將表示多項Logistic迴歸的PMML轉換回R係數

我最近的任務是將PMML解析回R模型。（我已經進行了大量搜索，並且沒有爲您完成此轉換的庫。）我試圖將包含多項Logistic迴歸的PMML轉換回R模型，但我不知道如何轉換任何保留在PMML文檔中的係數與R模型保存的係數相關。將表示多項Logistic迴歸的PMML轉換回R係數

的PMML低於：

<?xml version="1.0"?> 
<PMML version="4.2" xmlns="http://www.dmg.org/PMML-4_2" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.dmg.org/PMML-4_2 http://www.dmg.org/v4-2/pmml-4-2.xsd"> 
<Header copyright="Copyright (c) 2014 hlin117" description="Generalized Linear Regression Model"> 
    <Extension name="user" value="hlin117" extender="Rattle/PMML"/> 
    <Application name="Rattle/PMML" version="1.4"/> 
    <Timestamp>2014-06-23 13:04:17</Timestamp> 
</Header> 
<DataDictionary numberOfFields="13"> 
    <DataField name="audit.train$TARGET_Adjusted" optype="continuous" dataType="double"/> 
    <DataField name="ID" optype="continuous" dataType="double"/> 
    <DataField name="Age" optype="continuous" dataType="double"/> 
    <DataField name="Employment" optype="categorical" dataType="string"> 
    <Value value="Consultant"/> 
    <Value value="Private"/> 
    <Value value="PSFederal"/> 
    <Value value="PSLocal"/> 
    <Value value="PSState"/> 
    <Value value="SelfEmp"/> 
    <Value value="Volunteer"/> 
    </DataField> 
    <DataField name="Education" optype="categorical" dataType="string"> 
    <Value value="Associate"/> 
    <Value value="Bachelor"/> 
    <Value value="College"/> 
    <Value value="Doctorate"/> 
    <Value value="HSgrad"/> 
    <Value value="Master"/> 
    <Value value="Preschool"/> 
    <Value value="Professional"/> 
    <Value value="Vocational"/> 
    <Value value="Yr10"/> 
    <Value value="Yr11"/> 
    <Value value="Yr12"/> 
    <Value value="Yr1t4"/> 
    <Value value="Yr5t6"/> 
    <Value value="Yr7t8"/> 
    <Value value="Yr9"/> 
    </DataField> 
    <DataField name="Marital" optype="categorical" dataType="string"> 
    <Value value="Absent"/> 
    <Value value="Divorced"/> 
    <Value value="Married"/> 
    <Value value="Married-spouse-absent"/> 
    <Value value="Unmarried"/> 
    <Value value="Widowed"/> 
    </DataField> 
    <DataField name="Occupation" optype="categorical" dataType="string"> 
    <Value value="Cleaner"/> 
    <Value value="Clerical"/> 
    <Value value="Executive"/> 
    <Value value="Farming"/> 
    <Value value="Home"/> 
    <Value value="Machinist"/> 
    <Value value="Military"/> 
    <Value value="Professional"/> 
    <Value value="Protective"/> 
    <Value value="Repair"/> 
    <Value value="Sales"/> 
    <Value value="Service"/> 
    <Value value="Support"/> 
    <Value value="Transport"/> 
    </DataField> 
    <DataField name="Income" optype="continuous" dataType="double"/> 
    <DataField name="Gender" optype="categorical" dataType="string"> 
    <Value value="Female"/> 
    <Value value="Male"/> 
    </DataField> 
    <DataField name="Deductions" optype="continuous" dataType="double"/> 
    <DataField name="Hours" optype="continuous" dataType="double"/> 
    <DataField name="IGNORE_Accounts" optype="categorical" dataType="string"> 
    <Value value="Canada"/> 
    <Value value="China"/> 
    <Value value="Columbia"/> 
    <Value value="Cuba"/> 
    <Value value="Ecuador"/> 
    <Value value="England"/> 
    <Value value="Fiji"/> 
    <Value value="Germany"/> 
    <Value value="Greece"/> 
    <Value value="Guatemala"/> 
    <Value value="Hong"/> 
    <Value value="Hungary"/> 
    <Value value="India"/> 
    <Value value="Indonesia"/> 
    <Value value="Iran"/> 
    <Value value="Ireland"/> 
    <Value value="Italy"/> 
    <Value value="Jamaica"/> 
    <Value value="Japan"/> 
    <Value value="Malaysia"/> 
    <Value value="Mexico"/> 
    <Value value="NewZealand"/> 
    <Value value="Nicaragua"/> 
    <Value value="Philippines"/> 
    <Value value="Poland"/> 
    <Value value="Portugal"/> 
    <Value value="Scotland"/> 
    <Value value="Singapore"/> 
    <Value value="Taiwan"/> 
    <Value value="UnitedStates"/> 
    <Value value="Vietnam"/> 
    <Value value="Yugoslavia"/> 
    </DataField> 
    <DataField name="RISK_Adjustment" optype="continuous" dataType="double"/> 
</DataDictionary> 
<GeneralRegressionModel modelName="General_Regression_Model" modelType="generalizedLinear" functionName="regression" algorithmName="glm" distribution="binomial" linkFunction="logit"> 
    <MiningSchema> 
    <MiningField name="audit.train$TARGET_Adjusted" usageType="predicted"/> 
    <MiningField name="ID" usageType="active"/> 
    <MiningField name="Age" usageType="active"/> 
    <MiningField name="Employment" usageType="active"/> 
    <MiningField name="Education" usageType="active"/> 
    <MiningField name="Marital" usageType="active"/> 
    <MiningField name="Occupation" usageType="active"/> 
    <MiningField name="Income" usageType="active"/> 
    <MiningField name="Gender" usageType="active"/> 
    <MiningField name="Deductions" usageType="active"/> 
    <MiningField name="Hours" usageType="active"/> 
    <MiningField name="IGNORE_Accounts" usageType="active"/> 
    <MiningField name="RISK_Adjustment" usageType="active"/> 
    </MiningSchema> 
    <Output> 
    <OutputField name="Predicted_audit.train$TARGET_Adjusted" feature="predictedValue"/> 
    </Output> 
    <ParameterList> 
    <Parameter name="p0" label="(Intercept)"/> 
    <Parameter name="p1" label="ID"/> 
    <Parameter name="p2" label="Age"/> 
    <Parameter name="p3" label="EmploymentPrivate"/> 
    <Parameter name="p4" label="EmploymentPSFederal"/> 
    <Parameter name="p5" label="EmploymentPSLocal"/> 
    <Parameter name="p6" label="EmploymentPSState"/> 
    <Parameter name="p7" label="EmploymentSelfEmp"/> 
    <Parameter name="p8" label="EmploymentVolunteer"/> 
    <Parameter name="p9" label="EducationBachelor"/> 
    <Parameter name="p10" label="EducationCollege"/> 
    <Parameter name="p11" label="EducationDoctorate"/> 
    <Parameter name="p12" label="EducationHSgrad"/> 
    <Parameter name="p13" label="EducationMaster"/> 
    <Parameter name="p14" label="EducationPreschool"/> 
    <Parameter name="p15" label="EducationProfessional"/> 
    <Parameter name="p16" label="EducationVocational"/> 
    <Parameter name="p17" label="EducationYr10"/> 
    <Parameter name="p18" label="EducationYr11"/> 
    <Parameter name="p19" label="EducationYr12"/> 
    <Parameter name="p20" label="EducationYr1t4"/> 
    <Parameter name="p21" label="EducationYr5t6"/> 
    <Parameter name="p22" label="EducationYr7t8"/> 
    <Parameter name="p23" label="EducationYr9"/> 
    <Parameter name="p24" label="MaritalDivorced"/> 
    <Parameter name="p25" label="MaritalMarried"/> 
    <Parameter name="p26" label="MaritalMarried-spouse-absent"/> 
    <Parameter name="p27" label="MaritalUnmarried"/> 
    <Parameter name="p28" label="MaritalWidowed"/> 
    <Parameter name="p29" label="OccupationClerical"/> 
    <Parameter name="p30" label="OccupationExecutive"/> 
    <Parameter name="p31" label="OccupationFarming"/> 
    <Parameter name="p32" label="OccupationHome"/> 
    <Parameter name="p33" label="OccupationMachinist"/> 
    <Parameter name="p34" label="OccupationMilitary"/> 
    <Parameter name="p35" label="OccupationProfessional"/> 
    <Parameter name="p36" label="OccupationProtective"/> 
    <Parameter name="p37" label="OccupationRepair"/> 
    <Parameter name="p38" label="OccupationSales"/> 
    <Parameter name="p39" label="OccupationService"/> 
    <Parameter name="p40" label="OccupationSupport"/> 
    <Parameter name="p41" label="OccupationTransport"/> 
    <Parameter name="p42" label="Income"/> 
    <Parameter name="p43" label="GenderMale"/> 
    <Parameter name="p44" label="Deductions"/> 
    <Parameter name="p45" label="Hours"/> 
    <Parameter name="p46" label="IGNORE_AccountsChina"/> 
    <Parameter name="p47" label="IGNORE_AccountsColumbia"/> 
    <Parameter name="p48" label="IGNORE_AccountsCuba"/> 
    <Parameter name="p49" label="IGNORE_AccountsEcuador"/> 
    <Parameter name="p50" label="IGNORE_AccountsEngland"/> 
    <Parameter name="p51" label="IGNORE_AccountsFiji"/> 
    <Parameter name="p52" label="IGNORE_AccountsGermany"/> 
    <Parameter name="p53" label="IGNORE_AccountsGreece"/> 
    <Parameter name="p54" label="IGNORE_AccountsGuatemala"/> 
    <Parameter name="p55" label="IGNORE_AccountsHong"/> 
    <Parameter name="p56" label="IGNORE_AccountsHungary"/> 
    <Parameter name="p57" label="IGNORE_AccountsIndia"/> 
    <Parameter name="p58" label="IGNORE_AccountsIndonesia"/> 
    <Parameter name="p59" label="IGNORE_AccountsIran"/> 
    <Parameter name="p60" label="IGNORE_AccountsIreland"/> 
    <Parameter name="p61" label="IGNORE_AccountsItaly"/> 
    <Parameter name="p62" label="IGNORE_AccountsJamaica"/> 
    <Parameter name="p63" label="IGNORE_AccountsJapan"/> 
    <Parameter name="p64" label="IGNORE_AccountsMalaysia"/> 
    <Parameter name="p65" label="IGNORE_AccountsMexico"/> 
    <Parameter name="p66" label="IGNORE_AccountsNewZealand"/> 
    <Parameter name="p67" label="IGNORE_AccountsNicaragua"/> 
    <Parameter name="p68" label="IGNORE_AccountsPhilippines"/> 
    <Parameter name="p69" label="IGNORE_AccountsPoland"/> 
    <Parameter name="p70" label="IGNORE_AccountsPortugal"/> 
    <Parameter name="p71" label="IGNORE_AccountsScotland"/> 
    <Parameter name="p72" label="IGNORE_AccountsSingapore"/> 
    <Parameter name="p73" label="IGNORE_AccountsTaiwan"/> 
    <Parameter name="p74" label="IGNORE_AccountsUnitedStates"/> 
    <Parameter name="p75" label="IGNORE_AccountsVietnam"/> 
    <Parameter name="p76" label="IGNORE_AccountsYugoslavia"/> 
    <Parameter name="p77" label="RISK_Adjustment"/> 
    </ParameterList> 
    <FactorList> 
    <Predictor name="Employment"/> 
    <Predictor name="Education"/> 
    <Predictor name="Marital"/> 
    <Predictor name="Occupation"/> 
    <Predictor name="Gender"/> 
    <Predictor name="IGNORE_Accounts"/> 
    </FactorList> 
    <CovariateList> 
    <Predictor name="ID"/> 
    <Predictor name="Age"/> 
    <Predictor name="Income"/> 
    <Predictor name="Deductions"/> 
    <Predictor name="Hours"/> 
    <Predictor name="RISK_Adjustment"/> 
    </CovariateList> 
    <PPMatrix> 
    <PPCell value="1" predictorName="ID" parameterName="p1"/> 
    <PPCell value="1" predictorName="Age" parameterName="p2"/> 
    <PPCell value="Private" predictorName="Employment" parameterName="p3"/> 
    <PPCell value="PSFederal" predictorName="Employment" parameterName="p4"/> 
    <PPCell value="PSLocal" predictorName="Employment" parameterName="p5"/> 
    <PPCell value="PSState" predictorName="Employment" parameterName="p6"/> 
    <PPCell value="SelfEmp" predictorName="Employment" parameterName="p7"/> 
    <PPCell value="Volunteer" predictorName="Employment" parameterName="p8"/> 
    <PPCell value="Bachelor" predictorName="Education" parameterName="p9"/> 
    <PPCell value="College" predictorName="Education" parameterName="p10"/> 
    <PPCell value="Doctorate" predictorName="Education" parameterName="p11"/> 
    <PPCell value="HSgrad" predictorName="Education" parameterName="p12"/> 
    <PPCell value="Master" predictorName="Education" parameterName="p13"/> 
    <PPCell value="Preschool" predictorName="Education" parameterName="p14"/> 
    <PPCell value="Professional" predictorName="Education" parameterName="p15"/> 
    <PPCell value="Vocational" predictorName="Education" parameterName="p16"/> 
    <PPCell value="Yr10" predictorName="Education" parameterName="p17"/> 
    <PPCell value="Yr11" predictorName="Education" parameterName="p18"/> 
    <PPCell value="Yr12" predictorName="Education" parameterName="p19"/> 
    <PPCell value="Yr1t4" predictorName="Education" parameterName="p20"/> 
    <PPCell value="Yr5t6" predictorName="Education" parameterName="p21"/> 
    <PPCell value="Yr7t8" predictorName="Education" parameterName="p22"/> 
    <PPCell value="Yr9" predictorName="Education" parameterName="p23"/> 
    <PPCell value="Divorced" predictorName="Marital" parameterName="p24"/> 
    <PPCell value="Married" predictorName="Marital" parameterName="p25"/> 
    <PPCell value="Married-spouse-absent" predictorName="Marital" parameterName="p26"/> 
    <PPCell value="Unmarried" predictorName="Marital" parameterName="p27"/> 
    <PPCell value="Widowed" predictorName="Marital" parameterName="p28"/> 
    <PPCell value="Clerical" predictorName="Occupation" parameterName="p29"/> 
    <PPCell value="Executive" predictorName="Occupation" parameterName="p30"/> 
    <PPCell value="Farming" predictorName="Occupation" parameterName="p31"/> 
    <PPCell value="Home" predictorName="Occupation" parameterName="p32"/> 
    <PPCell value="Machinist" predictorName="Occupation" parameterName="p33"/> 
    <PPCell value="Military" predictorName="Occupation" parameterName="p34"/> 
    <PPCell value="Professional" predictorName="Occupation" parameterName="p35"/> 
    <PPCell value="Protective" predictorName="Occupation" parameterName="p36"/> 
    <PPCell value="Repair" predictorName="Occupation" parameterName="p37"/> 
    <PPCell value="Sales" predictorName="Occupation" parameterName="p38"/> 
    <PPCell value="Service" predictorName="Occupation" parameterName="p39"/> 
    <PPCell value="Support" predictorName="Occupation" parameterName="p40"/> 
    <PPCell value="Transport" predictorName="Occupation" parameterName="p41"/> 
    <PPCell value="1" predictorName="Income" parameterName="p42"/> 
    <PPCell value="Male" predictorName="Gender" parameterName="p43"/> 
    <PPCell value="1" predictorName="Deductions" parameterName="p44"/> 
    <PPCell value="1" predictorName="Hours" parameterName="p45"/> 
    <PPCell value="China" predictorName="IGNORE_Accounts" parameterName="p46"/> 
    <PPCell value="Columbia" predictorName="IGNORE_Accounts" parameterName="p47"/> 
    <PPCell value="Cuba" predictorName="IGNORE_Accounts" parameterName="p48"/> 
    <PPCell value="Ecuador" predictorName="IGNORE_Accounts" parameterName="p49"/> 
    <PPCell value="England" predictorName="IGNORE_Accounts" parameterName="p50"/> 
    <PPCell value="Fiji" predictorName="IGNORE_Accounts" parameterName="p51"/> 
    <PPCell value="Germany" predictorName="IGNORE_Accounts" parameterName="p52"/> 
    <PPCell value="Greece" predictorName="IGNORE_Accounts" parameterName="p53"/> 
    <PPCell value="Guatemala" predictorName="IGNORE_Accounts" parameterName="p54"/> 
    <PPCell value="Hong" predictorName="IGNORE_Accounts" parameterName="p55"/> 
    <PPCell value="Hungary" predictorName="IGNORE_Accounts" parameterName="p56"/> 
    <PPCell value="India" predictorName="IGNORE_Accounts" parameterName="p57"/> 
    <PPCell value="Indonesia" predictorName="IGNORE_Accounts" parameterName="p58"/> 
    <PPCell value="Iran" predictorName="IGNORE_Accounts" parameterName="p59"/> 
    <PPCell value="Ireland" predictorName="IGNORE_Accounts" parameterName="p60"/> 
    <PPCell value="Italy" predictorName="IGNORE_Accounts" parameterName="p61"/> 
    <PPCell value="Jamaica" predictorName="IGNORE_Accounts" parameterName="p62"/> 
    <PPCell value="Japan" predictorName="IGNORE_Accounts" parameterName="p63"/> 
    <PPCell value="Malaysia" predictorName="IGNORE_Accounts" parameterName="p64"/> 
    <PPCell value="Mexico" predictorName="IGNORE_Accounts" parameterName="p65"/> 
    <PPCell value="NewZealand" predictorName="IGNORE_Accounts" parameterName="p66"/> 
    <PPCell value="Nicaragua" predictorName="IGNORE_Accounts" parameterName="p67"/> 
    <PPCell value="Philippines" predictorName="IGNORE_Accounts" parameterName="p68"/> 
    <PPCell value="Poland" predictorName="IGNORE_Accounts" parameterName="p69"/> 
    <PPCell value="Portugal" predictorName="IGNORE_Accounts" parameterName="p70"/> 
    <PPCell value="Scotland" predictorName="IGNORE_Accounts" parameterName="p71"/> 
    <PPCell value="Singapore" predictorName="IGNORE_Accounts" parameterName="p72"/> 
    <PPCell value="Taiwan" predictorName="IGNORE_Accounts" parameterName="p73"/> 
    <PPCell value="UnitedStates" predictorName="IGNORE_Accounts" parameterName="p74"/> 
    <PPCell value="Vietnam" predictorName="IGNORE_Accounts" parameterName="p75"/> 
    <PPCell value="Yugoslavia" predictorName="IGNORE_Accounts" parameterName="p76"/> 
    <PPCell value="1" predictorName="RISK_Adjustment" parameterName="p77"/> 
    </PPMatrix> 
    <ParamMatrix> 
    <PCell parameterName="p0" df="1" beta="-12.0199804097759"/> 
    <PCell parameterName="p1" df="1" beta="3.62329433275629e-08"/> 
    <PCell parameterName="p2" df="1" beta="0.0380676635766761"/> 
    <PCell parameterName="p3" df="1" beta="0.756901134378277"/> 
    <PCell parameterName="p4" df="1" beta="0.375762595900717"/> 
    <PCell parameterName="p5" df="1" beta="0.50309824514625"/> 
    <PCell parameterName="p6" df="1" beta="0.470897191210805"/> 
    <PCell parameterName="p7" df="1" beta="-2.10284542055317"/> 
    <PCell parameterName="p8" df="1" beta="-15.5455611068614"/> 
    <PCell parameterName="p9" df="1" beta="0.0997435072074993"/> 
    <PCell parameterName="p10" df="1" beta="-1.22905386951777"/> 
    <PCell parameterName="p11" df="1" beta="-6.76667195830752"/> 
    <PCell parameterName="p12" df="1" beta="-1.01297363710822"/> 
    <PCell parameterName="p13" df="1" beta="-0.340407862763258"/> 
    <PCell parameterName="p14" df="1" beta="-15.8841924243017"/> 
    <PCell parameterName="p15" df="1" beta="3.18173392385448"/> 
    <PCell parameterName="p16" df="1" beta="-0.569821531302005"/> 
    <PCell parameterName="p17" df="1" beta="-3.3033217141108"/> 
    <PCell parameterName="p18" df="1" beta="-0.430994461878221"/> 
    <PCell parameterName="p19" df="1" beta="-17.0972305473487"/> 
    <PCell parameterName="p20" df="1" beta="-15.929168040244"/> 
    <PCell parameterName="p21" df="1" beta="-17.7483980280451"/> 
    <PCell parameterName="p22" df="1" beta="-16.1514804898207"/> 
    <PCell parameterName="p23" df="1" beta="-10.3889654044557"/> 
    <PCell parameterName="p24" df="1" beta="-0.690592385956069"/> 
    <PCell parameterName="p25" df="1" beta="2.53630505787246"/> 
    <PCell parameterName="p26" df="1" beta="1.41541804527502"/> 
    <PCell parameterName="p27" df="1" beta="1.49491086815453"/> 
    <PCell parameterName="p28" df="1" beta="0.174099244312997"/> 
    <PCell parameterName="p29" df="1" beta="1.01865424623088"/> 
    <PCell parameterName="p30" df="1" beta="1.73213477081248"/> 
    <PCell parameterName="p31" df="1" beta="-1.80877402327631"/> 
    <PCell parameterName="p32" df="1" beta="-12.4454410582178"/> 
    <PCell parameterName="p33" df="1" beta="-0.417346874910574"/> 
    <PCell parameterName="p34" df="1" beta="-12.475145396564"/> 
    <PCell parameterName="p35" df="1" beta="1.45214141089004"/> 
    <PCell parameterName="p36" df="1" beta="1.6405"/> 
    <PCell parameterName="p37" df="1" beta="0.134775653612853"/> 
    <PCell parameterName="p38" df="1" beta="0.948585540443075"/> 
    <PCell parameterName="p39" df="1" beta="0.144171863863442"/> 
    <PCell parameterName="p40" df="1" beta="0.789971116324262"/> 
    <PCell parameterName="p41" df="1" beta="0.842781801750256"/> 
    <PCell parameterName="p42" df="1" beta="-9.63129083571953e-07"/> 
    <PCell parameterName="p43" df="1" beta="-0.52313575926474"/> 
    <PCell parameterName="p44" df="1" beta="0.00125611277933667"/> 
    <PCell parameterName="p45" df="1" beta="0.0109489183058056"/> 
    <PCell parameterName="p46" df="1" beta="-2.86790934232277"/> 
    <PCell parameterName="p47" df="1" beta="-10.4586048958891"/> 
    <PCell parameterName="p48" df="1" beta="-11.8078344468555"/> 
    <PCell parameterName="p49" df="1" beta="-8.15369086351991"/> 
    <PCell parameterName="p50" df="1" beta="-15.1509749621394"/> 
    <PCell parameterName="p51" df="1" beta="-12.6588234930477"/> 
    <PCell parameterName="p52" df="1" beta="7.44342418994783"/> 
    <PCell parameterName="p53" df="1" beta="-8.80415604321149"/> 
    <PCell parameterName="p54" df="1" beta="-0.909551298634999"/> 
    <PCell parameterName="p55" df="1" beta="3.21333791872318"/> 
    <PCell parameterName="p56" df="1" beta="-9.7080063371067"/> 
    <PCell parameterName="p57" df="1" beta="-9.94640566996892"/> 
    <PCell parameterName="p58" df="1" beta="-7.34469543656762"/> 
    <PCell parameterName="p59" df="1" beta="-10.1375079207868"/> 
    <PCell parameterName="p60" df="1" beta="4.03786237290128"/> 
    <PCell parameterName="p61" df="1" beta="-9.95289672035589"/> 
    <PCell parameterName="p62" df="1" beta="-11.2800534550324"/> 
    <PCell parameterName="p63" df="1" beta="-8.5259456003378"/> 
    <PCell parameterName="p64" df="1" beta="-11.1183864482514"/> 
    <PCell parameterName="p65" df="1" beta="-3.17790587178398"/> 
    <PCell parameterName="p66" df="1" beta="7.62183148791729"/> 
    <PCell parameterName="p67" df="1" beta="-9.29840834254978"/> 
    <PCell parameterName="p68" df="1" beta="5.87739404847556"/> 
    <PCell parameterName="p69" df="1" beta="-11.0988711939497"/> 
    <PCell parameterName="p70" df="1" beta="-5.78171399043641"/> 
    <PCell parameterName="p71" df="1" beta="-11.009822161619"/> 
    <PCell parameterName="p72" df="1" beta="-7.98831399897464"/> 
    <PCell parameterName="p73" df="1" beta="-14.2857685874083"/> 
    <PCell parameterName="p74" df="1" beta="4.89065048867447"/> 
    <PCell parameterName="p75" df="1" beta="-2.21686920486685"/> 
    <PCell parameterName="p76" df="1" beta="-10.0494769160447"/> 
    <PCell parameterName="p77" df="1" beta="0.0044395180546043"/> 
    </ParamMatrix> 
</GeneralRegressionModel> 
</PMML>

通過R模型所保持的係數是如下：

Coefficients: 
           Estimate Std. Error z value Pr(>|z|) 
(Intercept)     -5.779e+00 1.108e+04 -0.001 0.999584 
ID       3.922e-08 6.187e-08 0.634 0.526164 
Age       2.705e-02 1.388e-02 1.949 0.051314 . 
EmploymentPrivate    1.087e+00 6.774e-01 1.605 0.108550 
EmploymentPSFederal   1.155e+00 1.050e+00 1.101 0.271105 
EmploymentPSLocal    1.262e+00 8.811e-01 1.432 0.152036 
EmploymentPSState    8.151e-01 1.011e+00 0.806 0.420221 
EmploymentSelfEmp    2.217e-01 9.859e-01 0.225 0.822066 
EmploymentVolunteer   -1.667e+01 1.075e+04 -0.002 0.998764 
EducationBachelor    4.297e-01 7.768e-01 0.553 0.580154 
EducationCollege    -1.234e+00 8.393e-01 -1.470 0.141592 
EducationDoctorate   1.604e+00 1.697e+00 0.945 0.344690 
EducationHSgrad    -5.332e-01 7.613e-01 -0.700 0.483661 
EducationMaster    -3.705e-01 1.117e+00 -0.332 0.740081 
EducationPreschool   -1.306e+01 3.588e+03 -0.004 0.997096 
EducationProfessional   1.600e+00 1.251e+00 1.279 0.200733 
EducationVocational   -3.887e-01 1.023e+00 -0.380 0.703998 
EducationYr10    -2.121e+00 1.897e+00 -1.118 0.263626 
EducationYr11    -3.222e-01 1.294e+00 -0.249 0.803322 
EducationYr12    -4.786e+00 1.235e+01 -0.388 0.698298 
EducationYr1t4    -1.588e+01 4.174e+03 -0.004 0.996965 
EducationYr5t6    -1.779e+01 2.356e+03 -0.008 0.993976 
EducationYr7t8    -1.659e+01 1.951e+03 -0.009 0.993214 
EducationYr9     -1.672e+01 2.680e+03 -0.006 0.995022 
MaritalDivorced    -6.700e-01 8.277e-01 -0.809 0.418238 
MaritalMarried    2.269e+00 5.238e-01 4.332 1.48e-05 *** 
MaritalMarried-spouse-absent 1.299e+00 1.385e+00 0.938 0.348362 
MaritalUnmarried    1.570e+00 9.025e-01 1.740 0.081926 . 
MaritalWidowed    7.018e-01 1.209e+00 0.581 0.561438 
OccupationClerical   1.060e+00 1.224e+00 0.866 0.386731 
OccupationExecutive   1.851e+00 1.138e+00 1.627 0.103649 
OccupationFarming    1.189e-01 1.530e+00 0.078 0.938065 
OccupationHome    -1.296e+01 6.601e+03 -0.002 0.998434 
OccupationMachinist   2.869e-01 1.299e+00 0.221 0.825190 
OccupationMilitary   -1.318e+01 1.075e+04 -0.001 0.999022 
OccupationProfessional  1.589e+00 1.187e+00 1.339 0.180656 
OccupationProtective   1.099e+00 1.622e+00 0.678 0.497935 
OccupationRepair    1.641e-01 1.204e+00 0.136 0.891597 
OccupationSales    7.170e-01 1.205e+00 0.595 0.551929 
OccupationService   -5.600e-02 1.348e+00 -0.042 0.966858 
OccupationSupport    8.431e-01 1.348e+00 0.626 0.531515 
OccupationTransport   3.488e-01 1.242e+00 0.281 0.778911 
Income      1.442e-06 3.112e-06 0.463 0.643050 
GenderMale     1.510e-01 5.361e-01 0.282 0.778254 
Deductions     1.476e-03 4.109e-04 3.593 0.000327 *** 
Hours       2.116e-02 1.433e-02 1.476 0.139922 
IGNORE_AccountsChina   -2.048e+01 1.867e+04 -0.001 0.999125 
IGNORE_AccountsColumbia  -2.085e+01 1.294e+04 -0.002 0.998715 
IGNORE_AccountsCuba   -1.942e+01 1.544e+04 -0.001 0.998997 
IGNORE_AccountsEcuador  -1.701e+01 1.544e+04 -0.001 0.999121 
IGNORE_AccountsEngland  -1.418e+01 1.109e+04 -0.001 0.998980 
IGNORE_AccountsGermany  -4.952e-02 1.108e+04 0.000 0.999996 
IGNORE_AccountsGreece  -1.645e+01 1.544e+04 -0.001 0.999150 
IGNORE_AccountsGuatemala  -2.767e+00 1.459e+04 0.000 0.999849 
IGNORE_AccountsHong   -3.325e+00 1.557e+04 0.000 0.999830 
IGNORE_AccountsIndia   -1.506e+01 1.110e+04 -0.001 0.998918 
IGNORE_AccountsIndonesia  -1.692e+01 1.225e+04 -0.001 0.998897 
IGNORE_AccountsIreland  -3.329e+00 1.108e+04 0.000 0.999760 
IGNORE_AccountsItaly   -1.663e+01 1.304e+04 -0.001 0.998982 
IGNORE_AccountsJamaica  -2.174e+01 2.163e+04 -0.001 0.999198 
IGNORE_AccountsJapan   -1.577e+01 1.544e+04 -0.001 0.999185 
IGNORE_AccountsMalaysia  -1.903e+01 1.206e+04 -0.002 0.998741 
IGNORE_AccountsMexico  -9.440e+00 1.108e+04 -0.001 0.999320 
IGNORE_AccountsNewZealand  1.773e-01 1.562e+04 0.000 0.999991 
IGNORE_AccountsNicaragua  -1.786e+01 1.200e+04 -0.001 0.998812 
IGNORE_AccountsPhilippines -9.526e-01 1.108e+04 0.000 0.999931 
IGNORE_AccountsPoland  -1.878e+01 1.544e+04 -0.001 0.999030 
IGNORE_AccountsPortugal  -1.432e+00 1.557e+04 0.000 0.999927 
IGNORE_AccountsSingapore  -1.778e+01 1.225e+04 -0.001 0.998842 
IGNORE_AccountsTaiwan  -1.922e+01 1.259e+04 -0.002 0.998782 
IGNORE_AccountsUnitedStates -2.519e+00 1.108e+04 0.000 0.999819 
IGNORE_AccountsVietnam  -1.984e+01 1.250e+04 -0.002 0.998734 
IGNORE_AccountsYugoslavia -1.774e+01 1.544e+04 -0.001 0.999083 
RISK_Adjustment    3.802e-03 6.819e-04 5.575 2.47e-08 ***

（生成此GLM模型和相應的PMML R中的腳本如下：

library(pmml) 
auditDF <- read.csv("http://rattle.togaware.com/audit.csv") 
auditDF <- na.omit(auditDF) 
target <- auditDF$TARGET_Adjusted 
N <- length(target); M <- N - 500 
i.train <- sample(N, M) 
audit.train <- auditDF[i.train,] 
audit.test <- auditDF[-i.train,] 
glm.model <- glm(audit.train$TARGET_Adjusted ~ ., data = audit.train, family = "binomial") 
glm.pmml <- pmml(glm.model, name = "glm model", data = trainDF) 
xmlFile <- file.path(getwd(), "audit-glm.xml") 
saveXML(glm.pmml, xmlFile)

來源：http://blog.revolutionanalytics.com/2011/03/predicting-r-models-with-pmml.html）

來源

2014-06-23 hlin117

我想這取決於你想要對模型做什麼，一旦你把它放回到R.一次我幫助某人創建了一個僞gml對象，知道變量的係數，並可以用於predict() 。許多其他功能需要填充數據集存在。

如果這可能對您感興趣。該功能被稱爲makeglm.R。您只需將該功能複製並粘貼到您的R會話中。但是有必要先轉換你的數據。這裏有一些幫助功能可以做到這一點。

getdata <- function(xml, ns=attr(xml,"ns")) { 
    names<-xpathSApply(xml, "//d:DataField/@name", namespaces = ns) 
    vals<-xpathApply(xml, "//d:DataField", function(x) { 
     if(xmlGetAttr(x, "optype")=="categorical") { 
      levels<-xpathSApply(x, "Value/@value") 
      factor(character(0), levels=levels) 
     } else if (xmlGetAttr(x, "optype")=="continuous"){ 
      numeric(0) 
     } 
    }, namespaces = ns) 
    names(vals)<-names 
    as.data.frame(vals) 
} 

getformula <- function(xml, ns=attr(xml,"ns")) { 
    resp<-xpathSApply(xml, "//d:MiningField[@usageType=\"predicted\"]/@name", 
     namespaces = ns) 
    covar<-xpathSApply(xml, "//d:MiningField[@usageType=\"active\"]/@name", 
     namespaces = ns) 
    fmc<-paste(paste(resp, collapse=" + "), "~", paste(covar, collapse=" + ")) 
    as.formula(fmc) 
} 

getestimates <- function(xml, ns=attr(xml,"ns")) { 
    betas <- setNames(as.numeric(xpathSApply(xml, "//d:PCell/@beta", namespaces = ns)), 
     xpathSApply(xml, "//d:PCell/@parameterName", namespaces = ns)) 
    numericparam <- unname(xpathSApply(xml, "//d:CovariateList/d:Predictor/@name", namespaces = ns)) 
    factorparam <- unname(xpathSApply(xml, "//d:FactorList/d:Predictor/@name", namespaces = ns)) 
    values <- do.call(rbind, Map(function(x,y,z) data.frame(p=x, val=y, pred=z, stringsAsFactors=F), 
     unname(xpathSApply(xml,"//d:PPCell/@parameterName", namespaces = ns)), 
     xpathSApply(xml, "//d:PPCell/@value", namespaces = ns), 
     xpathSApply(xml, "//d:PPCell/@predictorName", namespaces = ns))) 
    lf<-Map(function(x) { 
     vv <- values[values$pred==x, ] 
     setNames(betas[vv$p], vv$val) 
    }, factorparam) 
    ln<-Map(function(x) { 
     vv <- values[values$pred==x, ] 
     unname(betas[vv$p]) 
    }, numericparam) 
    estimates<-c(lf,ln) 
    intercept<-getNodeSet(xml,"//d:Parameter[@label=\"(Intercept)\"]", namespaces = ns) 
    if(length(intercept)) { 
     estimates<-c(unname(betas[xmlGetAttr(intercept[[1]],"name")]), estimates) 
    } 
    estimates 
}

我完全不熟悉PMML格式，但是我根據您的示例文檔將它們放在一起。我嘗試從數據中提取所有建立公式，data.frame存根和參數估計所需的所有正確信息，以便使用makeglm()函數。一旦你加載了這個功能和這些幫助功能，你可以運行

library(XML) 
mypmml <- xmlParse("pmml.xml") 
attr(mypmml, "ns")<-"d" 

dd <- getdata(mypmml) 
ff <- getformula(mypmml) 
ee <- getestimates(mypmml) 
do.call(makeglm, c(list(ff, family="binomial", data=dd), ee))

實際運行該功能。這將返回一個glm對象，您可以使用predict()。我確實必須在您的示例數據中更改一件事。出於某種原因，你的表名作爲GLM模型

glm(audit.train$TARGET_Adjusted ~ ., data = audit.train, ...)

公式的一部分，而不是

glm(TARGET_Adjusted ~ ., data = audit.train, ...)

這可能會導致問題。所以在我讀入之前，我只是從xml文件中取出了「audit.train $」。也許可以進行更多的錯誤檢查，但我甚至不確定這是否最終是你以後的樣子。

來源

2014-06-24 05:41:04 MrFlick

謝謝！我會讓你知道它是否有效。 – hlin117

好的，我們可能會運行不同版本的R;上面的腳本不適合我。我運行R 3.1.0，我的XML包是3.98-1.1。我運行： >庫（XML） > mypmml < - XMLPARSE（「審計glm.xml」） > DD < - 的GetData（mypmml）但是，當我打印DD，它說：數據幀與0列和0行我假設這不是你的輸出。當我運行getformula（）時，我實際上在函數中得到了一個分析錯誤。我多次檢查，以確保該腳本是複製粘貼正確。 – hlin117

（道歉的錯誤消息之前不增加。當我嘗試後我以前的評論，我打代替 + ，我也爲我的上述評論的格式表示歉意。我不知道多內襯意見是不允許的。但要點是，的GetData（）和getformula（）似乎都沒有被正確解析我的XML。） – hlin117

將表示多項Logistic迴歸的PMML轉換回R係數

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