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title Create R Models| Microsoft Docs
ms.custom
ms.date 05/18/2017
ms.prod sql-server-2016
ms.reviewer
ms.suite
ms.technology
r-services
ms.tgt_pltfrm
ms.topic article
applies_to
SQL Server 2016
dev_langs
R
ms.assetid a195d5e2-72e2-4dd6-bf43-947312e4a52a
caps.latest.revision 14
author jeannt
ms.author jeannt
manager jhubbard

Create R Models

Now that you have enriched the training data, it's time to analyze the data using linear regression. Linear models are an important tool in the world of predictive analytics, and the RevoScaleR package in [!INCLUDErsql_productname] includes a high-performance, scalable algorithm.

Create a Linear Regression Model

You'll create a simple linear model that estimates the credit card balance for the customers, using as independent variables the values in the gender and creditLine columns.

To do this, you'll use the rxLinMod function, which supports remote compute contexts.

  1. Create an R variable to store the completed model, and call the rxLinMod function, passing an appropriate formula.

    linModObj <- rxLinMod(balance ~ gender + creditLine,  data = sqlFraudDS)

  2. To view a summary of the results, you can call the standard R summary function on the model object.

    summary(linModObj)

You might think it peculiar that a plain R function like summary would work here, since in the previous step, you set the compute context to the server. However, even when the rxLinMod function uses the remote compute context to create the model, it also returns an object that contains the model to your local workstation, and stores it in the shared directory.

Therefore, you can run standard R commands against the model just as if it had been created using the "local" context.

Results

Linear Regression Results for: balance ~ gender + creditLineData: sqlFraudDS (RxSqlServerData Data Source)

Dependent variable(s): balance

Total independent variables: 4 (Including number dropped: 1)

Number of valid observations: 10000

Number of missing observations: 0

Coefficients: (1 not defined because of singularities)

Estimate Std. Error t value Pr(>|t|) (Intercept)

3253.575 71.194 45.700 2.22e-16

gender=Male -88.813 78.360 -1.133 0.257

gender=Female Dropped Dropped Dropped Dropped

creditLine 95.379 3.862 24.694 2.22e-16

Signif. codes: 0 0.001 0.01 ‘’ 0.05 ‘.’ 0.1 ‘ ’ 1*

Residual standard error: 3812 on 9997 degrees of freedom

Multiple R-squared: 0.05765

Adjusted R-squared: 0.05746

F-statistic: 305.8 on 2 and 9997 DF, p-value: < 2.2e-16

Condition number: 1.0184

Create a Logistic Regression Model

Now, you'll create a logistic regression model that indicates whether a particular customer is a fraud risk. You'll use the rxLogit function, included in the RevoScaleR package, which supports fitting of logistic regression models in remote compute contexts.

  1. Keep the compute context as is. You’ll also continue to use the same data source as well.

  2. Call the rxLogit function and pass the formula needed to define the model.

    logitObj <- rxLogit(fraudRisk ~ state + gender + cardholder + balance +      numTrans + numIntlTrans + creditLine, data = sqlFraudDS,      dropFirst = TRUE)

    Because it is a large model, containing 60 independent variables, including three dummy variables that are dropped, you might have to wait some time for the compute context to return the object.

    The reason the model is so large is that, in R and in the RevoScaleR package, every level of a categorical factor variable is automatically treated as a separate dummy variable.

  3. To view a summary of the returned model, call the R summary function.

    summary(logitObj)

Partial results

Logistic Regression Results for: fraudRisk ~ state + gender + cardholder + balance + numTrans + numIntlTrans + creditLine

Data: sqlFraudDS (RxSqlServerData Data Source)

Dependent variable(s): fraudRisk

Total independent variables: 60 (Including number dropped: 3)

Number of valid observations: 10000 -2

LogLikelihood: 2032.8699 (Residual deviance on 9943 degrees of freedom)

Coefficients:

Estimate Std. Error z value Pr(>|z|) (Intercept)

-8.627e+00 1.319e+00 -6.538 6.22e-11

state=AK Dropped Dropped Dropped Dropped

state=AL -1.043e+00 1.383e+00 -0.754 0.4511

(other states omitted)

gender=Male Dropped Dropped Dropped Dropped

gender=Female 7.226e-01 1.217e-01 5.936 2.92e-09

cardholder=Principal Dropped Dropped Dropped Dropped

cardholder=Secondary 5.635e-01 3.403e-01 1.656 0.0977

balance 3.962e-04 1.564e-05 25.335 2.22e-16

numTrans 4.950e-02 2.202e-03 22.477 2.22e-16

numIntlTrans 3.414e-02 5.318e-03 6.420 1.36e-10

creditLine 1.042e-01 4.705e-03 22.153 2.22e-16

---

Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Condition number of final variance-covariance matrix: 3997.308

Number of iterations: 15

Next Step

Score New Data

Previous Step

Visualize SQL Server Data using R