two primary input tables: the 'links' table, which contains the graph link information, and the 'nodes' table, which contains the graph node information....
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The 'direction' parameter specifies whether to treat the input graph as directed or undirected. If 'DIRECTED', flow is from a source (tail) node to a sink (head) node. If 'UNDIRECTED', flow can be in either direction along a link....
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The maxFlow action returns several results, including: 'ProblemSummary' (a summary of the graph input), 'SolutionSummary' (a summary of the algorithm's solution), 'objective' (the total weight of the maximum network flow), and 'solutionStatus' (the solution status of the algorithm)....
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The mbanalysis action performs rule generation for market basket analysis....
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The required parameters are 'idVariable', which specifies the variable used to group the target variable into baskets, 'table', which specifies the input data table, and 'tgtVariable', which specifies the target nominal variable for the analysis....
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The 'conf' parameter specifies the minimum confidence for the rules, with a value ranging from 0 to 100. The default value is 50....
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You can use the 'lift' parameter to specify the minimum lift value necessary to generate a rule. The value can range from 0 to 100, with a default of 1....
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