Releases of Oracle R Enterprise often contain new features. The features in the current release and in some previous releases are described in the following topics:
The following topics describe the changes in Oracle R Enterprise 1.4:
The following changes are in Oracle R Enterprise 1.4.1:
The ore.glm function now accepts offset terms in the model formula and the function can now be used to fit negative binomial and tweedie families of generalized linear models.
The ore.sync function has an additional optional argument, query, that creates an ore.frame object from an optimized SQL SELECT statement without creating a view in the database. You can use this argument to create a query even when you do not have the CREATE VIEW system privilege for the current schema.
The new global option for serialization, ore.envAsEmptyenv, specifies whether referenced environments in an object should be replaced with an empty environment during serialization to an Oracle Database. This option is used by the following functions:
ore.push, which for a list object accepts envAsEmptyenv as an optional argument
ore.save, which has envAsEmptyenv as a named argument
ore.doEval and the other embedded R execution functions, which accept ore.envAsEmptyenv as a control argument.
The default values of the above arguments are regulated by the global option ore.envAsEmptyenv, but by using the argument you can override the global option value for a function.
See Also:
The online help for the ore.push and ore.save functions
Other changes in this release are the following:
The arules and statmod packages from The Comprehensive R Archive Network (CRAN) are now included in the Oracle R Enterprise supporting packages. For information on the supporting packages, see Oracle R Enterprise Installation and Administration Guide.
The following topics describe the changes in Oracle R Enterprise 1.4:
The following changes are in Oracle R Enterprise 1.4:
Additions and improvements to data preparation functions:
The new factanal function performs factor analysis on a formula or an ore.frame object that contains numeric columns.
Both signatures of the princomp function support the scores, subset, and na.action arguments.
The new getXlevels function creates a list of factor levels that can be used in the xlev argument of a model.matrix call that involves an ore.frame object.
The new exploratory data analysis function ore.esm builds exponential smoothing models for time series data. The function builds a model using either the simple exponential smoothing method or the double exponential smoothing method. The function can preprocess the time series data with operations such as aggregation and the handling of missing values. See "Building Exponential Smoothing Models on Time Series Data".
Additions and improvements to the Oracle R Enterprise regression and neural network modeling functions:
The new ore.glm function provides methods for fitting generalized linear models, which include logistic regression, probit regression, and poisson regression. See "Building a Generalized Linear Model".
The ore.lm and ore.stepwise functions are no longer limited to a total of 1,000 columns when deriving columns in the model formula.
The ore.lm function now supports a weights argument for performing weighted least squares regression.
The anova function can now perform analysis of variance on an ore.lm object.
For the ore.stepwise function, the values for the direction argument have changed. The value "both" now prefers drops over adds. The new direction argument value "alternate" has the previous meaning of the "both" value.
The ore.neural function has several new arguments.
Additions and improvements to the Oracle Data Mining model algorithm functions:
The new ore.odmAssocRules function, which builds an Oracle Data Mining association model using the apriori algorithm. See "Building an Association Rules Model".
The new ore.odmNMF function, which builds an Oracle Data Mining model for feature extraction using the Non-Negative Matrix Factorization (NMF) algorithm. See "Building a Non-Negative Matrix Factorization Model".
The new ore.odmOC function, which builds an Oracle Data Mining model for clustering using the Orthogonal Partitioning Cluster (O-Cluster) algorithm. See "Building an Orthogonal Partitioning Cluster Model".
An additional global option for Oracle R Enterprise, ore.parallel. See "Oracle R Enterprise Global Options".
The following topics describe the changes in Oracle R Enterprise 1.3:
The new features in Oracle R Enterprise 1.3 are the following:
Predicting with R models using in-database data with the OREpredict package
Ordering and indexing with row.names<-
Predicting with Oracle Data Mining models using the OREodm package
Saving and managing R objects in the database
Date and time data types
Sampling and partitioning
Long names for columns
Automatically connecting to an Oracle Database instance in embedded R scripts
Building an R neural network using in-database data with the ore.neural function
Other changes in this release are the following:
Installation and administration information has moved from this manual to Oracle R Enterprise Installation and Administration Guide. New features related to installation and administration are described in that book.
The new features in Oracle R Enterprise 1.1 are the following:
Support for additional operating systems:
Oracle R Distribution and Oracle R Enterprise are now supported IBM AIX 5.3 and higher and on 10 and higher for both 64-bit SPARC and 64-bit x386 (Intel) processors.
The Oracle R Enterprise Server now runs on 64-bit and 32-bit Windows operating systems.
Improved mathematics libraries in R:
You can now use the improved Oracle R Distribution with support for dynamically picking up either the Intel Math Kernel Library (MKL) or the AMD Core Math Library (ACML) with Oracle R Enterprise.
On Solaris, Oracle R Distribution dynamically links with Oracle SUN performance library for high speed BLAS and LAPACK operations.
Support for Oracle Wallet enables R scripts to no longer need to have database authentication credentials in clear text. Oracle R Enterprise is integrated with Oracle Wallet for that purpose.
Improved installation scripts provide more prerequisite checks and detailed error messages. Error messages provide specific instructions on remedial actions.