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Yes, the 'hierarchy' parameter allows you to specify one or more hierarchy tables (up to five). If this parameter is omitted, the action performs a simple association analysis without a hierarchy....
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The action provides several output parameters: 'out' saves frequent item sets, 'outfreq' saves unique frequent items with their counts and support, 'outrule' saves the generated rules including support and lift, and 'saveState' saves the model state for future scoring....
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The mbcFit action performs model-based clustering using the Expectation-Maximization (EM) algorithm....
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The 'nClusters' parameter specifies the number of Gaussian clusters to be used in the model....
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You can use the 'covStruct' parameter to specify the covariance model. Options include various structures like 'EEE', 'EEV', 'VII', 'VVI', etc., which define constraints on the volume, shape, and orientation of the covariance matrices across clusters....
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The 'initMethod' parameter allows you to choose the initialization method. The available options are 'KMEANS' and 'RANDOM'....
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Yes, you can use the 'noise' parameter by setting it to 'Y' to include a noise cluster in the model to capture outliers or data points that do not fit well into any of the Gaussian clusters....
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The 'store' parameter allows you to save the resulting model in a CAS table as a binary large object (blob) for future scoring with the 'mbcScore' action....
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The 'criterion' parameter lets you specify the model selection criterion. Available options are 'AIC' (Akaike Information Criterion), 'AICC' (Corrected Akaike Information Criterion), 'BIC' (Bayesian Information Criterion), and 'LOGL' (Log-Likelihood)....
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The 'technique' parameter specifies the algorithm to use for clustering. You can choose 'EM' for the standard Expectation-Maximization algorithm or 'CEM' for the Classification Expectation-Maximization algorithm....
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The mbcScore action produces clustering weights using a previously stored model....
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The 'table' parameter is required to specify the input data table that you want to process....
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The 'restore' parameter is required. It specifies the item store that contains the model to be used for scoring....
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The 'casOut' parameter is a required parameter that specifies the settings for the output table where the results will be stored....
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You can set the 'allstats' parameter to TRUE to add all statistics to the output table....
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The 'copyVars' parameter allows you to specify a list of one or more variables to be copied from the input table to the output table. Keywords such as 'ALL' or 'ALL_NUMERIC' can also be used....
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The mca action reduces the dimensionality of nominal variables by using a multiple correspondence analysis (MCA)....
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Use the 'dimensions' parameter to specify the number of reduced variables to be generated....
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The 'table' parameter is required to specify the input data table for the action....
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You can use the 'saveState' parameter to specify an output data table where the model for dimensionality reduction will be saved....
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Yes, the 'prefix' parameter allows you to specify a prefix that will be applied to the names of the newly created reduced variables. The default prefix is "rv"....
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The 'nominals' parameter is used to specify the list of nominal variables to be used in the training process....
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The spc.mChart action produces charts of subgroup medians, which are used to analyze the central tendency of a process....
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The primary input table is specified using the 'table' parameter. This table must contain the process measurement variables and subgroup variables....
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Use the 'sMethod' parameter. Available methods include 'RNOWEIGHT' (unweighted estimate based on subgroup ranges), 'SNOWEIGHT' (unweighted estimate based on subgroup standard deviations), 'RMVLUE', 'SMVLUE', and 'RMSDF'....
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The estimation method for the process mean (central line) is specified with the 'medCentral' parameter. Options are 'AVGMED' (average of subgroup medians), 'MEDMED' (median of subgroup medians), or 'AVGMEAN' (average of subgroup means)....
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Yes, you can request tests for special causes using the 'primaryTests' parameter. You can enable specific tests like 'test1' for points outside control limits, 'test2' for nine points on one side of the center line, and others....
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The 'limitN' parameter specifies a nominal sample size for the control limits. This is particularly useful when subgroup sample sizes vary....
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You can use the 'outLimitsTable' parameter to specify an output CAS table to save the calculated control limits....
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Yes, by setting the 'exChart' parameter to TRUE, a control chart is included in the results only when exceptions (points outside the limits or failing a test) occur....
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The mdSummary action calculates multidimensional summaries of numeric variables. It is part of the Simple Analytics action set....
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The input data must contain at least one numeric variable. If this condition is not met, the action will return an error status and an empty dictionary as the result....
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The 'subSet' parameter allows you to specify the summary statistics to generate, which include: CSS (Corrected Sum of Squares), CV (Coefficient of Variation), KURTOSIS, MAX (Maximum), MEAN, MIN (Minimum), N (Number of Observations), NMISS (Number of Missing Values), PROBT (p-value for t-statistic), ...
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You can use the 'sets' parameter, which includes a 'groupBy' subparameter. This allows you to specify a list of variables to group the data by for the analysis. You can also use 'groupByFmts' to specify formats for these group-by variables....
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The 'groupByLimit' parameter specifies the maximum number of levels in a group-by set. When the server reaches this number, it stops processing to avoid creating excessively large result sets. This helps manage performance and resource usage....
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When 'includeMissing' is set to True, which is the default, missing values are included in the determination of group-by values....
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Yes, you can use the 'weight' parameter to specify a numeric variable whose values will be used to weight the values of the analysis variables....
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The metrics action displays the metrics for each action after it executes....
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The 'on' parameter specifies whether a brief set of action metrics is displayed....
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The 'on' parameter can be set to TRUE or FALSE. The default value is FALSE....
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session.metrics / on=TRUE;...
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The minCostFlow action calculates the minimum-cost network flow of a graph....
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The primary input tables are 'links', which contains the graph link information, and 'nodes', which contains the graph node information....
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You can use the 'direction' parameter. Set it to 'DIRECTED' for directed graphs where flow is from a source to a sink node, or 'UNDIRECTED' for undirected graphs where flow can be in either direction....
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The 'logLevel' parameter controls the amount of information displayed in the SAS log. It can be set to 'NONE' (no messages), 'BASIC' (brief summary), 'MODERATE' (detailed summary), or 'AGGRESSIVE' (most detailed summary)....
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The main output tables are 'outLinks', which contains the graph link information with algorithm results, and 'outNodes', which contains the graph node information with algorithm results....
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Set the 'multiLinks' parameter to TRUE to include multilinks when the input graph is read. This is the default behavior for this action....
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The minCut action is used to calculate the minimum cut of a graph, which involves finding a partition of the graph's nodes into two disjoint sets (a source set and a sink set) that minimizes the total weight of the links between them....
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To define a minimum cut problem, you must specify the source node using the 'source' parameter and the sink node using the 'sink' parameter. The graph itself is provided via the 'links' or 'graph' parameter....
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You can use the 'maxCuts' parameter to specify the maximum number of cuts that the minCut action should return. By default, it returns only one cut....
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