将表示多项Logistic回归的PMML转换回R系数(Converting PMML representing Multinomial Logistic Regression back into R

将表示多项Logistic回归的PMML转换回R系数(Converting PMML representing Multinomial Logistic Regression back into R coefficients)

我最近的任务是将PMML解析为R模型。 （我已经进行了广泛的搜索，并且没有库可以为您进行此转换。）我正在尝试将包含多项Logistic回归的PMML转换回R模型，但我不知道如何转换任何一个PMML文档中保存的系数与R模型保持的系数。

PMML如下：

<?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="1.64050123149924"/> <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>

R模型保持的系数如下：

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模型的R中的脚本和相应的PMML如下所示：

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)

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

My most recent assignment has been to parse PMML back into R models. (I've searched extensively, and there is no library that does this conversion for you.) I'm trying to convert PMML containing a Multinomial Logistic Regression back into an R model, but I don't know how to convert any of the coefficients held in the PMML document to the coefficients held by the R model.

The PMML is below:

<?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="1.64050123149924"/> <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>

The coefficients held by the R model are below:

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 ***

(The script in R that generated this GLM model and the corresponding PMML is below:

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)

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

最满意答案

我认为这取决于你将模型重新放回到R后你想要做什么。有一次，我帮助某人创建了一个伪gml对象，它知道变量的系数，可以和predict()一起使用。 许多其他功能需要填充数据集。

如果那可能对您感兴趣。 该函数名为makeglm.R 。 您只想将该功能复制并粘贴到R会话中。 但是有必要首先转换您的数据。 这里有一些辅助函数可以做到这一点。

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 }

我对PMML格式并不熟悉，但我根据您的示例文档将它们放在一起。 我尝试从数据中提取构建公式，data.frame存根和参数估计所需的所有正确信息，以便使用makeglm()函数。 一旦你加载了这个函数和这些辅助函数，你就可以运行了

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))

实际运行该功能。 这将返回一个glm对象，您可以将其与predict() 。 我确实需要在示例数据中更改一件事。 出于某种原因，您将表名作为glm模型中公式的一部分

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

而不是

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

这可能会导致问题。 所以我在读取之前从xml文件中取出了“audit.train $”。它可能会更多的错误检查，但我甚至不确定这是否最终是你所追求的。

I suppose that depends on exactly what you want to do with the model once you have it back into R. At one point i helped someone create a pseudo-gml object that knew the coefficients for the variables and could be used with predict(). Many other functions required the fill dataset to be present.

If that may be of possible interested to you. The function is called makeglm.R. You will want to copy and paste just that function into your R session. But it will be necessary to transform your data first. Here are some helper functions to do just that.

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 }

I'm not at all familiar with the PMML format but i put these together based on your sample document. I tried extracting all the correct information needed to build a formula, data.frame stub, and parameter estimates from the data in order to use the makeglm() function. Once you've loaded that function and these helper functions, you can run

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))

to actually run the function. This will return a glm object that you can use with predict(). I did have to change one thing in your sample data. For some reason you had the table name as part of the formula in the glm model

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

rather than

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

which can cause problems. So I just took out the "audit.train$" from the xml file before I read it in. It maybe possible to more error checking, but I wasn't even sure if this is ultimately what you were after.