RStat: Release 1.2

RStat: Release 1.2

Ali
-
Zain Rahim, Strategic Product Manager

March 18, 2010

Agenda
:


Differentiators and Benefits


Review 1.2 Enhancements


Survival Analysis demo
-

Child welfare


Questions


RStat: Differentiators & Benefits



Based on

R
-
Project


Open Source


Maintained by world wide consortium of universities, scientists,
government funded research organizations, statisticians.


Over 2000 packages


RStat is a GUI to R


Intuitive guided approach to modeling


Simple model evaluation


Intended both for business analysts and advanced modelers


Single BI and Predictive Modeling Environment


Re
-
use metadata and queries


Perform data manipulation and sampling


Build scoring applications


Unique Deployment Method for Scoring Solutions



Scoring

models are built
directly into WF metadata


Deployment on any platform and operating system
-

Windows, Unix,
Linux, Z/OS, and i Series.


RStat 1.2 Enhancements:


New Modeling Technique:


Survival Analysis:


Two Techniques
–

Cox Regression and Parametric Time Regression


Cox Regression
–

risk scoring routine


Parametric regression
–

time scoring routine


What Survival Does and when to use


Survival analysis
encompasses a wide variety of methods for
analyzing the timing of events with censored data (Censoring: Nearly
every sample contains some cases that do not experience an event)


How to study the causes of


Births and Deaths


Marriages and Divorces


Arrests and Convictions


Job Changes and Promotions


Bankruptcies and Mergers









Wars and Revolutions


Residence Changes


Consumer Purchases


Adoption of Innovations


Hospitalizations






.

RStat 1.2 Enhancements
–

cont’d


New Scoring Routines:


Neural Network model with comprehensive output
–

Enables
users to compile NNET models into WebFOCUS functions for
creation of applications.


Transformation capabilities for scoring routines
–

Allows for data
manipulation within the RStat tool. Some methods are: Imputation,
Scaling, and Remapping


Enhanced statistical output:


Indicators to Regression models ANOVA table to show
significance
–

Enables users to determine the variables that are
significant to the model.


Performance and Usability optimization


Auto sampling for faster visualization of large data sets in the
KMeans model
–

Enables more optimized and efficient resource
usage to display Cluster model statistics and data plots.



Performance and Usability optimization


Model optimization
–

Allows only the variables used to create
the model to be included in the exported C file. [In RStat 1.1 all
variables selected by the user were included in the model]


Enhanced Log functionality
–

Allows users to create R
-
scripts for
use with other applications, such as a Dialogue Manager
application.


Process Cancellation capability
–

Allows users to cancel a long
running process from within RStat.


Special characters functionality
–

Enables efficient handling of
data with special characters.


Timestamp within the RConsole and Log Textview
–

Enables
users to view and match the log with any errors received,
thereby allowing for easier troubleshooting.


RStat 1.2 Enhancements
–

cont’d

Copyright 2007, Information Builders. Slide
7

Demo: Child Welfare Use Case



To identify the children who will stay in Child Welfare
programs, and at what age will the children leave the
programs
–

a time to event analysis

Foster Care Analytical Framework: Background and
Optimization Goals


Half a million children in foster care


Managed by county departments and the private
agencies who train families


It is a team effort to find a child a permanent
home


Severe consequence of bad foster care:


Youth who leave the system are more likely
to be homeless, incarcerated, unemployed,
and unskilled.



Foster Care Analytical Framework
: Goals &
Benefits :


Provide better understanding of the factors
that contribute to better foster care to all
parties involved in the process


Provide standardized analytic and reporting
system


Match children with better foster parents


Optimize child foster care duration





Survival Analysis
–

Child Welfare

Survival Analysis
–

Child Welfare

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Survival Analysis
–

Child Welfare



(cont’d)

Copyright 2007, Information Builders. Slide
22

Thank you!



"..if you are serious about statistics as a career, you need to become
familiar with R because it is the most powerful and flexible
language available, and may become the
lingua franca

of
statistical programming in the near future.“



Source
: "Statistics in a Nutshell" by Sarah Boslaugh
published by O'Reilly