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Oracle® R Enterprise User's Guide
Release 1.3 for Windows, Linux, Solaris, and AIX

E36761-08
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6 Oracle R Enterprise Versions of R Models

Oracle R Enterprise includes several functions that create R models with data in Database tables.

These functions are available at this time:

This approach has several advantages, as described in ore.lm() and ore.stepwise() Advantages.

ore.lm()

ore.lm() performs least squares regression on data represented in an ore.frame object. The model creates a model matrix using the model.matrix method from the OREstats package. The model matrix and the response variable are then represented in SQL and passed to an in-database algorithm. The in-database algorithm estimates the model using an algorithm involving a block update QR decomposition with column pivoting. After the in-database algorithm estimates the coefficients, it does a second pass of the data to estimate the model-level statistics. Finally, the model is returned as an ore.lm object.

The implementation of ore.lm() and ore.stepwise() provides several advantages, as described in ore.lm() and ore.stepwise() Advantages.

ore.lm will not estimate the coefficient values for a set of collinear terms.

After the model is created, use summary to create a summary of the model.

For an example, see Linear Regression Example.

ore.lm() and ore.stepwise() Advantages

These are important advantages of the way that ore.lm() and ore.stepwise() are implemented:

  • Both algorithms provide accurate solutions using out-of-core QR factorization. QR factorization decomposes a matrix into an orthogonal matrix and a triangular matrix.

    QR-based estimates are often are substantially more accurate than alternative techniques.

    QR is an algorithm of choice for difficult rank-deficient models.

  • You can process data that does not fit into machine's memory, that is, out-of-core data. QR factors a matrix into two matrices, one of which fit into memory with he other stored on disk.

    ore.lm() and ore.stepwise() can solve data sets with more than one billion rows.

  • ore.lm() and ore.stepwise() allow fast implementations of forward, backward, and stepwise model selection techniques.

ore.neural has similar advantages.

Linear Regression Example

This example pusheslongley to a table and builds a regression model:

# longley consiste of employment statistics:
head(longley)
     GNP.deflator     GNP Unemployed Armed.Forces Population Year Employed
1947         83.0 234.289      235.6        159.0    107.608 1947   60.323
1948         88.5 259.426      232.5        145.6    108.632 1948   61.122
1949         88.2 258.054      368.2        161.6    109.773 1949   60.171
1950         89.5 284.599      335.1        165.0    110.929 1950   61.187
1951         96.2 328.975      209.9        309.9    112.075 1951   63.221
1952         98.1 346.999      193.2        359.4    113.270 1952   63.639
#Push longley to a table
LONGLEY <- ore.push(longley)
# Fit full model
  oreFit1 <- ore.lm(Employed ~ ., data = LONGLEY)
  summary(oreFit1)

For more information, see the R help associated with ore.lm invoked by help(ore.lm).

ore.stepwise()

ore.stepwise() performs stepwise least squares regression on data represented in an ore.frame object. The model creates a model matrix using the model.matrix method from the OREstats package. The model matrix and the response variable are then represented in SQL and passed to an in-database algorithm. The in-database algorithm estimates the model using an algorithm involving a block update QR decomposition with column pivoting. After the in-database algorithm estimates the coefficients, it does a second pass of the data to estimate the model-level statistics. Finally, the model is returned as an ore.stepwise object.

ore.stepwise() excludes collinear terms throughout the computation.

After the model is created, use summary to view a summary of the model.

For an example, see Stepwise Regression Example.

Stepwise Regression Example

This example pushes longley to a table and builds a stepwise model.

LONGLEY <- ore.push(longley)
 
 # Two stepwise alternatives
  oreStep1 <-
    ore.stepwise(Employed ~ .^2, data = LONGLEY, add.p = 0.1, drop.p = 0.1)
  oreStep2 <-
    step(ore.lm(Employed ~ 1, data = LONGLEY),
         scope = terms(Employed ~ .^2, data = LONGLEY))

For more information, see the R help associated with ore.lm invoked by help(ore.lm).

ore.neural()

Neural network models can be used to capture intricate nonlinear relationships between inputs and outputs, or to find patterns in data.

ore.neural() builds a single layer feedforward neural network on ore.frame data.

ore.neural()uses the Broyden–Fletcher–Goldfarb–Shanno (BFGS) method to solve the underlying unconstrained nonlinear optimization problem that results from fitting a neural network.

The output of ore.neural() is an object of type ore.neural.

For detailed information about parameters and output, see the R help for ore.neural(). For an example, see Neural Network Example.

Neural Network Example

This example builds a neural network with default values, including hidden size 1.

The longley data set consists of statistics related to employment. This example pushes longley to a table. Note that the example creates a model that uses a subset of longley and then predicts results for a different subset of longley.

trainData <- ore.push(longley[1:11, ])
testData <- ore.push(longley[12:16, ])

fit <- ore.neural('Employed ~ GNP + Population + Year', data = trainData)

ans <- predict(fit, newdata = testData)