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The new GAMPL procedure fits generalized additive models via penalized likelihood estimation. The ANOM, CUSUM, MACONTROL, and SHEWHART procedures are now capable of producing graphs that you can edit by using the ODS Graphics Editor. Support for large matrices on the Windows operating system (up to 231 elements, or more than 2 billion elements). The PACKAGE statement supports installing and using packages, which are ZIP files that contain source code, data sets, documentation, and sample programs. SAS Factory Miner is new in the 14.1 release and runs as an add-on to SAS Enterprise Miner.
Provides model templates that can be customized to include data preparation capabilities, variable selection, and various options for the predictive model used.
Identifies a champion model based on one of several statistical criteria for each segment that can easily be deployed in production environments, and generates complete scoring code for each model. The Text Parsing node now does multithreaded parsing (using PROC HPTMINE) for most languages.
You can now specify where scoring data sets are written by using the EM_TERM_LOC macro variable, which facilitates both deploying your text mining models and building stored processes. The concurrent FOR loop (the COFOR loop) in PROC OPTMODEL can run in distributed mode (requires SAS High-Performance Optimization).
PROC OPTMODEL adds a profiler that tracks the amount of time spent in problem generation, presolving, and various stages of the solution process.
PROC OPTNET enables parallel computing, provides faster graph data input, and adds enhancements to three of its algorithms.
The decomposition algorithm expands the range of constraint matrix structures that it can detect automatically. SAS Simulation Studio adds controls on the order of execution for ports and for blocks, controls on the allocation of resource, availability adjustments, and automatic launching of the local SAS server.
The new X13 procedure incorporates the X12 procedure in response to the US Census Bureau's inclusion of the X-12-ARIMA methodology in the X-13ARIMA-SEATS program. PROC X13 also adds numerous options, displays additional tables, and changes the default value of the MAXITER= option to 1,500. The COUNTREG procedure adds the TEST statement, three statements that enable you to include spatial effects in a model, and more Bayesian analysis features.
The HPCOUNTREG procedure adds the TEST statement and support for the Conway-Maxwell distribution. The HPPANEL procedure adds support for the between-groups estimator, between-time-periods estimator, and pooled OLS regression. The QLIM procedure adds the RANDOM statement, which enables you to estimate the random-intercept models, and more Bayesian analysis features. The SSM procedure adds the DEPLAG statement, which simplifies the specification of models that have lagged values of response variables in the observation equation.
The interface is now extensible via additional user-defined segmentation and modeling strategies. The HP Cluster node enables automatic selection of the number of clusters by using the ABC criterion. The HP SVM and HP Forest nodes can create an analytic store, which is a portable format of the model that can be used to score observations within a database. New default values for Leaf Size (1) and Leaf Fraction (0.00001) to improve model accuracy.
Options for the Nominal Target Criterion property are expanded to include Information Gain Ratio and CHAID. A new Use Input Once property controls whether an input can be used multiple times or at most once in a branch. The HPCOUNTREG procedure adds a TEST statement and support for the Conway-Maxwell Poisson distribution.

The HPPANEL procedure supports the between-groups estimator, the between-time-periods estimator, and pooled OLS regression. The new GAMPL procedure fits generalized additive models by penalized likelihood estimation. The HPGENSELECT procedure supports the LASSO method, BY-group processing, and the RESTRICT statement.
The HPPRINCOMP procedure adds the METHOD= option in the PROC HPPRINCOMP statement, which specifies which principal component extraction method to use. The HPSPLIT procedure supports MODEL and CLASS statements and cost-complexity pruning with k-fold cross validation as the default method of selecting the penalty parameter. Enhancements to the HPTMINE procedure enable you to select or ignore parts of speech, attributes, and entities, as well as to build a search index. The HP Text Miner node now uses PROC HPTMINE to perform topic rotation and to create the topic table. Eleven parsing languages have been added to the Language property in the HP Text Miner node. ODS Graphics output is produced by using templates that are written in the Graph Template Language.
The Model Registration Node enables you to register a model on a remote metadata server by specifying macro variable values in your project start-up code.
The HP Regression node produces a new variance inflation factor (VIF) table that can be used for multicollinearity detection. The new MVPDIAGNOSE procedure produces principal component score plots and process variable contribution plots that are used to investigate the causes of unusual variation in a process.
The CAPABILITY procedure has a number of new options for the CDFPLOT, COMPHISTOGRAM, HISTOGRAM, PPPLOT, PROBPLOT, and QQPLOT statements. The RELIABILITY procedure now supports EFFECTPLOT, ESTIMATE, LSMEANS, LSMESTIMATE, SLICE, STORE, and TEST statements. The SAS Enterprise Miner client can now be opened directly into a specific project or diagram, or from the most recent project and diagram. Batch code from the SAS Enterprise Miner client can be used more easily with input tables of different names and locations. The SAS Enterprise Miner SAS Code node for version 12.1 contains enhanced support for score code that contains SAS Procedure steps.
PMML scoring for the Decision Tree, Regression, Neural Network, and Clustering nodes has been promoted to production status.
The Interactive Grouping node user interface has been redesigned to provide improved usability, performance, and computational scalability.
The Scorecard node features an output variable that counts the number of adverse characteristics, and users can select named input variables for adverse characteristic reporting. The Gradient Boosting node now provides users with the capability to disable the H statistic calculation, resulting in improved run-time performance. The Time Series Data Mining nodes have been promoted from experimental to production status. The Decision Tree output displays have been enhanced to display variable precision values in the split branches and nodes. OPTMODEL, nonlinear multistart optimization, and the new decomposition-based algorithm for linear and mixed integer optimization. The OPTMODEL procedure adds a SUBMIT block, which enables you to run other SAS code (including calling other procedures) within PROC OPTMODEL. PROC QLIM now provides users with Bayesian estimation methods for most univariate models supported by the procedure. A variety of new model specification tests have been added to the PANEL and AUTOREG procedure. The new Text Rule Builder node enables you to do predictive modeling directly from the term-by-document matrix, thereby allowing user-assisted or “active” learning.

Improvements to previously existing text mining nodes include enhancements to the Text Filter node and viewer, the Text Topic node and viewer, and the Text Cluster node.
On the 10th of April, 1912, the RMS Titanic set out on its maiden voyage across the Atlantic Ocean carrying 2,223 passengers. The titanic dataset describes the survival status of 1 309 individual passengers on the Titanic.  Besides the survival status (0=No, 1=Yes) the data set contains the age of 1 046 passengers, their names, their gender, the class they were in (first, second or third) and the fare they had paid for their ticket in Pre-1970 British Pounds. You can learn more about the survival rates by building a decision tree on this data set in JMP. If you save the prediction formula (click the red triangle next to Partition for Survival and select Save Columns -> Save Prediction Formula).
You can calculate your survival chance by entering your gender and age in an empty row.  Your odds appear in the Survival Tolerant Predictor Column. I believe chance on survival should be much higher as a assume the dataset does not account for people staying on the ship out of free will.
Interesting point of view but I am not sure how to obtain data on the people staying on the ship out of free will? The blog content appearing on this site does not necessarily represent the opinions of SAS.
In case you did not know, SAS On-Demand is the *FREE* (as in free puppy, although occasionally as in free beer) offering from SAS.
Step 1: Go to TASKS in the top menu, select SURVIVAL ANALYSIS and then PROPORTIONAL HAZARDS, as shown below.
When your program runs, you get the pdf file as output, but if you look at the top of your screen, next to the output you can see three other tabs, for log, code and input data. Well, now you have SAS computing in the cloud while in the clouds, so that must be cloud-squared. 2015 ducati monster 796 super bike price specifications, 2015 ducati monster 796 super bike price specifications overview review all new motor cycle sport – he ducati monster traces back its origins back to when.
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British Airways unveiled a new corporate identity in 1997 which involved repainting its fleet with around 20 daring tailfin designs by world artists?
It contains two topics, ‘Positive Tone’ and ‘Negative Tone’, that can be used as User Topics in the Text Topic node. If you want to calculate the calculate the chance on survival than you should not account for the people who died on the ship while having the opportunity to save themselfs.
Presumably, if you charge people $12.95 for three hours to access your wireless system on the plane there are fewer people using it than if everyone can use it as long as they want for free, say, at a university. More specifically, let’s say I wanted to do a proportional hazards regression model using the PROC PHREG procedure. In this case, the value of 0 means there was no event (the patient survived to the end of the study).
Season and Trend information is now extracted and included in Time Series Data Mining results. The SAS Learning Post is where you'll find tutorials, tips and practical information to help you become a better SAS user. You can always enter the value that denotes censoring the box above that says Enter Custom Value and click ADD.

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