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PMML 4.4.1 - General Regression

Model XSD and Tag Description

<xs:element name="GeneralRegressionModel">
  <xs:complexType>
    <xs:sequence>
      <xs:element minOccurs="0" maxOccurs="unbounded" ref="Extension"/>
      <xs:element ref="MiningSchema"/>
      <xs:element minOccurs="0" ref="Output"/>
      <xs:element minOccurs="0" ref="ModelStats"/>
      <xs:element ref="ModelExplanation" minOccurs="0"/>
      <xs:element minOccurs="0" ref="Targets"/>
      <xs:element minOccurs="0" ref="LocalTransformations"/>
      <xs:element ref="ParameterList"/>
      <xs:element minOccurs="0" ref="FactorList"/>
      <xs:element minOccurs="0" ref="CovariateList"/>
      <xs:element ref="PPMatrix"/>
      <xs:element minOccurs="0" ref="PCovMatrix"/>
      <xs:element ref="ParamMatrix"/>
      <xs:element minOccurs="0" ref="EventValues"/>
      <xs:element minOccurs="0" ref="BaseCumHazardTables"/>
      <xs:element ref="ModelVerification" minOccurs="0"/>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="targetVariableName" type="FIELD-NAME"/>

    <xs:attribute name="modelType" use="required">
      <xs:simpleType>
        <xs:restriction base="xs:string">
          <xs:enumeration value="regression"/>
          <xs:enumeration value="generalLinear"/>
          <xs:enumeration value="multinomialLogistic"/>
          <xs:enumeration value="ordinalMultinomial"/>
          <xs:enumeration value="generalizedLinear"/>
          <xs:enumeration value="CoxRegression"/>
        </xs:restriction>
      </xs:simpleType>
    </xs:attribute>
    <xs:attribute name="modelName" type="xs:string"/>
    <xs:attribute name="functionName" type="MINING-FUNCTION" use="required"/>
    <xs:attribute name="algorithmName" type="xs:string"/>
    <xs:attribute name="targetReferenceCategory" type="xs:string"/>
    <xs:attribute name="cumulativeLink" type="CUMULATIVE-LINK-FUNCTION"/>
    <xs:attribute name="linkFunction" type="LINK-FUNCTION"/>
    <xs:attribute name="linkParameter" type="REAL-NUMBER"/>   
    <xs:attribute name="trialsVariable" type="FIELD-NAME"/>
    <xs:attribute name="trialsValue" type="INT-NUMBER"/>
    <xs:attribute name="distribution">
      <xs:simpleType>
        <xs:restriction base="xs:string">
          <xs:enumeration value="binomial"/>
          <xs:enumeration value="gamma"/>
          <xs:enumeration value="igauss"/>
          <xs:enumeration value="negbin"/>
          <xs:enumeration value="normal"/>
          <xs:enumeration value="poisson"/>
          <xs:enumeration value="tweedie"/>
        </xs:restriction>
      </xs:simpleType>
    </xs:attribute>
    <xs:attribute name="distParameter" type="REAL-NUMBER"/>   
    <xs:attribute name="offsetVariable" type="FIELD-NAME"/>
    <xs:attribute name="offsetValue" type="REAL-NUMBER"/>
    <xs:attribute name="modelDF" type="REAL-NUMBER"/>
    <xs:attribute name="endTimeVariable" type="FIELD-NAME"/>
    <xs:attribute name="startTimeVariable" type="FIELD-NAME"/>
    <xs:attribute name="subjectIDVariable" type="FIELD-NAME"/>
    <xs:attribute name="statusVariable" type="FIELD-NAME"/>
    <xs:attribute name="baselineStrataVariable" type="FIELD-NAME"/>
    <xs:attribute name="isScorable" type="xs:boolean" default="true"/>
  </xs:complexType>
</xs:element>

<xs:element name="ParameterList">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="Parameter" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
  </xs:complexType>
</xs:element>

<xs:element name="Parameter">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="name" type="xs:string" use="required"/>
    <xs:attribute name="label" type="xs:string"/>
    <xs:attribute name="referencePoint" type="REAL-NUMBER" default="0"/>
  </xs:complexType>
</xs:element>

<xs:element name="FactorList">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element minOccurs="0" maxOccurs="unbounded" ref="Predictor"/>
    </xs:sequence>
  </xs:complexType>
</xs:element>

<xs:element name="CovariateList">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element minOccurs="0" maxOccurs="unbounded" ref="Predictor"/>
    </xs:sequence>
  </xs:complexType>
</xs:element>

<xs:element name="Predictor">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="Categories" minOccurs="0" maxOccurs="1"/>
      <xs:element ref="Matrix" minOccurs="0"/>
    </xs:sequence>
    <xs:attribute name="name" type="FIELD-NAME" use="required"/>
    <xs:attribute name="contrastMatrixType" type="xs:string"/>
  </xs:complexType>
</xs:element>

<xs:element name="Categories">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="Category" minOccurs="1" maxOccurs="unbounded"/>
    </xs:sequence>
  </xs:complexType>
</xs:element>

<xs:element name="Category">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="value" type="xs:string" use="required"/>
  </xs:complexType>
</xs:element>

<xs:element name="PPMatrix">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="PPCell" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
  </xs:complexType>
</xs:element>

<xs:element name="PPCell">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="value" type="xs:string" use="required"/>
    <xs:attribute name="predictorName" type="FIELD-NAME" use="required"/>
    <xs:attribute name="parameterName" type="xs:string" use="required"/>
    <xs:attribute name="targetCategory" type="xs:string"/>
  </xs:complexType>
</xs:element>

<xs:element name="PCovMatrix">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element maxOccurs="unbounded" ref="PCovCell"/>
    </xs:sequence>
    <xs:attribute name="type">
      <xs:simpleType>
        <xs:restriction base="xs:string">
          <xs:enumeration value="model"/>
          <xs:enumeration value="robust"/>
        </xs:restriction>
      </xs:simpleType>
    </xs:attribute>
  </xs:complexType>
</xs:element>

<xs:element name="PCovCell">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="pRow" type="xs:string" use="required"/>
    <xs:attribute name="pCol" type="xs:string" use="required"/>
    <xs:attribute name="tRow" type="xs:string"/>
    <xs:attribute name="tCol" type="xs:string"/>
    <xs:attribute name="value" type="REAL-NUMBER" use="required"/>
    <xs:attribute name="targetCategory" type="xs:string"/>
  </xs:complexType>
</xs:element>

<xs:element name="ParamMatrix">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element ref="PCell" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
  </xs:complexType>
</xs:element>

<xs:element name="PCell">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="targetCategory" type="xs:string"/>
    <xs:attribute name="parameterName" type="xs:string" use="required"/>
    <xs:attribute name="beta" type="REAL-NUMBER" use="required"/>
    <xs:attribute name="df" type="INT-NUMBER"/>
  </xs:complexType>
</xs:element>

<xs:element name="BaseCumHazardTables">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:choice> 
        <xs:element maxOccurs="unbounded" ref="BaselineStratum"/>
        <xs:element maxOccurs="unbounded" ref="BaselineCell"/>
      </xs:choice> 
    </xs:sequence>
    <xs:attribute name="maxTime" type="REAL-NUMBER" use="optional"/>
  </xs:complexType>
</xs:element>

<xs:element name="BaselineStratum">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element minOccurs="0" maxOccurs="unbounded" ref="BaselineCell"/>
    </xs:sequence>
    <xs:attribute name="value" type="xs:string" use="required"/>
    <xs:attribute name="label" type="xs:string"/>
    <xs:attribute name="maxTime" type="REAL-NUMBER" use="required"/>
  </xs:complexType>
</xs:element>

<xs:element name="BaselineCell">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
    </xs:sequence>
    <xs:attribute name="time" type="REAL-NUMBER" use="required"/>
    <xs:attribute name="cumHazard" type="REAL-NUMBER" use="required"/>
  </xs:complexType>
</xs:element> 

<xs:element name="EventValues">
  <xs:complexType>
    <xs:sequence>
      <xs:element ref="Extension" minOccurs="0" maxOccurs="unbounded"/>
      <xs:element minOccurs="0" maxOccurs="unbounded" ref="Value"/>
      <xs:element minOccurs="0" maxOccurs="unbounded" ref="Interval"/>
    </xs:sequence>
  </xs:complexType>
</xs:element>

GeneralRegressionModel: marks the beginning of a general regression model. As the name says it, this is intended to support a multitude of regression models.

ParameterList: lists all Parameters. Each Parameter contains a required name and optional label. Parameter names should be unique within the model and as brief as possible (since Parameter names appear frequently in the document). The label, if present, is meant to give a hint on a Parameter's correlation with the Predictors. The optional attribute referencePoint is used in Cox regression models only and has a default value of 0. ParameterList can be empty only for CoxRegression models, for other models at least one Parameter should be present.

FactorList: list of factor (categorical predictor) names. Not present if this particular regression flavor does not support factors (ex. linear regression). If present, the list may or may not be empty. Each name in the list must match a DataField name or a DerivedField name. The factors must be categorical variables.

Predictor: describes a categorical (factor) or a continuous (covariate) predictor for the model. When describing a factor, it can optionally contain a list of categories and a contrast matrix. Such matrix describes the codings of categorical variables. If a categorical variable has n values, there will be n rows and n-1 or n columns in the matrix. The rows and columns correspond to the categories of the factor in the order listed in the Category element if it is present, otherwise in the order listed in the DataField or DerivedField element. If the Categories element is present and the corresponding DataField or DerivedField element has a list of valid categories, then the list in Categories should be a subset of that in DataField or DerivedField. A contrast matrix with n-1 columns helps to reduce the total number of parameters in the model. The use of such a matrix during scoring is described below.

CovariateList: list of covariate names. Will not be present when there is no covariate. Each name in the list must match a DataField name or a DerivedField name. The covariates will be treated as continuous variables.

targetVariableName: name of the target variable (also called response variable). This attribute has been deprecated since PMML 3.0. If present, it should match the name of the target MiningField.

modelType: specifies the type of regression model in use. This information will be used to select the appropriate mathematical formulas during scoring. The supported regression algorithms are listed.

modelName and algorithmName can have arbitrary strings describing the specific model.

functionName can only be classification or regression.

targetReferenceCategory can be used for specifying the reference category of the target variable in a multinomial classification model. Normally the reference category is the one from DataDictionary that does not appear in the ParamMatrix, but when several models are combined in one PMML file an explicit specification is needed.

cumulativeLink: specifies the type of cumulative link function to use when ordinalMultinomial model type is specified.

isScorable: This attribute indicates if the model is valid for scoring. If this attribute is true or if it is missing, then the model should be processed normally. However, if the attribute is false, then the model producer has indicated that this model is intended for information purposes only and should not be used to generate results. In order to be valid PMML, all required elements and attributes must be present, even for non-scoring models. For more details, see General Structure.

cumulativeLink: specifies the type of cumulative link function to use when ordinalMultinomial model type is specified.

CUMULATIVE-LINK-FUNCTION data type

The following definition is used for specifying a cumulative link function used in ordinalMultinomial model.

<xs:simpleType name="CUMULATIVE-LINK-FUNCTION">
  <xs:restriction base="xs:string">
    <xs:enumeration value="logit"/>
    <xs:enumeration value="probit"/>
    <xs:enumeration value="cloglog"/>
    <xs:enumeration value="loglog"/>
    <xs:enumeration value="cauchit"/>
  </xs:restriction>
</xs:simpleType>

Specific formulas are listed below in the scoring example.

linkFunction: specifies the type of link function to use when generalizedLinear model type is specified.

LINK-FUNCTION data type

The following definition is used for specifying a link function used in generalizedLinear model.

<xs:simpleType name="LINK-FUNCTION">
  <xs:restriction base="xs:string">
    <xs:enumeration value="cloglog"/>
    <xs:enumeration value="identity"/>
    <xs:enumeration value="log"/>
    <xs:enumeration value="logc"/>
    <xs:enumeration value="logit"/>
    <xs:enumeration value="loglog"/>
    <xs:enumeration value="negbin"/>
    <xs:enumeration value="oddspower"/>
    <xs:enumeration value="power"/>
    <xs:enumeration value="probit"/>
  </xs:restriction>
</xs:simpleType>

Specific formulas are listed below in the scoring example.

linkParameter: specifies an additional number the following link functions need: oddspower and power.

trialsVariable: specifies an additional variable used during scoring some generalizedLinear models (see the description of scoring procedure below). This attribute must refer to a DataField or a DerivedField. This attribute can only be used when the distribution is binomial.

trialsValue: a positive integer used during scoring some generalizedLinear models (see the description of scoring procedure below). At most one of the attributes trialsVariable and trialsValue can be present in a model. This attribute can only be used when the distribution is binomial.

distribution: the probability distribution of the dependent variable for generalizedLinear model may be specified as normal, binomial, gamma, inverse Gaussian, negative binomial, or Poisson. Note that binomial distribution can be used in two situtaions: either the target is categorical with two categories or a trialsVariable or trialsValue is specified.

distParameter: specifies an ancillary parameter value for the negative binomial distribution.

offsetVariable: if present, this variable is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField.

offsetValue: if present, this value is used during scoring generalizedLinear, ordinalMultinomial, or multinomialLogistic models. It works like a user-specified intercept (see the description of the scoring procedures below). At most one of the attributes offsetVariable and offsetValue can be present in a model.

modelDF: the value of degrees of freedom for the model. This value is needed for computing confidence intervals for predicted values.

endTimeVariable: if modelType is CoxRegression, this variable is required during scoring (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField containing a continuous variable.

startTimeVariable: if modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building. This attribute must refer to a DataField or a DerivedField containing a continuous variable.

subjectIDVariable: if modelType is CoxRegression, this variable is optional, it is not used during scoring but is an important piece of information about model building. This attribute must refer to a DataField or a DerivedField. Explicitly listing all categories of this variable is not recommended.

statusVariable: if modelType is CoxRegression, this variable is optional. This attribute must refer to a DataField or a DerivedField.

baselineStrataVariable: if modelType is CoxRegression, this variable is optional, if present it is used during scoring (see the description of scoring procedures below). This attribute must refer to a DataField or a DerivedField containing a categorical variable.

PPMatrix: Predictor-to-Parameter correlation matrix. It is a rectangular matrix having a column for each Predictor (factor or covariate) and a row for each Parameter. The matrix is represented as a sequence of cells, each cell containing a number representing the correlation between the Predictor and the Parameter. The cell values are computed as follows:

  • For each Predictor variable v and each Parameter p, the corresponding cell value is missing (empty) if there is no correlation between v and p.
  • If there is a correlation between a covariate Predictor and the Parameter, the cell value is set to the exponent that the covariate is raised to in the dependency expression. Example: assuming variable jobcat is a factor and work is a covariate, the Parameter [jobcat=professional] * work * work is correlated to the covariate work, and the number that should be entered in the cell is 2 because work is present at second power in the expression.
  • If there is a correlation between a factor variable and the Parameter, the cell value is set to the Predictor value that determines the correlation. Example: Assuming the categories of the factor variable jobcat are professional, clerical, skilled, unskilled, the cell in the matrix that corresponds to (jobcat, jobcat=skilled) has a value of skilled.

The empty cells are not required to be present in the exported model file. All cells determined to be missing from the xml file at model parsing will be assumed to be empty. Since empty cells make up a large chunk of the matrix, this will reduce the size of the exported model. Note that PPMatrix can be empty if a model is intercept-only or Cox regression without parameters.

Note the implied targetCategory attribute. This is permitted in order to allow usage of different PPMatrices for different response values in classification models. For multinomialLogistic model if any PPCell contains this attribute, the expectation is that for that particular response level, a full PPMatrix can be reconstructed from the PMML document. It is that matrix which will be used during scoring in order to get the probability (and other statistics) for the response level. By default, all target categories share the PPMatrix.

targetCategory attribute can thus be used to override the default for some or all target categories.

PPCell: cell in the PPMatrix. Knows its row name, column name, and information as described above.

PCovMatrix: matrix of Parameter estimate covariances. Made up of PCovCells, each of them being located via row information for Parameter name (pRow), row information for target variable value (tRow), column information for Parameter name (pCol) and column information for target variable value (tCol). Note that the matrix is symmetric with respect to the main diagonal (interchanging tRow and tCol together with pRow and pCol will not change the value). Therefore it is sufficient that only half of the matrix be exported. Attributes tRow and tCol are optional since they are not needed for linear regression models. This element has an optional attribute type that can take values model and robust. This attribute describes the way the covariance matrix was computed in generalizedLinear model. The robust option is also known as Huber-White or sandwich or HCCM.

ParamMatrix: Parameter matrix. A table containing the Parameter values along with associated statistics (degrees of freedom). One dimension has the target variable's categories, the other has the Parameter names. The table is represented by specifying each cell. There is no requirement for Parameter names other than that each name should uniquely identify one Parameter.

PCell: cell in the ParamMatrix. The optional targetCategory and required parameterName attributes determine the cell's location in the Parameter matrix. The information contained is: beta (actual Parameter value, required), and df (degrees of freedom, optional). For ordinalMultinomial model ParamMatrix specifies different values for the intercept parameter: one for each target category except one. Values for all other parameters are constant across all target variable values. For multinomialLogistic model ParamMatrix specifies parameter estimates for each target category except the reference category.

EventValues contains a list of Value and/or Interval elements that describe values of the status variable in Cox Regression model corresponding to the "Event". Please see example of Cox Regression below for explanation.

BaseCumHazardTables: Values of baseline cumulative hazard for Cox regression. In the presence of baseline strata variable there is a separate table for each baseline stratum value, otherwise only one table is needed. There is a value for maximum time for which data was available, and a set of pairs of time and cumulative hazard values, in BaselineCell elements.

BaselineCell: cell in the BaseCumHazardTables. The required time and cumHazard attributes contain all needed information.

BaselineStratum contains a set of BaselineCells plus the maximum time for one value of baseline strata variable. The optional label attribute makes it more human-readable.

General Regression Samples: Multinomial Logistic Example

Here is the information about the variables:

Name Type Number of categories Categories (numeric coding in parentheses)
JOBCAT Target 7 Clerical(1), Office trainee(2), Security officer(3), College trainee(4), Exempt employee(5), MBA trainee(6), and Technical(7)
SEX Factor 2 Male(0), and Female(1)
MINORITY Factor 2 Non-Minority(0), and Minority(1)
AGE Covariate
WORK Covariate

The Parameter estimates are displayed as follows:

Parameter Estimates
Employment Categorya B df
Clerical Intercept 26.836 1
[sex=0] -.719 1
[sex=1] 0b 0
[sex=0] * [minority=0] -19.214 1
[sex=0] * [minority=1] 0b 0
[sex=1] * [minority=0] -.114 1
[sex=1] * [minority=1] 0b 0
age -.133 1
work 7.885E-02 1
Office trainee Intercept 31.077 1
[sex=0] -0.869 1
[sex=1] 0b 0
[sex=0] * [minority=0] -18.990 1
[sex=0] * [minority=1] 0b 0
[sex=1] * [minority=0] 1.010 1
[sex=1] * [minority=1] 0b 0
age -.300 1
work .152 1
Security officer Intercept 6.836 1
[sex=0] 16.305 1
[sex=1] 0b 0
[sex=0] * [minority=0] -20.041 1
[sex=0] * [minority=1] 0b 0
[sex=1] * [minority=0] -.730 1
[sex=1] * [minority=1] 0b 0
age -.156 1
work .267 1
College trainee Intercept 8.816 1
[sex=0] 15.264 1
[sex=1] 0b 0
[sex=0] * [minority=0] -16.799 1
[sex=0] * [minority=1] 0b 0
[sex=1] * [minority=0] 16.480 1
[sex=1] * [minority=1] 0b 0
age -.133 1
work -.160 1
Exempt employee Intercept 5.862 1
[sex=0] 16.437 1
[sex=1] 0b 0
[sex=0] * [minority=0] -17.309 1
[sex=0] * [minority=1] 0b 0
[sex=1] * [minority=0] 15.888 1
[sex=1] * [minority=1] 0b 0
age -.105 1
work 6.914E-02 1
MBA trainee Intercept 6.495 1
[sex=0] 17.297 1
[sex=1] 0b 0
[sex=0] * [minority=0] -19.098 1
[sex=0] * [minority=1] 0b 0
[sex=1] * [minority=0] 16.841 1
[sex=1] * [minority=1] 0b 0
age -.141 1
work -5.058E-02 1
a. The reference category is: Technical.
b. This parameter is set to zero because it is redundant.

The PPMatrix is:

Parameter SEX MINORITY AGE WORK
Intercept
[SEX = 0] 0
[SEX = 1] 1
[MINORITY = 0]([SEX = 0]) 0 0
[MINORITY = 1]([SEX = 0]) 0 1
[MINORITY = 0]([SEX = 1]) 1 0
[MINORITY = 1]([SEX = 1]) 1 1
AGE 1
WORK 1

This Predictor-to-Parameter combinations mapping is the same for each target variable category. The corresponding XML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="dmg.org"/>
  <DataDictionary numberOfFields="5">
    <DataField name="jobcat" optype="categorical" dataType="double">
      <Value value="1" displayValue="Clerical"/>
      <Value value="2" displayValue="Office trainee"/>
      <Value value="3" displayValue="Security officer"/>
      <Value value="4" displayValue="College trainee"/>
      <Value value="5" displayValue="Exempt employee"/>
      <Value value="6" displayValue="MBA trainee"/>
      <Value value="7" displayValue="Technical"/>
    </DataField>
    <DataField name="minority" optype="categorical" dataType="double">
      <Value value="0" displayValue="Non-Minority"/>
      <Value value="1" displayValue="Minority"/>
    </DataField>
    <DataField name="sex" optype="categorical" dataType="double">
      <Value value="0" displayValue="Male"/>
      <Value value="1" displayValue="Female"/>  
    </DataField>
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="work" optype="continuous" dataType="double"/>
  </DataDictionary>

  <GeneralRegressionModel modelType="multinomialLogistic" functionName="classification" targetReferenceCategory="7">
     
    <MiningSchema>
      <MiningField name="jobcat" usageType="target"/>
      <MiningField name="minority" usageType="active"/>
      <MiningField name="sex" usageType="active"/>
      <MiningField name="age" usageType="active"/>
      <MiningField name="work" usageType="active"/>
    </MiningSchema>

    <ParameterList>
      <Parameter name="p0" label="Intercept"/>
      <Parameter name="p1" label="[SEX=0]"/>
      <Parameter name="p2" label="[SEX=1]"/>
      <Parameter name="p3" label="[MINORITY=0]([SEX=0])"/>
      <Parameter name="p4" label="[MINORITY=1]([SEX=0])"/>
      <Parameter name="p5" label="[MINORITY=0]([SEX=1])"/>
      <Parameter name="p6" label="[MINORITY=1]([SEX=1])"/>
      <Parameter name="p7" label="age"/>
      <Parameter name="p8" label="work"/>
    </ParameterList>

    <FactorList>
      <Predictor name="sex"/>
      <Predictor name="minority"/>
    </FactorList>

    <CovariateList>
      <Predictor name="age"/>
      <Predictor name="work"/>
    </CovariateList>

    <PPMatrix>
      <PPCell value="0" predictorName="sex" parameterName="p1"/>
      <PPCell value="1" predictorName="sex" parameterName="p2"/>
      <PPCell value="0" predictorName="sex" parameterName="p3"/>
      <PPCell value="0" predictorName="sex" parameterName="p4"/>
      <PPCell value="1" predictorName="sex" parameterName="p5"/>
      <PPCell value="1" predictorName="sex" parameterName="p6"/>
      <PPCell value="0" predictorName="minority" parameterName="p3"/>
      <PPCell value="1" predictorName="minority" parameterName="p4"/>
      <PPCell value="0" predictorName="minority" parameterName="p5"/>
      <PPCell value="1" predictorName="minority" parameterName="p6"/>
      <PPCell value="1" predictorName="age" parameterName="p7"/>
      <PPCell value="1" predictorName="work" parameterName="p8"/>
    </PPMatrix>

    <ParamMatrix>
      <PCell targetCategory="1" parameterName="p0" beta="26.836" df="1"/>
      <PCell targetCategory="1" parameterName="p1" beta="-.719" df="1"/>
      <PCell targetCategory="1" parameterName="p3" beta="-19.214" df="1"/>
      <PCell targetCategory="1" parameterName="p5" beta="-.114" df="1"/>
      <PCell targetCategory="1" parameterName="p7" beta="-.133" df="1"/>
      <PCell targetCategory="1" parameterName="p8" beta="7.885E-02" df="1"/>
      <PCell targetCategory="2" parameterName="p0" beta="31.077" df="1"/>
      <PCell targetCategory="2" parameterName="p1" beta="-.869" df="1"/>
      <PCell targetCategory="2" parameterName="p3" beta="-18.99" df="1"/>
      <PCell targetCategory="2" parameterName="p5" beta="1.01" df="1"/>
      <PCell targetCategory="2" parameterName="p7" beta="-.3" df="1"/>
      <PCell targetCategory="2" parameterName="p8" beta=".152" df="1"/>
      <PCell targetCategory="3" parameterName="p0" beta="6.836" df="1"/>
      <PCell targetCategory="3" parameterName="p1" beta="16.305" df="1"/>
      <PCell targetCategory="3" parameterName="p3" beta="-20.041" df="1"/>
      <PCell targetCategory="3" parameterName="p5" beta="-.73" df="1"/>
      <PCell targetCategory="3" parameterName="p7" beta="-.156" df="1"/>
      <PCell targetCategory="3" parameterName="p8" beta=".267" df="1"/>
      <PCell targetCategory="4" parameterName="p0" beta="8.816" df="1"/>
      <PCell targetCategory="4" parameterName="p1" beta="15.264" df="1"/>
      <PCell targetCategory="4" parameterName="p3" beta="-16.799" df="1"/>
      <PCell targetCategory="4" parameterName="p5" beta="16.48" df="1"/>
      <PCell targetCategory="4" parameterName="p7" beta="-.133" df="1"/>
      <PCell targetCategory="4" parameterName="p8" beta="-.16" df="1"/>
      <PCell targetCategory="5" parameterName="p0" beta="5.862" df="1"/>
      <PCell targetCategory="5" parameterName="p1" beta="16.437" df="1"/>
      <PCell targetCategory="5" parameterName="p3" beta="-17.309" df="1"/>
      <PCell targetCategory="5" parameterName="p5" beta="15.888" df="1"/>
      <PCell targetCategory="5" parameterName="p7" beta="-.105" df="1"/>
      <PCell targetCategory="5" parameterName="p8" beta="6.914E-02" df="1"/>
      <PCell targetCategory="6" parameterName="p0" beta="6.495" df="1"/>
      <PCell targetCategory="6" parameterName="p1" beta="17.297" df="1"/>
      <PCell targetCategory="6" parameterName="p3" beta="-19.098" df="1"/>
      <PCell targetCategory="6" parameterName="p5" beta="16.841" df="1"/>
      <PCell targetCategory="6" parameterName="p7" beta="-.141" df="1"/>
      <PCell targetCategory="6" parameterName="p8" beta="-5.058E-02" df="1"/>
    </ParamMatrix>

  </GeneralRegressionModel>

</PMML>

Scoring Algorithm

We will use the above example to illustrate the steps that should be followed in the scoring process. Say the following case (observation) must be scored:

         obs = (sex=1 minority=0 age=25 work=4)
  1. Do model file parsing. Reconstruct the PPMatrix and the Parameter matrix.
  2. To score a case, construct the vector x (of length equal to the number of Parameters in the model) as follows.
    • If row i of the PP correlation matrix is empty, that means the i-th parameter is an intercept, set xi = 1.
    • If row of the PP correlation matrix is nonempty and corresponds to a factor value or set of factor values, set xi to 1 if the case being scored matches this row, 0 if it does not.
    • If row i of the PP correlation matrix is nonempty and corresponds to a covariate c, with the entry r, then r is the multiplicity of the covariate c in the parameter, so set xi=cr using the value of c in the record.
    • If row i of the PP correlation matrix is nonempty and corresponds to a number of covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix row.
    • Finally, if row i of the PP correlation matrix is nonempty and corresponds to a combination of factors and covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix if the factor values in the record match those in the PP matrix row and 0 otherwise.
  3. Now for each response category (value of the target variable) j, let βj be the vector of Parameter estimates for that response category. (If k is the last response category, remember that by convention β k= 0.) Set r j= <x,βj > and s j= exp ri. The probability that our case falls into category j is then p j= sj/ (s1 + ... + s k).
  4. If you just want to assign each case to the category into which it has the highest probability of falling, it is not necessary to compute anything after rj; the category whose rj value is highest is the one you want. If you want to compute the actual probabilities (for instance, in order to know whether you are assigning a case to a 51% good or a 99% good category), we use a little dodge to avoid overflow. Namely, pj is the reciprocal of exp (r1-rj ) +... + exp (rk-rj). If ri-rj> 700 for any i, then the exponential will overflow; but in this case Pj is so small that we can set it to zero. Underflow in the denominator can be ignored since the term exp (rj-rj) ensures the denominator is at least 1.

General Regression Samples: General Linear Example

The information about the variables is the same as in the previous example, but now the target variable JOBCAT is considered to be continuous.

The Predictor-to-Parameter combinations mapping is the same as above. The corresponding XML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="dmg.org"/>
  <DataDictionary numberOfFields="5">
    <DataField name="jobcat" optype="continuous" dataType="double"/>
    <DataField name="minority" optype="categorical" dataType="double"/>
    <DataField name="sex" optype="categorical" dataType="double"/>
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="work" optype="continuous" dataType="double"/>
  </DataDictionary>

  <GeneralRegressionModel modelType="generalLinear" functionName="regression">

    <MiningSchema>
      <MiningField name="jobcat" usageType="target"/>
      <MiningField name="minority" usageType="active"/>
      <MiningField name="sex" usageType="active"/>
      <MiningField name="age" usageType="active"/>
      <MiningField name="work" usageType="active"/>
    </MiningSchema>

    <ParameterList>
      <Parameter name="p0" label="Intercept"/>
      <Parameter name="p1" label="[SEX=0]"/>
      <Parameter name="p2" label="[SEX=1]"/>
      <Parameter name="p3" label="[MINORITY=0]([SEX=0])"/>
      <Parameter name="p4" label="[MINORITY=1]([SEX=0])"/>
      <Parameter name="p5" label="[MINORITY=0]([SEX=1])"/>
      <Parameter name="p6" label="[MINORITY=1]([SEX=1])"/>
      <Parameter name="p7" label="age"/>
      <Parameter name="p8" label="work"/>
    </ParameterList>

    <FactorList>
      <Predictor name="sex"/>
      <Predictor name="minority"/>
    </FactorList>

    <CovariateList>
      <Predictor name="age"/>
      <Predictor name="work"/>
    </CovariateList>

    <PPMatrix>
      <PPCell value="0" predictorName="sex" parameterName="p1"/>
      <PPCell value="1" predictorName="sex" parameterName="p2"/>
      <PPCell value="0" predictorName="sex" parameterName="p3"/>
      <PPCell value="0" predictorName="sex" parameterName="p4"/>
      <PPCell value="1" predictorName="sex" parameterName="p5"/>
      <PPCell value="1" predictorName="sex" parameterName="p6"/>
      <PPCell value="0" predictorName="minority" parameterName="p3"/>
      <PPCell value="1" predictorName="minority" parameterName="p4"/>
      <PPCell value="0" predictorName="minority" parameterName="p5"/>
      <PPCell value="1" predictorName="minority" parameterName="p6"/>
      <PPCell value="1" predictorName="age" parameterName="p7"/>
      <PPCell value="1" predictorName="work" parameterName="p8"/>
    </PPMatrix>

    <ParamMatrix>
      <PCell parameterName="p0" beta="1.602" df="1"/>
      <PCell parameterName="p1" beta="0.580" df="1"/>
      <PCell parameterName="p3" beta="0.831" df="1"/>
      <PCell parameterName="p5" beta="0.429" df="1"/>
      <PCell parameterName="p7" beta="-0.012" df="1"/>
      <PCell parameterName="p8" beta="0.010" df="1"/>
    </ParamMatrix>

  </GeneralRegressionModel>

</PMML>

Scoring Algorithm

For this example the steps that should be followed in the scoring process are similar to the previous one but fewer. Say the following case (observation) must be scored:

         obs = (sex=1 minority=0 age=25 work=4)
  1. Do model file parsing. Reconstruct the PPMatrix and the Parameter matrix.
  2. To score a case, construct the vector x (of length equal to the number of Parameters in the model) as follows.
    • If row i of the PP correlation matrix is empty, set x i = 1.
    • If row of the PP correlation matrix is nonempty and corresponds to a factor value or set of factor values, set x i to 1 if the case being scored matches this row, 0 if it does not.
    • If row i of the PP correlation matrix is nonempty and corresponds to a covariate c, with the entry r, then r is the multiplicity of the covariate c in the parameter, so set xi=cr using the value of c in the record.
    • If row i of the PP correlation matrix is nonempty and corresponds to a number of covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix row.
    • Finally, if row i of the PP correlation matrix is nonempty and corresponds to a combination of factors and covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix if the factor values in the record match those in the PP matrix row and 0 otherwise.
  3. Now let β be the vector of Parameter estimates. The inner product r = <x,β> is the predicted value for the considered case.

General Regression Samples: Ordinal Multinomial Example

The information about the variables is the same as in the previous examples, but now the target variable JOBCAT is considered to be ordinal. The order is very important for ordinal fields. Therefore, a list of all valid values must be present in element DataField anytime attribute optype is set to ordinal. In this way, the sequence of values is determined by the order in which they appear in element DataField, from top to bottom.

The Predictor-to-Parameter combinations mapping is the same as above. The corresponding XML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="dmg.org"/>
  <DataDictionary numberOfFields="5">
    <DataField name="jobcat" optype="ordinal" dataType="integer">
      <Value value="1" displayValue="Clerical"/>
      <Value value="2" displayValue="Office trainee"/>
      <Value value="3" displayValue="Security officer"/>
      <Value value="4" displayValue="College trainee"/>
      <Value value="5" displayValue="Exempt employee"/>
      <Value value="6" displayValue="MBA trainee"/>
      <Value value="7" displayValue="Technical"/>
    </DataField>
    <DataField name="minority" optype="categorical" dataType="double">
      <Value value="0" displayValue="Non-Minority"/>
      <Value value="1" displayValue="Minority"/>
    </DataField>
    <DataField name="sex" optype="categorical" dataType="double">
      <Value value="0" displayValue="Male"/>
      <Value value="1" displayValue="Female"/>  
    </DataField> 
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="work" optype="continuous" dataType="double"/>
  </DataDictionary>

  <GeneralRegressionModel modelType="ordinalMultinomial" functionName="classification" cumulativeLink="logit">

    <MiningSchema>
      <MiningField name="jobcat" usageType="target"/>
      <MiningField name="minority" usageType="active"/>
      <MiningField name="sex" usageType="active"/>
      <MiningField name="age" usageType="active"/>
      <MiningField name="work" usageType="active"/>
    </MiningSchema>

    <ParameterList>
      <Parameter name="p0" label="Intercept"/>
      <Parameter name="p1" label="[SEX=0]"/>
      <Parameter name="p2" label="[SEX=1]"/>
      <Parameter name="p3" label="[MINORITY=0]([SEX=0])"/>
      <Parameter name="p4" label="[MINORITY=1]([SEX=0])"/>
      <Parameter name="p5" label="[MINORITY=0]([SEX=1])"/>
      <Parameter name="p6" label="[MINORITY=1]([SEX=1])"/>
      <Parameter name="p7" label="age"/>
      <Parameter name="p8" label="work"/>
    </ParameterList>

    <FactorList>
      <Predictor name="sex"/>
      <Predictor name="minority"/>
    </FactorList>

    <CovariateList>
      <Predictor name="age"/>
      <Predictor name="work"/>
    </CovariateList>

    <PPMatrix>
      <PPCell value="0" predictorName="sex" parameterName="p1"/>
      <PPCell value="1" predictorName="sex" parameterName="p2"/>
      <PPCell value="0" predictorName="sex" parameterName="p3"/>
      <PPCell value="0" predictorName="sex" parameterName="p4"/>
      <PPCell value="1" predictorName="sex" parameterName="p5"/>
      <PPCell value="1" predictorName="sex" parameterName="p6"/>
      <PPCell value="0" predictorName="minority" parameterName="p3"/>
      <PPCell value="1" predictorName="minority" parameterName="p4"/>
      <PPCell value="0" predictorName="minority" parameterName="p5"/>
      <PPCell value="1" predictorName="minority" parameterName="p6"/>
      <PPCell value="1" predictorName="age" parameterName="p7"/>
      <PPCell value="1" predictorName="work" parameterName="p8"/>
    </PPMatrix>

    <ParamMatrix>
      <PCell targetCategory="1" parameterName="p0" beta="-0.683" df="1"/>
      <PCell targetCategory="2" parameterName="p0" beta="0.723" df="1"/>
      <PCell targetCategory="3" parameterName="p0" beta="1.104" df="1"/>
      <PCell targetCategory="4" parameterName="p0" beta="1.922" df="1"/>
      <PCell targetCategory="5" parameterName="p0" beta="3.386" df="1"/>
      <PCell targetCategory="6" parameterName="p0" beta="4.006" df="1"/>
      <PCell parameterName="p1" beta="1.096" df="1"/>
      <PCell parameterName="p3" beta="0.957" df="1"/>
      <PCell parameterName="p5" beta="1.149" df="1"/>
      <PCell parameterName="p7" beta="-0.067" df="1"/>
      <PCell parameterName="p8" beta="0.060" df="1"/>
    </ParamMatrix>

  </GeneralRegressionModel>

</PMML>

Scoring Algorithm

For this example the steps that should be followed in the scoring process are somewhat similar to the first example but also the link function is used. Say the following case (observation) must be scored:

         obs = (sex=1 minority=0 age=25 work=4)
  1. Do model file parsing. Reconstruct the PPMatrix and the Parameter matrix.
  2. To score a case, construct the vector x (of length equal to the number of Parameters in the model) as follows.
    • If row i of the PP correlation matrix is empty, set x i= 1.
    • If row of the PP correlation matrix is nonempty and corresponds to a factor value or set of factor values, set x i to 1 if the case being scored matches this row, 0 if it does not.
    • If row i of the PP correlation matrix is nonempty and corresponds to a covariate c, with the entry r, then r is the multiplicity of the covariate c in the parameter, so set xi=cr using the value of c in the record.
    • If row i of the PP correlation matrix is nonempty and corresponds to a number of covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix row.
    • Finally, if row i of the PP correlation matrix is nonempty and corresponds to a combination of factors and covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix if the factor values in the record match those in the PP matrix row and 0 otherwise.
  3. Obtain the values of the offsetVariable or offsetValue a.
    Set
    • a = value from the observation information if offsetVariable is used
    • a = offsetValue from the XML file if offsetValue is used
    • a = 0 otherwise.
  4. When the target variable has only two categories, the inverse of link function transforms the value predicted by the regression equation into the corresponding probability of the first target category. If target variable is ordinal with more than two categories, a different intercept parameter value is specified by the model for each target category except the last. Inverse of the link function transforms value predicted by the regression equation with specified intercept value into the corresponding cumulative probability for the given category.

How to compute pj := probability of target=Valuej

For each response category (value of the target variable) j, let βj be the vector of Parameter estimates for that response category. (If k is the last response category, βk is not specified.) For the given case let <x,βj> be the result of evaluating the inner product just like in the multinomialLogistic model and yj = <x,βj> + a. Predicted probability for each category is then computed according to the following formulas:

p1 = F(y1)
pj = F(yj) - F(yj-1) , for 2 ≤ j < k
pk = 1 - F(yk-1)

Function F is an inverse of the specified link function:

logit, ordinal
inverse of logit function: F(y)= 1/(1+exp(-y)).

probit, ordinal
inverse of probit function: F(y)= integral(from -∞ to y)(1/sqrt(2*π))exp(-0.5*u*u)du.

cloglog, ordinal
inverse of cloglog function: F(y)= 1 - exp( -exp(y) ).

loglog, ordinal
inverse of loglog function: F(y)= exp( -exp(-y) ).

cauchit, ordinal
inverse of cauchit function: F(y)= 0.5 + (1/π) arctan(y).

General Regression Samples: Simple Regression Example

Only two continuous predictors are used in this example, and the target variable JOBCAT is considered to be continuous.

The Predictor-to-Parameter combinations mapping is trivial. The corresponding XML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="dmg.org"/>
  <DataDictionary numberOfFields="5">
    <DataField name="jobcat" optype="continuous" dataType="double"/>
    <DataField name="minority" optype="continuous" dataType="double"/>
    <DataField name="sex" optype="continuous" dataType="double"/>
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="work" optype="continuous" dataType="double"/>
  </DataDictionary>

  <GeneralRegressionModel modelType="regression" functionName="regression">

    <MiningSchema>
      <MiningField name="jobcat" usageType="target"/>
      <MiningField name="age" usageType="active"/>
      <MiningField name="work" usageType="active"/>
    </MiningSchema>

    <ParameterList>
      <Parameter name="p0" label="Intercept"/>
      <Parameter name="p1" label="age"/>
      <Parameter name="p2" label="work"/>
    </ParameterList>

    <CovariateList>
      <Predictor name="age"/>
      <Predictor name="work"/>
    </CovariateList>

    <PPMatrix>
      <PPCell value="1" predictorName="age" parameterName="p1"/>
      <PPCell value="1" predictorName="work" parameterName="p2"/>
    </PPMatrix>

    <ParamMatrix>
      <PCell parameterName="p0" beta="2.922" df="1"/>
      <PCell parameterName="p1" beta="-0.031" df="1"/>
      <PCell parameterName="p2" beta="0.034" df="1"/>
    </ParamMatrix>

  </GeneralRegressionModel>

</PMML>

Scoring Algorithm

For this example the steps that should be followed in the scoring process are somewhat similar to the general linear example but are even simpler. Say the following case (observation) must be scored:

         obs = (age=25 work=4)
  1. Do model file parsing. Reconstruct the PPMatrix and the Parameter matrix.
  2. To score a case, construct the vector x (of length equal to the number of Parameters in the model) as follows.
    • If row i of the PP correlation matrix is empty, set x i = 1.
    • If row i of the PP correlation matrix is nonempty and corresponds to a covariate c, the row should contain exactly one nonzero entry, in the column corresponding to the independent variable c. The value of this entry should be 1 since it is a linear model, so set xi=x (using the value of c which appears in this case).
  3. Now let β be the vector of Parameter estimates. The inner product r = <x,β> is the predicted value for the considered case.

General Regression Samples: Generalized Linear Model Example

The information about the variables is the same as in the previous examples, but now the target variable JOBCAT is considered to be continuous.

The Predictor-to-Parameter combinations mapping is the same as above. The corresponding XML model is:

<PMML xmlns="https://www.dmg.org/PMML-4_4" version="4.4">
  <Header copyright="dmg.org"/>
  <DataDictionary numberOfFields="5">
    <DataField name="jobcat" optype="continuous" dataType="double"/>
    <DataField name="minority" optype="categorical" dataType="double"/>
    <DataField name="sex" optype="categorical" dataType="double"/>
    <DataField name="age" optype="continuous" dataType="double"/>
    <DataField name="work" optype="continuous" dataType="double"/>
  </DataDictionary>

  <GeneralRegressionModel modelType="generalizedLinear" modelName="GZLM" functionName="regression" distribution="gamma" linkFunction="power" linkParameter="-1" offsetValue="3">

    <MiningSchema>
      <MiningField name="jobcat" usageType="target"/>
      <MiningField name="minority" usageType="active"/>
      <MiningField name="sex" usageType="active"/>
      <MiningField name="age" usageType="active"/>
      <MiningField name="work" usageType="active"/>
    </MiningSchema>

    <ParameterList>
      <Parameter name="p0" label="Intercept"/>
      <Parameter name="p1" label="[SEX=0]"/>
      <Parameter name="p2" label="[SEX=1]"/>
      <Parameter name="p3" label="[MINORITY=0]([SEX=0])"/>
      <Parameter name="p4" label="[MINORITY=1]([SEX=0])"/>
      <Parameter name="p5" label="[MINORITY=0]([SEX=1])"/>
      <Parameter name="p6" label="[MINORITY=1]([SEX=1])"/>
      <Parameter name="p7" label="age"/>
      <Parameter name="p8" label="work"/>
    </ParameterList>

    <FactorList>
      <Predictor name="sex"/>
      <Predictor name="minority"/>
    </FactorList>

    <CovariateList>
      <Predictor name="age"/>
      <Predictor name="work"/>
    </CovariateList>

    <PPMatrix>
      <PPCell value="0" predictorName="sex" parameterName="p1"/>
      <PPCell value="1" predictorName="sex" parameterName="p2"/>
      <PPCell value="0" predictorName="sex" parameterName="p3"/>
      <PPCell value="0" predictorName="sex" parameterName="p4"/>
      <PPCell value="1" predictorName="sex" parameterName="p5"/>
      <PPCell value="1" predictorName="sex" parameterName="p6"/>
      <PPCell value="0" predictorName="minority" parameterName="p3"/>
      <PPCell value="1" predictorName="minority" parameterName="p4"/>
      <PPCell value="0" predictorName="minority" parameterName="p5"/>
      <PPCell value="1" predictorName="minority" parameterName="p6"/>
      <PPCell value="1" predictorName="age" parameterName="p7"/>
      <PPCell value="1" predictorName="work" parameterName="p8"/>
    </PPMatrix>

    <ParamMatrix>
      <PCell parameterName="p0" beta="-2.30824444845005" df="1"/>
      <PCell parameterName="p1" beta="-0.268177596945098" df="1"/>
      <PCell parameterName="p3" beta="-0.169104566719988" df="1"/>
      <PCell parameterName="p5" beta="-0.219215962160056" df="1"/>
      <PCell parameterName="p7" beta="0.00427629446211706" df="1"/>
      <PCell parameterName="p8" beta="-0.00397117497757107" df="1"/>
    </ParamMatrix>

  </GeneralRegressionModel>

</PMML>

Scoring Algorithm

For this example the steps that should be followed in the scoring process are somewhat similar to the second example but also the link function is used. Say the following case (observation) must be scored:

         obs = (sex=1 minority=0 age=25 work=4)
  1. Do model file parsing. Reconstruct the PPMatrix and the Parameter matrix.
  2. To score a case, construct the vector x (of length equal to the number of Parameters in the model) as follows.
    • If row i of the PP correlation matrix is empty, set x i = 1.
    • If row of the PP correlation matrix is nonempty and corresponds to a factor value or set of factor values, set x i to 1 if the case being scored matches this row, 0 if it does not.
    • If row i of the PP correlation matrix is nonempty and corresponds to a covariate c, with the entry r, then r is the multiplicity of the covariate c in the parameter, so set xi=cr using the value of c in the record.
    • If row i of the PP correlation matrix is nonempty and corresponds to a number of covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix row.
    • Finally, if row i of the PP correlation matrix is nonempty and corresponds to a combination of factors and covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix if the factor values in the record match those in the PP matrix row and 0 otherwise.
  3. Obtain the values of the offsetVariable or offsetValue (a), TrialsVariable or TrialsValue (b), distParameter (c) and linkParameter (d).
    Set
    • a = value from the observation information if offsetVariable is used
    • a = offsetValue from the XML file if offsetValue is used
    • a = 0 otherwise
    Set
    • b = value from the observation information if TrialsVariable is used
    • b = trialsValue from the XML file if TrialsValue is used
    • b = 1 otherwise
    Set
    • c = distParameter from the XML file if link = negbin and distribution = negbin
    Set
    • d = linkParameter from the XML file if link = oddspower or power.
  4. Let β be the vector of Parameter estimates and <x,β> be the inner product of two vectors x and β. The predicted value for the considered case is F(<x,β> + a)*b,

    where function F is an inverse of the specified link function:

    cloglog
    inverse of cloglog function: F(y) = 1 - exp( -exp(y) ).

    identity
    inverse of identity function: F(y) = y.

    log
    inverse of log function: F(y) = exp(y).

    logc
    inverse of logc function: F(y) = 1 - exp(y).

    logit
    inverse of logit function: F(y)= 1/(1 + exp(-y)).

    loglog
    inverse of loglog function: F(y) = exp( -exp(-y) ).

    negbin(c)
    inverse of negbin(c) function: F(y) = 1/(c(exp(-y) - 1)).

    oddspower(d)
    inverse of oddspower(d) function:
    F(y) = 1/(1 + (1 + d*y)-1/d) if d!=0;
    F(y) = 1/(1 + exp(-y)) if d=0.

    power(d)
    inverse of power(d) function:
    F(y) = y1/d if d!=0;
    F(y) = exp(y) if d=0.
    Note that two popular (in open source software) link functions can be represented using power: inverse corresponds to d=-1 and sqrt corresponds to d=1/2.
    probit
    inverse of probit function: F(y) = integral(from -∞ to y)(1/sqrt(2*π))exp(-0.5*u*u)du.

    For a generalized linear model with binomial distribution and a categorical target the predicted probabilities are computed as follows:

    • For the target category found in PCells (called response category) compute p1=F(<x,β> + a) as described above.
    • If p1 < 0 then set p1 = 0
    • If p1 > 1 then set p1 = 1
    • Compute p2 = 1 - p1.
    • Now p1 and p2 are the predicted probabilities of the response category and reference category, respectively.

    The predicted category is selected based on the maximum of those two probabilities, with a tie broken using prior probabilities if present in Targets element.

    General Regression Samples: Example of a model with contrast matrices

    The following example illustrates the use of contrast matrices in a regression model. Here salCat is a target variable with two categories, "Low" and "High". There are two factors in the model and two covariates. The factor gender has two categories and uses "Simple" contrast matrix, while jobcat has three categories and "Helmert" contrast matrix. The model uses main effects and some interaction effects as indicated in PPMatrix and in parameter labels.

    <GeneralRegressionModel modelType="multinomialLogistic" modelName="contrastLogistic" functionName="classification" targetReferenceCategory="High">
  <MiningSchema>
    <MiningField name="salCat" usageType="target"/> 
    <MiningField name="gender" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="educ" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="jobcat" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="salbegin" usageType="active" missingValueTreatment="asIs"/> 
  </MiningSchema>
  <ParameterList>
    <Parameter name="P0000001" label="Constant"/> 
    <Parameter name="P0000002" label="gender(1)"/> 
    <Parameter name="P0000003" label="educ"/> 
    <Parameter name="P0000004" label="jobcat(1)"/> 
    <Parameter name="P0000005" label="jobcat(2)"/> 
    <Parameter name="P0000006" label="gender(1) by jobcat(1)"/> 
    <Parameter name="P0000007" label="gender(1) by jobcat(2)"/> 
    <Parameter name="P0000008" label="educ by gender(1) by salbegin"/>
  </ParameterList>
  <FactorList>
    <Predictor name="gender" contrastMatrixType="Simple">
      <Categories>
        <Category value="f"/>
        <Category value="m"/>
      </Categories>
      <Matrix nbRows="2" nbCols="1">
        <Array type="real" n="1">.5</Array> 
        <Array type="real" n="1">-.5</Array> 
      </Matrix>
    </Predictor>
    <Predictor name="jobcat" contrastMatrixType="Helmert">
      <Categories>
        <Category value="1"/>
        <Category value="2"/>
        <Category value="3"/>
      </Categories>
      <Matrix nbRows="3" nbCols="2">
        <Array type="real" n="2">.666666666667 0</Array> 
        <Array type="real" n="2">-.333333333333 .5</Array> 
        <Array type="real" n="2">-.333333333333 -.5</Array> 
      </Matrix>
    </Predictor>
  </FactorList>
  <CovariateList>
    <Predictor name="educ"/> 
    <Predictor name="salbegin"/> 
  </CovariateList>
  <PPMatrix>
    <PPCell value="f" predictorName="gender" parameterName="P0000002"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000003"/> 
    <PPCell value="1" predictorName="jobcat" parameterName="P0000004"/> 
    <PPCell value="2" predictorName="jobcat" parameterName="P0000005"/> 
    <PPCell value="f" predictorName="gender" parameterName="P0000006"/> 
    <PPCell value="1" predictorName="jobcat" parameterName="P0000006"/> 
    <PPCell value="f" predictorName="gender" parameterName="P0000007"/> 
    <PPCell value="2" predictorName="jobcat" parameterName="P0000007"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000008"/> 
    <PPCell value="f" predictorName="gender" parameterName="P0000008"/> 
    <PPCell value="1" predictorName="salbegin" parameterName="P0000008"/>
    
  </PPMatrix>
  <ParamMatrix>
    <PCell targetCategory="Low" parameterName="P0000001" beta="17.0599111512836" df="1"/> 
    <PCell targetCategory="Low" parameterName="P0000002" beta="-2.79578119817189" df="1"/> 
    <PCell targetCategory="Low" parameterName="P0000003" beta="-0.625739483585618" df="1"/> 
    <PCell targetCategory="Low" parameterName="P0000004" beta="-5.76523337984277" df="1"/> 
    <PCell targetCategory="Low" parameterName="P0000005" beta="17.743574615114" df="1"/> 
    <PCell targetCategory="Low" parameterName="P0000006" beta="0.421913613872923" df="1"/> 
    <PCell targetCategory="Low" parameterName="P0000007" beta="0" df="0"/> 
    <PCell targetCategory="Low" parameterName="P0000008" beta="1.1136356754678E-005" df="1"/>
  </ParamMatrix>
</GeneralRegressionModel>

    Scoring Algorithm

    For this example the following steps are needed to score the case

             obs = ( gender="f"  educ=19  jobcat=3  salbegin=45000 )
    Note that as indicated by the Categories element, the categories for factor gender have the following indices: "f" is 1, "m" is 2. For jobcat the categories are "1", "2", "3".
    1. Do model file parsing. Reconstruct the PPMatrix, the Parameter matrix, contrast matrices Cgender and Cjobcat for the factors.
    2. To score the above case, construct the vector x (of length equal to the number of Parameters in the model, 8) as follows.
      • If row i of the PP correlation matrix is empty, set x i = 1. Here 
                x1=1.
      • If row of the PP correlation matrix corresponds to exactly one factor value, set x i to the entry of the contrast matrix for this factor with row index defined by the factor category in the record and column index defined by the category in the PP matrix. For our example we get: 
                x2=Cgender(1,1)=0.5
        x4=Cjobcat(3,1)=-0.333333333333
        x5=Cjobcat(3,2)=-0.5
      • If row of the PP correlation matrix corresponds to a set of factor values, set xi to the product of the entries of contrast matrices as described above. In our example we get: 
                x6=Cgender(1,1)*Cjobcat(3,1)=0.5*(-0.333333333333)=-0.16666666666666
        x7=Cgender(1,1)*Cjobcat(3,2)=0.5*(-0.5)=-0.25
      • If row i of the PP correlation matrix corresponds to a covariate c, with the entry r, then r is the multiplicity of the covariate c in the parameter, so set xi=cr using the value of c in the record. In our example we get: 
                x3=educ=19
      • If row i of the PP correlation matrix corresponds to a number of covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix row.
      • Finally, if row i of the PP correlation matrix corresponds to a combination of factors and covariates, then set xi to be the product of covariate values (from the record) using their corresponding multiplicities found in the PP matrix times the product of contrast matrix entries as described above. In the example we get: 
                x8=educ*Cgender(1,1)*salbegin=19*0.5*45000=427500
    3. Now let β be the vector of Parameter estimates (for a specified target category, if applicable). The inner product r = <x,β> can be used as described before for various types of regression models. In our example the probability of target category "Low" will be computed as p("Low") = exp( r )/(1 + exp( r ) ).

    Cox Regression Model Explanation and Examples

    Cox proportional hazards model of survival is often used in real-life research studies in various industries including pharmaceutical and telecommunications. The idea is as follows: the data must contain an end time variable and a status variable, in addition to any number of predictor variables, and optionally a baseline strata variable, a start time variable, and a subject ID variable. Usually the status variable has certain values or intervals of values that are considered "an event", such as the death of a patient or a telephone customer switching to a competing carrier. The event is happening or not at the time indicated by the end time variable. Survival is the probability of the event not happening. Cumulative hazard is defined as the negative log of survival. The main assumption is that cumulative hazard for a case with predictors x at time t is computed as

       H( t | x ) = H0( t ) exp( x' * β ),
    where H0(t) is the baseline cumulative hazard at time t, vector β has regression parameter estimates. The probability of survival is 
       S( t | x ) = exp( -H( t | x ) ).
    The start time and subject ID variables provide an opportunity to represent time-dependent predictors that often appear in survival models. The baseline strata variable, if present, divides all data into several strata based on its categories, with separate baseline hazard values for each stratum. The same regression coefficients β are used in all strata.

    Note that all factors and covariates, as well as the end time variable and baseline strata variable, if present, should be listed as "active" in the MiningSchema. The event field can be listed as "target" since we are predicting the probability of its non-event category(ies), or it can be "supplementary" or omitted. Note that the main results of the model, the survival or cumulative hazard, do not directly correspond to any existing data field and hence are not reflected in the MiningSchema. The survival is considered the "predicted value" of a Cox Regression model, and and an OutputField for cumulative hazard can be defined by using a transformation.

    In the following examples variable childs is used as the end time variable, variable life is the status variable with value 1 corresponding to the "event", happy and educ are a factor and a covariate, respectively, and the model is using their main effects and their interaction term. The first example does not have a baseline strata variable, while the second one uses region for that.

    <GeneralRegressionModel modelType="CoxRegression" modelName="CSCox" functionName="regression" endTimeVariable="childs" statusVariable="life">
  <MiningSchema>
    <MiningField name="childs" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="happy" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="educ" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="life" usageType="target"/>
  </MiningSchema>
  <ParameterList>
    <Parameter name="P0000001" label="[happy=1]" referencePoint="0"/> 
    <Parameter name="P0000002" label="[happy=2]" referencePoint="0"/> 
    <Parameter name="P0000003" label="[happy=3]" referencePoint="0"/> 
    <Parameter name="P0000004" label="educ" referencePoint="12.85536159601"/> 
    <Parameter name="P0000005" label="[happy=1] * educ" referencePoint="0"/> 
    <Parameter name="P0000006" label="[happy=2] * educ" referencePoint="0"/> 
    <Parameter name="P0000007" label="[happy=3] * educ" referencePoint="0"/> 
  </ParameterList>
  <FactorList>
    <Predictor name="happy"/> 
  </FactorList>
  <CovariateList>
    <Predictor name="educ"/> 
  </CovariateList>
  <PPMatrix>
    <PPCell value="1" predictorName="happy" parameterName="P0000001"/> 
    <PPCell value="2" predictorName="happy" parameterName="P0000002"/> 
    <PPCell value="3" predictorName="happy" parameterName="P0000003"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000004"/> 
    <PPCell value="1" predictorName="happy" parameterName="P0000005"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000005"/> 
    <PPCell value="2" predictorName="happy" parameterName="P0000006"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000006"/> 
    <PPCell value="3" predictorName="happy" parameterName="P0000007"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000007"/> 
  </PPMatrix>
  <ParamMatrix>
    <PCell parameterName="P0000001" beta="2.19176500383392" df="1"/> 
    <PCell parameterName="P0000002" beta="0.839584538765938" df="1"/> 
    <PCell parameterName="P0000003" beta="0" df="0"/> 
    <PCell parameterName="P0000004" beta="0.207006511267958" df="1"/> 
    <PCell parameterName="P0000005" beta="-0.124788379173099" df="1"/> 
    <PCell parameterName="P0000006" beta="-0.0652692443310469" df="1"/> 
    <PCell parameterName="P0000007" beta="0" df="0"/> 
  </ParamMatrix>
  <EventValues>
    <Value value="1"/>
  </EventValues>
  <BaseCumHazardTables maxTime="8">
    <BaselineCell time="1" cumHazard="0.0805149154781295"/> 
    <BaselineCell time="2" cumHazard="0.208621561646413"/> 
    <BaselineCell time="3" cumHazard="0.367889107749672"/> 
    <BaselineCell time="4" cumHazard="0.610515527436034"/> 
    <BaselineCell time="5" cumHazard="0.782436645962723"/> 
    <BaselineCell time="6" cumHazard="0.898256334351415"/> 
    <BaselineCell time="7" cumHazard="1.34645277785058"/> 
    <BaselineCell time="8" cumHazard="1.92644296943848"/> 
  </BaseCumHazardTables>
</GeneralRegressionModel>

    <GeneralRegressionModel modelType="CoxRegression" modelName="CSCox" functionName="regression" endTimeVariable="childs" statusVariable="life" baselineStrataVariable="region">
  <MiningSchema>
    <MiningField name="childs" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="happy" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="educ" usageType="active" missingValueTreatment="asIs"/> 
    <MiningField name="region" usageType="active"/> 
    <MiningField name="life" usageType="target"/>
  </MiningSchema>
  <ParameterList>
    <Parameter name="P0000001" label="[happy=1]" referencePoint="0"/> 
    <Parameter name="P0000002" label="[happy=2]" referencePoint="0"/> 
    <Parameter name="P0000003" label="[happy=3]" referencePoint="0"/> 
    <Parameter name="P0000004" label="educ" referencePoint="12.85536159601"/> 
    <Parameter name="P0000005" label="[happy=1] * educ" referencePoint="0"/> 
    <Parameter name="P0000006" label="[happy=2] * educ" referencePoint="0"/> 
    <Parameter name="P0000007" label="[happy=3] * educ" referencePoint="0"/> 
  </ParameterList>
  <FactorList>
    <Predictor name="happy"/> 
  </FactorList>
  <CovariateList>
    <Predictor name="educ"/> 
  </CovariateList>
  <PPMatrix>
    <PPCell value="1" predictorName="happy" parameterName="P0000001"/> 
    <PPCell value="2" predictorName="happy" parameterName="P0000002"/> 
    <PPCell value="3" predictorName="happy" parameterName="P0000003"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000004"/> 
    <PPCell value="1" predictorName="happy" parameterName="P0000005"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000005"/> 
    <PPCell value="2" predictorName="happy" parameterName="P0000006"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000006"/> 
    <PPCell value="3" predictorName="happy" parameterName="P0000007"/> 
    <PPCell value="1" predictorName="educ" parameterName="P0000007"/> 
  </PPMatrix>
  <ParamMatrix>
    <PCell parameterName="P0000001" beta="1.96429877799117" df="1"/> 
    <PCell parameterName="P0000002" beta="0.487952271605177" df="1"/> 
    <PCell parameterName="P0000003" beta="0" df="0"/> 
    <PCell parameterName="P0000004" beta="0.186388616742954" df="1"/> 
    <PCell parameterName="P0000005" beta="-0.0964727062694649" df="1"/> 
    <PCell parameterName="P0000006" beta="-0.0257167272021955" df="1"/> 
    <PCell parameterName="P0000007" beta="0" df="0"/> 
  </ParamMatrix>
  <EventValues>
    <Value value="1"/>
  </EventValues>
  <BaseCumHazardTables>
    <BaselineStratum value="1" label="[region=North East]" maxTime="7">
      <BaselineCell time="1" cumHazard="0.0480764996657994"/> 
      <BaselineCell time="2" cumHazard="0.213530888447458"/> 
      <BaselineCell time="3" cumHazard="0.347177590555568"/> 
      <BaselineCell time="4" cumHazard="0.700088580976311"/> 
      <BaselineCell time="5" cumHazard="0.756857216338272"/> 
      <BaselineCell time="6" cumHazard="0.880125294006154"/> 
      <BaselineCell time="7" cumHazard="1.79261158114014"/> 
    </BaselineStratum>
    <BaselineStratum value="2" label="[region=South East]" maxTime="7">
      <BaselineCell time="1" cumHazard="0.104783416911293"/> 
      <BaselineCell time="2" cumHazard="0.149899368179306"/> 
      <BaselineCell time="3" cumHazard="0.344676164146026"/> 
      <BaselineCell time="4" cumHazard="0.447807317242553"/> 
      <BaselineCell time="5" cumHazard="0.602148704727296"/> 
      <BaselineCell time="6" cumHazard="0.996057753780737"/> 
    </BaselineStratum>
    <BaselineStratum value="3" label="[region=West]" maxTime="8">
      <BaselineCell time="1" cumHazard="0.0798136487904092"/> 
      <BaselineCell time="2" cumHazard="0.148350388305914"/> 
      <BaselineCell time="3" cumHazard="0.252784132000578"/> 
      <BaselineCell time="4" cumHazard="0.366288821244008"/> 
      <BaselineCell time="5" cumHazard="0.562653812085775"/> 
      <BaselineCell time="6" cumHazard="0.61271473319101"/> 
      <BaselineCell time="7" cumHazard="0.81698327174713"/> 
      <BaselineCell time="8" cumHazard="1.28475458929774"/> 
    </BaselineStratum>
  </BaseCumHazardTables>
</GeneralRegressionModel>

    Scoring Algorithm

    For this example some steps that should be followed in the scoring process are somewhat similar to the general linear example but also additional work is done to compute survival and cumulative hazard values using the equations presented above.

    1. Check if baseline strata variable is present. If it is, get its value from the case and check if there is a BaselineStratum element for that value. If not, return missing values as the result, else get maxTime from the BaselineStratum's attribute. In the absence of strata variable get maxTime from the BaseCumHazardTables element.
    2. Get the value of the end time variable from the case. If it is less than the minimum time in a BaselineCell in the previously chosen BaselineStratum or BaseCumHazardTables, then predicted survival is 1 and cumulative hazard is 0. If the time value is greater than maxTime, return missing value. Otherwise find the BaselineCell that has the largest time attribute value that is not greater than the time from the case. Extract baseline cumulative hazard value H0(t) from its attribute cumHazard.
    3. Compute the inner product r = <x,β> as described above for regression models.
    4. Compute the inner product of the reference point vector x0 (its values are located in Parameter elements) and the parameter estimates β: s = <x0,β>
    5. Finally, compute the cumulative hazard and survival: 
         H( t | x ) = H0( t ) exp( r - s ),
   S( t | x ) = exp( -H( t | x ) ).
    Note that Cox Regression model can be valid even when there are no parameters at all. In that case r=0, s=0, so the cumulative hazard is the same as baseline hazard, and survival is still computed by the formula presented above.
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